■ PERSONAL DETAILS

Name	Zhongsheng Hou
Mailing Address	Ningxia Road   308, Research Building A, Floor 9, Qingdao University, Qingdao 266071, China
Nationality	P. R. China
Email	zhshhou@bjtu.eu.cn; zshou@qdu.edu.cn

■ EDUCATION

Doctor of Eng.	Dept. of   Automatic Control, Northeastern University, China. 1992-1994.
Master of Sci.	Dept. of Applied   Mathematics, Jilin University of Technology, China. 1985-1988.
Bachelor of Eng.	Dept. of Applied   Mathematics, Jilin University of Technology, China. 1979-1983.

■ POSITIONS

Chair Professor Founding Director/ Distinguished Prof.	Qingdao University. Since Dec. 2018 Advanced Control Systems Lab, School of Electronic and Information Engineering,   Beijing Jiaotong University. From Sept. 2005 to Dec. 2018
Prof./Dept. Head	Dept of Automatic Control, School   of Electronic and Information Engineering, Beijing Jiaotong University. From Sept. 1997 to   Dec. 2018
Visiting Scholar	Center for   Systems Science, Yale University, USA.   From Sept. 2002-Sept. 2003
Postdoctoral Associate Prof.	School of   Astronautics, Harbin Institute of Technology, China. From 1995 to 1997. Dept of Electrical Engineering, Shenyang   University of Technology. From 1988-1992
Assitant Prof.	Dept of Applied Mathematics, Shenyang University of   Technology. From 1983-1985

■ MEMBERSHIP AND COMMITTEE POSITION

          IEEE Fellow since 2020. Institute of Electrical and Electronics Engineers (IEEE);

          CAA Fellow since 2019. Chinese Assciation of Automation (CAA);

         AAIA Fellow since 2021. Asia-Pacific Artificial Intelligence Association (AAIA);

Founding Director, Technical Committee of “Data Driven Control, Learning and Optimization (DDCLO),” CAA, since June 13, 2015;

Founding General Chair, IEEE Conference on Data Driven Control and Learning Systems (DDCLS), since 2016;

Committee Member, IFAC Technical Committee "Adaptive and Learning Systems" (TC1.2.). 2014-2017

Committee Member, IFAC Technical Committee "(Transportation Systems)" (TC7.4). 2014-Present

■ RESEARCH INTERESTS

              Model Free Adaptive Control

              Data-Driven Control

              Data-Driven Iterative Learning Control

              Intelligent Transportation Systems (Road/Freeway/Train Traffic Controls)

■ INVITED AND PLENARY TALKS

[1] Invited to visit Computer and Engineering Department of Beijing Institute of Technology, with talk titled “Model-free adaptive control theory and latest development”. March 20, 2005

[2] Invited to visit Department of Automatic Control of Harbin Institute of Technology with talk titled “Brief introduction to model-free adaptive control”. June 27, 2005

[3] Invited to visit Cardiff University and Sheffield University, U.K, with talk titled “Brief introduction to model-free adaptive control”. July 18, 2006

[4] Invited to attend the forum “Data-based Control, Decision, Scheduling, and Fault Diagnostics”, with talk titled “On the data driven control”, The National Natural Science Foundation of China (NSFC). Nov. 5-7, 2008

[5] Invited to visit the University of Shihezi, Xinjiang, China, with talk titled “The state-of-the-art on the traffic information engineering and control”. June 30, 2009

[6] Plenary talk at “The National Academic Conference for Ph.D Students Majoring of Traffic and Transportation” with talks titled “Iterative Learning Control and Traffic Control”. July 20, 2009

[7] Invited talk “Data driven control theory and applications-Taking model free adaptive as an example”, Beijing University of Science and Technology. Oct. 30, 2009

[8] Plenary talk at the 1^st Academic Forum for Young Scientist, Beijing Automation Association, with title “What is data driven control”. Nov 7, 2009

[9] Plenary talk at the Forum “On Intelligent System and Intelligent Automation”, Beijing Automation Association. Talk title is “Data Driven Control and Intelligence”. Beijing University of Technology. Nov 3, 2009

[10] Plenary talk on the Forum of The Chinese Automation Congress, with talk titled “Data driven MFAC for the complex systems and its modularized control system design”. Nov. 1-3, 2009, Hangzhou

[11] Invited talk “What is the data driven control”. Yanshan University. Aug. 10, 2010

[12] Invited talk “Data driven control and the unified framework-Taking MFAC as example”. Southeastern University. Dec. 5-7, 2010

[13] Invited talk “Data driven control-Taking MFAC as example”. Nanjing University of Aeronautics and Astronautics. Dec. 5-7, 2010

[14] Invited to attend the International Workshop on Data Based Optimization, Control and Modelling”, with talk titled “From Model Based Control to Data Driven Control”, The National Natural Science Foundation of China (NSFC). Nov. 5-7, 2011

[15] Invited talk “Model free adaptive control and applications in process control”. Beijing University of Chemical Technology. June 15, 2011

[16] Plenary talk at “Forum on Advanced Traffic Management and Control” of the 2011 Chinese Intelligent Transportation Systems (ITS2011), with talk titled “Data Driven Control and Traffic System Control”. Tianjin. July 21-22, 2011

[17] Plenary talk at “Forum on Data Based Modeling, Control and Optimization” of The Chinese Automation Congress, with talk titled “The Research Method and Unified Framework for Data Driven Control”. Beijing. Nov. 1-3, 2011

[18] Invited to visit Department of Electrical Engineering and Computer Science of National University of Singapore, with talk tilted “From Model Based Control to Data Driven Control_ Taking Model Free Adaptive Control as a Example”. Singapore. Aug. 1, 2011

[19] Invited to visit Singapore Polytechnic, with talk tilted “Introduction to Iterative learning control”. Singapore. July 24-Aug. 2, 2011

[20] Invited talk “The Research Method and Unified Framework for Data Driven Control”. Nanjing University of Technology. Oct. 13-14, 2011

[21] Invited talk tilted “On the Data Driven Control”. Henan Polytechnic University. Oct. 26-28, 2012

[22] Plenary talk “Data Driven Control and Iterative Learning Control”. The International Workshop on Iterative Learning Control. Xi’an Jiaotong University. April 26-28, 2012

[23] Invited talk tilted “The Data Driven Control, Prediction and Evaluation for the Grid Complex Traffic System ”. North China University of Technology. Nov. 28, 2012

[24] Invited talk “Data Driven Control_Taking the MFAC as example”. IIT MADRAS, India. Jan 17-20, 2013

[25] Invited talk tilted “Data Driven Control and Optimization”. Shandong University. April 16-18, 2013

[26] Invited talk tilted “The Research Method and Unified Framework for Data Driven Control”. Zhejiang University. June 13, 2013

[27] Invited talk tilted “The Research Method and Unified Framework for Data Driven Control”. Zhejiang University of Technology. June 14, 2013

[28] Invited talk tilted “The Research Method and Unified Framework for Data Driven Control”. Tianjin University. June 18, 2013

[29] Invited talk tilted “On the Data Driven Control-Taking MFAC as example”. The University of Bedfordshire, UK. Aug. 20, 2013

[30] Invited to attend the “Shuangqing Forum: Big Data Technique and Challenge” of The National Natural Science Foundation of China (NSFC), with talk titled ”Data Driven Control and Optimization”. March. 5-7, 2013

[31] Invited talk tilted “Dynamic Linearization and Data Driven Control”. Bohai University. China. April 1, 2014

[32] Invited talk tilted “Data Driven Control and My Research”. Liaoning University of Technology. China. April 2, 2014

[33] Invited talk tilted “Dynamic Linearization Method—Fundamental Approach to Data Driven Control”. Jiangnan University, China. May 22, 2014

[34] Invited talk tilted “Dynamic Linearization Method—Fundamental Approach to Data Driven Control”. Northeastern University, China. July 1, 2014

[35] Invited talk tilted “Dynamic Linearization Method—Fundamental Approach to Data Driven Control”. Beihang University, Beijing, China. Sept. 10, 2014

[36] Plenary talk at “The 2014 Symposium on Intelligent Automation of China”, with talk titled “Data Driven Control and its Possible Unified Framework”. Nanning, China. Jan 10-1, 2014

[37] Plenary talk at “The 3rd International Workshop on Learning Control”, with talk titled “On Model Free Iterative Learning Control”. Qiangdao, China. April 25-27, 2014

[38] Panelist of the “Panel Discussion on data driven control” at the The 11th World Congress on Intelligent Control and Automation, Shenyang, China. June 29-July 2, 2014

[39] Semi-plenary talk at “Taishan Academic Forum-On Automatic Control”, with talk titled “Dynamic Linearization Method—Fundamental Approach to Data Driven Control Theory”. China. Oct 18-20, 2014

[40] Semi-plenary talk at “The 2014 Symposium on Big Data”, with talk titled ”How to Use Data to Design Controller-Taking Model-Free Adaptive Control Method as Example”. By The NSFC and The Chinese University of Hong Kong, Hong Kong, China. Sept 22-25, 2014

[41] Invited talk titled “On model free adaptive control”. Xidian University. April 6-7, 2015

[42] Invited talk titled “Control is dead?—On Data Driven Control”. East China University of Science and Technology. June 24, 2015

[43] Invited talk titled “On data driven control and the control methods under the age of big data”, Bohai University. Dec. 11, 2015

[44] Invited talk titled “On data driven control for transportation system”, Beijing Information Science and Technology University. Dec. 9, 2015

[45] Plenary talk with title “Dynamic linearization method-The basis for data driven control system design” at the “Frontier Forum on Control Theory and Application for Youth Scientists,” Liaocheng University. Jan.7-8,2015

[46] Plenary talk with title “Data Driven Control_The Foundmental Control Method of Knowledge Work Automation for Process Industry”, at the “Forum on Knowledge Work Automation, NSFC”. Central South University. Jan. 15-16, 2015

[47] Plenary talk with title “Data driven control for traffic systems”, at “China-EU Green Transportation Forum of Technical Committee on Intelligent Transport System, CAA,”. Beijing. July 4, 2015

[48] Plenary talk with title “On Data Driven Control” at “The 2rd Workshop on the Development in Information Technology and Control,” University of Shanghai for Science and Technology. July 26, 2015

[49] Plenary talk with title “Data Driven Control and the Control Theory under Big Dada Age”, at “The Forum on Big Data and Automation, CAA.” Wuhan. Nov. 27-29, 2015

[50] Invited talk titled “Controller-Dynamic-Linearization based MFAC and Modularized controller design”. North China Electric Power University. July 14, 2016

[51] Invited talk tilted “From model based control to data driven control”. College of Engineering, Georgia Institute of Technology, USA. Aug. 16, 2016

[52] Invited talk tilted “From model based control to data driven control”. College of Engineering, Cleveland State University. Cleveland, USA. Aug. 22, 2016.

[53] Invited talk titled “From model based control to data driven control”, Beijing Technology and Business University. Sept. 21, 2016

[54] Invited talk titled “Iterative learning control and traffic control”, Chongqing Jiaotong University, China. Sept. 22, 2016

[55] Invited talk titled “How to design the data driven control systems”, Shenyang University, China. Oct.27, 2016

[56] Invited talk titled “How to design the data driven control systems”, Shandong University of Science and Technology. Nov.11, 2016

[57] Invited talk titled “How to design the data driven controller”, Huazhong University of Science and Technology. Dec.9, 2016

[58] Invited talk titled “How to design the data driven controller”, Yanshan University. Dec.15, 2016

[59] Invited talk titled “The new transportation system research methods”, Southwest Jiaotong University. Dec.28, 2016

[60] Semi-plenary talk at the 28th Chinese Control and Decision Conference (CCDC’2016), with title “From Model Based Control to Data Driven Control”, Yinchuan, China. May 29-30, 2016

[61] Plenary talk at “the International Summit on Industrial Process Big Data & System and Control Engineering” with title “How to design a data driven controller”, Qingdao. Oct.29-Nov.1, 2016

[62] Invited talk titled “How to design the data driven control systems”, Zhejiang University of Technology. April 20, 2017

[63] Invited talk titled “How to design the data driven control systems”, Jiangnan University. May 18, 2017

[64] Invited talk titled “How to design the data driven control systems”, Southeast University. May 17, 2017

[65] Invited talk titled “Is PID Irreplaceable-On the MFAC and Progress”, South China University of Technology. Dec. 11, 2017

[66] Invited talk titled “On the MFAC for complicated Systems”, Guangdong University of Technology. Dec.11, 2017

[67] Invited talk titled “Data-Driven Learning Control for Traffic Systems” at “the Forum of on

Future Traffic Control Theory and Systems”. Tongji University. May 30-31, 2017

[68] Plenary talk at the “The International Symposium on Advanced Control Theory and Cyber-Physical System Security for Uncertainty Systems” with title “How to design the data driven control systems”, Sichuan. June 27-28, 2017.

[69] Semi-plenary talk at the “Cixi’s Forum on National Intelligent Manufacture, CAA” with title “MFAC: An alternative control method of PID”. Cixi, Zhejiang. Sept. 23-24, 2017

[70] Semi-plenary talk at “the Forum on Big Data and Knoweledge Works Automation, NSFC,” with title “ Superficial view on the basic control theory for Big Data and Knoweledge Works Automation” . Oct. 21-22, 2017

[71] Plenary talk at “2017 International Symposium on Information Oriented Control (ISIOC2017),” with title “MFAC and Progress”, Hangzhou Dianzi University. Oct. 18-19, 2017

[72] Plenary talk at “3^rd Frontier Hot Topics on Automation_The 2017 Seminar on Adaptive dynamic programming and reinforcement learning, with title “Is PID Irreplaceable-On the MFAC and Progress”. Dec. 10, 2017

[73] Plenary talk at “The Symposium on Control Science and Engineering”, with title “Is PID Irreplaceable-On the MFAC and Progress”, Shaanxi Normal University, Xi’an. Jan. 10-11, 2018

[74] Plenary talk with title “Data Driven Control and ADRC” at “IEEE 12^th Workshop on ADRC: In Memory of Prof. Jingqing Han on the 10th Anniversary of His Passing,” Beijing. July 10-21, 2018

[75] Plenary talk at “the National Postdoc Forum on Intelligent Perception, Control and Optimization”, with title “PID and its Puzzles-MFAC and Progress”, Shenyang. Sept. 7-9, 2018

[76] Plenary talk at “The International Symposium on Iterative Learning Control” with talk title “Data Driven Adaptive Iterative Learning Control”, Qingdao. May 22-24, 2018

[77] Plenary talk at “The 4th Workshop of TCCT Discontinuous Control Group” with title “PID and its Puzzles-MFAC and Progress”, Dalian Maritime University. Nov.17-19, 2018

[78] Semi-plenary talk at “Forum on Big Data Based System Control and Decision of CAC2018, CAA” with title “PID and its Puzzles-MFAC and Progress”, Xi’an. Nov.29-Dec.2, 2018

[79] Plenary talk at “The Yanzhao Forum on Artificial Intelligent Theory, Technology and Applications” with talk title “Simple Learning Control Method_ Data Driven Adaptive Learning Control”. Yanshan University. Dec.27-28, 2018

[80] Invited talk titled “Is PID Irreplaceable-On the MFAC and Progress”, Northwestern Polytechnical University. Jan. 9, 2018

[81] Invited talk titled “Is PID Irreplaceable-On the MFAC and Progress”, Xi’an University of Technology. Jan. 11, 2018

[82] Invited talk titled “Data Driven Control For Traffic Systems”, Beijing Information Science and Technology University. Jan. 15, 2018

[83] Invited talk titled “PID and its puzzles-On the MFAC and Progress”, Shenyang Institute of Automation of Chinese Academy of Sciences. June 10, 2018

[84] Invited talk titled “PID and its puzzles-On the MFAC and Progress”, Beijing University of Science and Technology. May 15, 2018

[85] Plenary talk with title “Data Driven Adaptive Learning Control” at “The Seminar on Deep Learning and Width Learning, CAA.”. Beijing. May 31, 2018

[86] Invited talk titled “PID and its puzzles-On the MFAC and Progress”, Guangdong University of Technology. May 16, 2018

[87] Invited talk titled “Is PID irreplaceable?—MFAC and Development”, Guizhou University. April 27, 2018

[88] Invited talk titled “PID and its puzzles-On the MFAC and Progress”, Cyprus University.  March 17-22, 2019

[89] Invited talk titled “PID and its puzzles-On the MFAC and Progress”, Shanghai University. April 10, 2019

[90] Invited talk titled “Does R. E. Kalman’s Paradigm Still Works at the Big Data/AI Era? at “The 2019 Symposium on Intelligent Traffic Safety Control and Information Processing”, Chongqing Jiaotong University. July 16, 2019.

[91] Plenary talk at “The International Symposium on Information and Control” with title “PID and its puzzles-On the MFAC and Progress”. Donghua University, Shanghai. Sept.16, 2019

[92] Invited talk titled “How to write a proposal to NSFC”, Shanghai University Of Engineering Science. Sept. 17, 2019

[93] Invited talk titled “PID and its puzzles-On the MFAC and Progress”, Nanjing University of Posts and Telecommunications. Sept. 18, 2019

[94] Plenary talk at “The Symposium on Distributed System Control, Learning and Optimization” with title “PID and its puzzles-On the MFAC and Progress”. Southwestern University of Finance And Economics, Chengdu. Sept. 20, 2019

[95] Invited talk titled “PID and its puzzles-On the MFAC and Progress”, Tianjin University. Sept. 28, 2019

[96] Plenary talk at “The 2019 International Workshop on Complex-systems for Future Technologies and Applications (IWCFTA)” talk title is “PID and its puzzles-On the MFAC and Progress”. Southeast University. Oct. 12, 2019

[97] Invited talk titled “PID and its puzzles-On the MFAC and Progress”, Nanjing University of Information Science and Technology. Oct. 12, 2019

[98] Plenary talk at “The 2019 Sino-German Science Forum” with title “PID and its puzzles-On the MFAC and Progress”. Shanghai University. Oct. 17, 2019

[99] Invited talk titled “PID and its puzzles-On the MFAC and Progress”, Qufu Normal University. Oct. 24, 2019

[100] Semi-Plenary talk at “The Forum on Urban Intelligent Transportation Development” of “The 14th China Intelligent Transportation Annual Conference” with title “On Data Driven Traffic Control Systems”, Qingdao. Oct.31-Nov.2, 2019

[101] Plenary talk at “The 5th Workshop of TCCT Discontinuous Control Group” with title “Does R. E. Kalman’s Paradigm Still Works at the Big Data/AI Age?”, Shandong Normal University. Nov.15-17, 2019

[102] Invited talk titled “PID and its puzzles-On the MFAC and Progress”, Southwest Jiaotong University. Dec. 17, 2019

[103] Invited talk titled “PID and its puzzles-On the MFAC and Progress”, Liaoning University of Technology. Dec. 30, 2019

[104] Plenary talk at “The Workshop on Artifical Intelligent: Intelligent Optimization, Technology and Application” with title “The Control Sciences at the Big Data or AI Age”. Qingdao University. Dec. 24-25, 2019.

[105] Semi-Plenary talk at “The Forum on Artifical Intelligent and Data Science, CAC Congress” with title “The Control Sciences at the Big Data or AI Age”. Nov. 22-24, 2019.

[106] Lecturer on the “Pre-conference Workshop on System Performance Analysis and Optimization, CCC 2020”. Title is “Does R. E. Kalman’s Paradigm Still Works at the Big Data/AI Age?”. June 26, 2020

[107] Lecturer on the “International Postgraduate Student School on Big Data and AI” With talk titled “The Control Sciences at the Big Data or AI Age”. Central South University, Hunan. Aug. 22, 2020

[108] Lecturer on the “Pre-conference Workshop on Big Data and Intelligent Control Theory, CSSC2020” , with title “On Model Free Adaptive Control”. Qingdao. Sept. 18, 2020.

[109] Plenary talk at IEEE ICCSS 2020 with title “On Model Free Adaptive Control”. Guangzhou. Nov. 13-15, 2020

[110] Plenary talk at “The 6th Workshop of TCCT Discontinuous Control Group” with title“The Control Theory under the Age of Big Data/AI” , Zhejiang Normal University, Nov. 29, 2020

[111] Plenary talk at “The 2021 IEEE International Conference on Real-time Computing and Robotics (IEEE RCAR 2021)” with title “The Control Theory under the Age of Big Data/AI”, Xining, China. July. 15-19, 2021

[112] Plenary talk at “The Seminar on Optimization and Intelligent Technology” with title “How to deaign a control system with ability of dealing with data and knowledge”, Qingdao. Dec. 3-5, 2021

[113] Invited talk with title “How to deaign a control system with ability of dealing with data and knowledge”, Jilin University (Online talk). Dec. 11, 2021

[114] Invited talk with title “How to deaign a control system with ability of dealing with data and knowledge”, Beijing Technology and Business University (Online talk). Dec. 11, 2021

■ Guest Editor

[1] IEEE Transactions on Neural Networks. Special Issue “Data Based Optimization, Control and Modelling ”, Guest Editor: Tianyou Chai, Zhongsheng Hou, F L Lewis, Amir Hussain and Dongbin Zhao, Vol.22, No.12, 2011.

[2] Acta Automatica Sinica. Special Issue on “Extensions of Reinforcement Learning and Adaptive Control”, Guest Editor: Frank L. Lewis, Warren Dixon, Zhongsheng Hou, Tansel Yucelen, Vol. 1, No.3, 2014.

[3] International Journal of Automation & Computing (IJAC). Special Issue: Latest Advances in ILC/RLC Theory and Applications. Quest Editors: Ronghu Chi, Zhongsheng Hou. Vol.12, No. 3, 2015

[4] IEEE Transactions on Industrial Electronics. Special issue “Data Driven Control and Learning Systems”. Guest Editor: Prof. Zhongsheng Hou; Prof. Huijun Gao, Prof. Frank L. Lewis. IEEE Transactions on Industrial Electronics, Vol.64, No.5, May 2017.

[5] Invited session organizer and Chairman on the title “Data Driven Control and Optimization” or “Data Driven Control and Applications” or “Data Driven Leaning and Control” in several international conferences, including CCC’2011/2012/2013, IEEE CCA’2011, ASCC’2013, IFAC- LSS’2013, respectively.

[6] Energies. journal special issue: “Energy Efficiency and Data-Driven Control”, 12(7), 2019. Guest Editor: Radu-Emil Precup, Zhongsheng Hou

■ Journal Editor

[1] Acta Automatica Sinica, 2012-2019.

[2] Control and Decision, 2012-present.

[3] Systems Science and Mathematics, 2013-present

[4] Control Theory and Applications, 2014- present

■  SELECTED RESEARCH FUNDS

[1] Principal Investigator: On the learning based model free adaptive control theory and applications in freeway traffic on/off ramp metering. The National Natural Science Foundation of China, 2005-2007. Grant No. 60474083. RMB 23.0000

[2] Principal Investigator: The Research and development on the License Plate- Recognition system and application in Shenzhen City, The Traffic Management Bureau of Shenzhen, 10.2006-3.2007. RMB 115.0000

[3] Principal Investigator: The high order internal model based iterative learning control and application in freeway traffic system. The National Natural Science Foundation of China, 2008-2010. Grant No. 60774022. RMB 28.0000

[4] Principal Investigator: The data-driven control theory and application in large scale complex systems. The Key Project Program of National Natural Science Foundation of China (NSFC), 2009-2012. Grant No. 60834001. RMB 210.0000

[5] Principal Investigator: The generalized iterative learning control and applications in high speed railway. The Major International Cooperation Project Program of National Natural Science Foundation of China (NSFC), 2012-2016. Grant No. 61120106009. RMB 265.0000.

[6] Principal Investigator: Big Data Based System Prediction and Control for Complex Urban Traffic Networks. The Key Project Program of National Natural Science Foundation of China (NSFC). 2015-2019. Grant No. 61433002. RMB 370.0000.

[7] Principal Investigator: On the basic theory and key technology for subway train intelligent unmanned operation. The Key Project Program of National Natural Science Foundation of China (NSFC). Grant No. 61833001. 2019-2023. RMB 283.0000

■ ACHIEVEMENTS AND SELECTED PUBLICATIONS

1. Founder on Model-Free Adaptive Control (MFAC) since 1994

Model Free Adaptive Control (MFAC) was initiated in Professor Hou’s seminal Ph.D. thesis in 1994, and thoroughly formalized in CRC Press book in 2013 and IEEE Trans. on Automatic Control regular paper in 2019. MFAC is a novel control theory with following characteristics: originality, integrity, correctness, superiority and applicability.

Originality means that, MFAC includes original basic concepts including, Pseudo Partial Derivative (PPD) or Pseudo Gradient (PG) for unknown SISO non-affine nonlinear system; Pseudo Jaccobian Matrix (PJM）or Partitioned Pseudo Jaccobian Matrix (PPJM）for unknown MIMO non-affine nonlinear system, and even term “Model Free Adaptive Control (MFAC)” itself; Original basic mathematical tool for system and control theory: the equivalent dynamic linearization data modeling method, including the Compact Form Dynamic Linearization (CFDL), the Partial Form Dynamic Linearization (PFDL), and the Full Form Dynamic Linearization (FFDL), data models for SISO/MISO/MIMO non-affine nonlinear systems; Original basic assumption for control system stability analysis: the generalized Lipschitz condition for SISO/MISO/MIMO non-affine nonlinear systems, and the original stability analysis approach: the contraction mapping principle based stability analysis approach rather than the overwhelming Lyapunov function method for SISO/MISO/MIMO non-affine nonlinear systems; Original robustness concept and its analysis tool: the statistics-based influence analysis on the stability and tracking performance under the data-dropout, packet loss/disorder, or data noise, etc. for SISO/MISO/MIMO non-affine nonlinear systems.

Integrity implies that MFAC is a novel adaptive control theory for the unknown non-affine discrete-time nonlinear systems with a novel mechanism, which was initiated in 1994. The outstanding feature of MFAC is that the control system design needs only the I/O data of the controlled process and does not include any information of the mathematical model. After about 30 years, it has been developed into systematic works. MFAC consists of indirect MFAC and direct MFAC, each kind of MFAC has following four dimensional framework. The dimensions are as follows: 1) Different controlled objects, including SISO/MISO/MIMO/complicated connected/repetitive operation nonlinear systems; 2) Different dynamic linearization data models for a given nonlinear system, including the compact-form dynamic linearization (CFDL), partial-form dynamic linearization (PFDL), and full-form dynamic linearization(FFDL), data models; 3) Different parameter estimation algorithms for the parameters in the data model, including projection-type algorithms, least-square-type algorithms for the time-varying parameters, etc. 4) Different controller structure designing methods, such as optimal control, predictive control, learning control, etc.

Indirect MFAC, by using the dynamic linearization data modeling apporach on the unknown nonlinear system model, is designed based on the linearized data model, and the adaptive control law and estimation law both for control input and pseudo-gradient vector are implemented using the I/O data of the closed-loop system. It has a four dimensional systematic framework. Different combination consists of different kinds of direct MFAC schemes.

表1 间接型MFAC的四维架构体系 [Tab.1: Systematic framework of Indirect MFAC]

Direct MFAC, based on the dynamic linearization data model on the pre-specified ideal controller for a given unknown nonaffine nonlinear system, is a novel kind of MFAC, which theoretically transformed the control system designing problem explicitly into the identification issue of the designed linearized controller parameters, and it also has a systematic framework. Different combination consists of different kinds of direct MFAC schemes.

表2 直接型MFAC的四维架构体系 [Tab. 2: Systematic framework of direct MFAC]

Correctness hints that the stability, monotonic convergence of the error dynamics, and internal stability of the MFAC schemes are proved rigorously. The stability analysis is based on the contraction mapping principle, not the Lyapunov stability theory. The contraction mapping based stability proof method is novel in the adaptive control research community, and it might be the fundamental method for control system design when the system model is unavailable.

The theoretical stability analysis for CFDL-MFAC scheme, the PFDL-MFAC scheme, and most general FFDL-MFAC scheme for SISO nonlinear systems are proved rigorously. For the MIMO cases, the stability results are also are proved rigorously for the different MFAC schemes.

Superiority indicates that MFAC has not only a systematic framework with rigorous stability guarantee, but also has progressiveness and compatibility with the other control methods. Since MFAC is designed by only using the measured closed-loop I/O data of the controlled plant, the unsolvable theoretical problems in traditional model-based control theory, such as the accurate modeling and model reduction, the unmolded dynamics and robustness, the persistent excitation condition and the closed-loop control, etc., does not exist under the framework of MFAC theory owing to the fact that all the information of plant dynamics is included in the I/O measurement data.

The well-known PID control and the traditional adaptive control for discrete-time linear time-invariant systems can be explicitly shown as the special cases of MFAC theoretically. Further, from the view point of MFAC, the traditional iterative learning control (ILC) theory for the discrete-time nonlinear systems can be regarded as the special case of MFAC in the iteration axis due to that the traditional ILC requires a pre-specified constructive controller structure and a constant control gain for the strictly repetitive task with ideal assumptions including the identical initial values, identical desired trajectory, and affine nonlinearity. The model free adaptive iterative learning control (MFAILC) consists of indirect MFAILC and direct MFAILC. Allmost all the controller forms in traditional ILC can be shown explicitly as special cases of the MFAILC controllers, which is designed in the systematic theory-support way in both indirect and direct MFAILC. Final, the learning errors for both kinds of MFAILC schemes can be guaranteed to converge monotonically to zero in the normal distance measurement with rigorously mathematics analysis rather than the lambda norm sense.

In additional, the MFAC theory and the dynamic linearization data model methods have been used in many control theory branches to give birth to some novel data driven control methods or research directions, such as data driven model free multi-agent consensus or formation control, data driven sliding mode control, data driven networked control, data driven predictive control, event-triggered MFAC, data driven fault diagnosis and adaptive fault-tolerant control, data driven networked predictive control, observer based MFAC, MFAC under attacks, etc.

Applicability of MFAC was validated by following records: citation as whole chapters or sections in 14 monographs, 2 textbooks; successful applications in over 230 different practical plants including field applications such as lateral control of autonomous vehicles, and temperature control of silicon rod. MFAC has been applied or studied with more than one chapter in 53 doctoral thesis, including 7 thesis from well-known foreign universities of USA, UK, Germany, Switzerland, Brazil, France, and Romania. Till June of 2021, there are 120 patents hold by others using MFAC theory as the key technology only within in China.

Selected papers on MFAC

Indirect MFAC

For SISO nonnlinear systems

[1] Zhongsheng Hou, The parameter identification, adaptive control and model free learning adaptive control for nonlinear systems, Ph.D thesis, Shenyang: Northeastern University, 1994. (in Chinese)

[2] Zhongsheng Hou, “Nonparametric models and its adaptive control theory”, Beijing: Science Press, 1999. (in Chinese)

[3] Zhongsheng Hou and Shangtai Jin, “Model Free Adaptive Control—Theory and Applications”, Beijing: Science Press, 2013 (In Chinese)

[4] Zhongsheng Hou and Shangtai Jin, “Model Free Adaptive Control—Theory and Applications”, CRC Press, 2013

[5] Zhongsheng Hou and Huang Wenhu, The model-free learning adaptive control of a class SISO nonlinear systems, Proceedings of the 1997 American Control Conference, Albuquerque, USA, 4-6, June 1997, P343-344.

[6] Zhongsheng Hou* and Shangtai Jin, A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems, IEEE Transactions on Control Systems Technology,19(6), 1549-1558. 2011

[7] Zhongsheng Hou, Ronghu Chi and Huijun Gao, An Overview of Dynamic-Linearization-Based Data-driven Control and Applications, IEEE Transactions on Industrial Electrnoics, 64(5), pp4076–4090, 2017

[8] Shida Liu, Zhongsheng Hou*, Taotao Tian, Zhidong Deng, and Zhenxuan Li, A Novel Dual Successive Projection-Based Model-Free Adaptive Control Method and Application to an Autonomous Car, IEEE Transactions on neural networks and learning systems, 30(11), 3444-3457, 2019

[9] Zhongsheng Hou*, Ronghu Chi and Huijun Gao, An Overview of Dynamic-Linearization-Based Data-driven Control and Applications, IEEE Transactions on Industrial Electrnoics, 64(5), pp4076–4090, 2017.

[10] Xuhui Bu*, Zhongsheng Hou and Hongwei Zhang, Data Driven Multiagent Systems Consensus Tracking Using Model Free Adaptive Control, IEEE Transactions on Neural Networks and Learning Systems, 29(5), 2018, 1514-1524.

[11] Zhongsheng Hou* and Shuangshuang Xiong, On Model-Free Adaptive Control and its Stability Analysis，IEEE Transactions on Automatic Control, 64(11), pp4555-4569, 2019.

[12] Ronghu Chi*, Yu Hui, Biao Huang and Zhongsheng Hou, Active Disturbance Rejection Control for Nonaffined Globally Lipschitz Nonlinear Discrete-time Systems, IEEE Transactions on Automatic Control, 66(12), Dec.2021. p5955-5967.

[13] Shida Liu, Zhongsheng Hou*, Taotao Tian, Zhidong Deng, and Zhenxuan Li, A Novel Dual Successive Projection-Based Model-Free Adaptive Control Method and Application to an Autonomous Car, IEEE Transactions on neural networks and learning systems, 30(11), 2019. 3444-3457.

[14] L Duan, Z Hou*, X Yu, S Jin, K Lu, Data-Driven Model-Free Adaptive Attitude Control Approach for Launch Vehicle With Virtual Reference Feedback Parameters Tuning Method, IEEE Access, 7, 54106-54116, 2019

[15] R Chi*, Y Hui, S Zhang, B Huang, Z Hou，Discrete-time Extended State Observer based Model-free Adaptive Control via Local Dynamic Linearization, IEEE Transactions on Industrial Electronics. Vol.67, No. 10, p8691-8701, 2020. [Regular Paper]

[16] S Liu, Z Hou*, T Tian, Z Deng, L Guo, Path tracking control of a self-driving wheel excavator via an enhanced data-driven model-free adaptive control approach, IET Control Theory & Applications, 14 (2), 220-232, 2020

[17] Haojun Wang and Zhongsheng Hou*, Model-Free Adaptive Fault-Tolerant Control for Subway Trains with Speed and Traction/Braking Force Constraints, IET Control Theory & Applications, 14(12), pp1557-1566, 2020

[18] S Wang, J Li, Z Hou, Q Meng, M Li, Wind power compound model-free adaptive predictive control based on full wind speed, CSEE Journal of Power and Energy Systems, DOI: 10.17775/CSEEJPES.2019.02170

[19] Jianshen Li, Shuangxin Wang, Zhongsheng Hou, and Juchao Zhao, Multivariable Model-free Adaptive Controller Design with Differential Characteristic for Load Reduction of Wind Turbines, IEEE Transactions on Energy Conversion, DOI 10.1109/TEC.2021.3125112.

[20] X Bu, W Yu, L Cui, Z Hou, Z Chen, Event-triggered Data-driven Load Frequency Control for Multi-Area Power Systems, IEEE Transactions on Industrial Informatics, DOI: 10.1109/TII.2021.3130415

[21] Xuhui Bu, Qingfeng Wang, Zhongsheng Hou, Wei Qian, Data driven control for a class of nonlinear systems with output saturation, ISA Transactions, 81, October 2018, Pages 1-7

[22] Y Hui, R Chi, B Huang, Z Hou, S Jin, Observer-based sampled-data model-free adaptive control for continuous-time nonlinear nonaffine systems with input rate constraints, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(12), p7813-7822, 2021

Robustness of MFAC

[23] Zhongsheng Hou*, Xuhui Bu, Model Free Adaptive Control with Data Dropouts, Expert Systems with Applications , 38(8), p10709-10717, 2011

[24] X. Bu, Zhongsheng Hou, F. Yu and F.Wang, Robust model free adaptive control with measurement disturbance, IET Control Theory Appl., 6(9), 1288-1296, 2012

[25] Bu Xuhui, Hou Zhongsheng, Yu Fashan, Fu Ziyi, Model Free Adaptive Control with Disturbance Observer, Journal of Control Engineering and Applied Informatics, 14(4), 42-49, 2012

[26] Xuhui Bu, Yingxu Qiao, Zhongsheng Hou, and Junqi Yang, Model Free Adaptive Control for a Class of Nonlinear Systems Using Quantized Information, Asian Journal of Control, 20(5), pp1-7, 2018

[27] X Bu, P Zhu, Q Yu, Z Hou, J Liang, Model-free adaptive control for a class of nonlinear systems with uniform quantizer, International Journal of Robust and Nonlinear Control. 30, p6383–6398, 2020

Model free adaptive predictive control (MFAPC)

[28] Zhongsheng Hou, Shida Liu and Chenkun Yin, Local Learning-based Model-Free Adaptive Predictive Control for Adjustment of Oxygen Concentration in Syngas Manufacturing Industry, IET Control Theory & Applications, 10(12), pp1384–1394, 2016

[29] Zhongsheng Hou, Shida Liu, Taotao Tian, Lazy-Learning-Based Data-Driven Model-Free Adaptive Predictive Control for a Class of Discrete-Time Nonlinear Systems, IEEE Transactions on Neural Networks and Learning Systems, 28(8), pp1914-1928, 2017

[30] Wei Yu , Rui Wang , Xuhui Bu , Zhongsheng Hou, Model Free Adaptive Control for a Class of Nonlinear Systems with Fading Measurements, Journal of the Franklin Institute, 357(12), Aug.2020, p7743-7760

For MIMO nonlinear systems

[31] Zhongsheng Hou* and Shangtai Jin, Data Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems, IEEE Transactions on Neural Networks, 22(12), pp2173-2188, 2011.

[32] Shuangshuang Xiong, and Zhongsheng Hou*, Data-driven Formation Control for Unknown MIMO Nonlinear Discrete-time Multi-agent Systems with Sensor Fault, IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2021.3087481.

[33] Y Guo, Z Hou*, S Liu, S Jin, Data-Driven Model-Free Adaptive Predictive Control for a Class of MIMO Nonlinear Discrete-Time Systems With Stability Analysis, IEEE Access 7, 102852-102866

[34] Shida Liu, Zhongsheng Hou, Xin Zhang, Honghai Ji, Model-Free Adaptive Control Method for a Class of Unknown MIMO Systems with Measurement Noise and Application to Quadrotor Aircraft, IET Control Theory & Applications, 14 (15), 2084-2096, 2020

[35] J Liang, X Bu, L Cui, Z Hou, Data-Driven Bipartite Formation for a Class of Nonlinear MIMO Multiagent Systems, IEEE Transactions on Neural Networks and Learning Systems, Digital Object Identifier 10.1109/TNNLS.2021.3111893

[36] S. S. Xiong, Z. S. Hou*, Model-Free Adaptive Control for Unknown MIMO Non-affine Nonlinear Discrete-time Systems with Experimental Validation，IEEE Transactions on Neural Networks and Learning Systems, Digital Object Identifier 10.1109/TNNLS.2020.3043711.

[37] S Xiong, Z Hou, S Jin, Model-free adaptive formation control for unknown multiinput-multioutput nonlinear heterogeneous discrete-time multiagent systems with bounded disturbance, International Journal of Robust and Nonlinear Control, 30 (15), 6330-6350, 2020

Direct MFAC

[38] Zhongsheng Hou* and Yuanming Zhu, Controller-Dynamic-Linearization Based Model Free Adaptive Control for Discrete-Time Nonlinear Systems, IEEE Transactions on Industrial Informatics, 9(4), pp2301–2309, 2013.

[39] Yuanming Zhu and Zhongsheng Hou*, Data driven MFAC for a class of discrete time nonlinear systems with RBFNN, IEEE Transactions on Neural Networks and Learning Systems, 25(5), pp1013-1020,2014

[40] Yuanming Zhu*, Zhongsheng Hou, Feng Qian and Wenli Du, Dual RBFNNs based Model-Free Adaptive Control with Aspen HYSYS Simulation, IEEE Transactions on Neural Networks and Learning Systems, 28(3), p759-765, 2017

[41] Y Zhu, Z Hou, Controller dynamic linearisation-based model-free adaptive control framework for a class of non-linear system, IET Control Theory & Applications, 9 (7), pp1162-1172, 2015

Indirect MFAILC

[42] Ronghu Chi, and Zhongsheng Hou*, Dual-stage optimal iterative learning control for nonlinear non-affine discrete-time systems, Acta Automatica Sinica, 33 (10), 1061-1065, 2008

Direct MFAILC

[43] Z. S. Hou*, X. Yu, and C. K. Yin, “A data-driven iterative learning control framework based on controller dynamic linearization,” in Proceedings of the 2018 Annual American Control Conference, Milwaukee, USA, 2018, pp. 5588–5593.

[44] Xian Yu, Zhongsheng Hou*, Marios Polycarpou, and Li Duan, Data-Driven Iterative Learning Control for Nonlinear Discrete-Time MIMO Systems, IEEE Transactions on Neural Networks and Learning Systems. DOI: 10.1109/TNNLS.2020.2980588

[45] Xian Yu, Zhongsheng Hou*, Marios Polycarpou, A Data-Driven ILC Framework for a Class of Nonlinear Discrete-Time Systems, IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2020.3029596

2. Pioneering works on Data Driven Iterative Learning Control (DDILC) Theory

Prof. Hou developed systematic design approaches with DDILC for unknown non-affine discrete-time nonlinear systems with guaranteed monotonic convergence using his pioneering results on the dynamic linearization data modeling technique, and the optimization algorithms for controller’s structure determination and parameter tuning along the iteration axis. DDILC also includes indirect and direct DDILC. DDILC has broken the fundamental limitations of traditional ILC which requires a constructive controller structure and a constant control gain for the strictly repetitive task with ideal assumptions including the identical initial values, identical desired trajectory, and affine nonlinearity. DDILC has re-shaped the prototype iterative learning control theory into data-driven paradigm. It paved a solid foundation for practical applications of ILC schemes. Further, the DDILC includes the most of the tranditional ILC theory as a special case.

Selected papers on DDILC

Indirect DDILC

[1] 卜旭辉 侯忠生，网络约束迭代学习控制理论，科学出版社，2019 [Xuhui Bu and Zhongsheng Hou, Iterative Learning Control Theory under the Networked Constraints, Science Press, 2019]

[2] Chi Ronghu, Zhongsheng Hou, Dual stage optimal iterative learning control for nonlinear non-affine discrete-time systems, Acta Automatica Sinica, 33(10), 1061-1065. 2007

[3] Chenkun Yin, Jian-Xin Xu, Zhongsheng Hou, A High-order Internal Model Based Iterative Learning Control Scheme for Nonlinear Systems with Time-iteration-varying Parameters, IEEE Transactions on Automatic Control, 55(11), 2665-2670, 2010

[4] Dong Shen, Zhongsheng Hou, Iterative Learning Control with Unknown Control Direction: A Novel Data-Based Approach, IEEE Transactions on Neural Networks, 22(12), pp2237-2249, 2011

[5] Bu Xuhui, Hou Zhongsheng, Jin Shangtai and Chi Ronghu, An iterative learning control design approach for networked control systems with data dropouts, International Journal of Robust and Nonlinear Control, 26(1), 91–109, 2016

[6] Xuhui Bu*, Jiaqi Liang, Zhongsheng Hou, and Junqi Yang, Quantized H¥ Control for a Class of 2-D Systems with Missing Measurements, International Journal of Control, Automation and Systems, 15(2), 2017, 706-715

[7] 池荣虎, 侯忠生, 黄彪, 间歇过程最优迭代学习控制的发展: 从基于模型到数据驱动, 自动化学报 43 (6), 917-932, 2017

[8] Ronghu Chi, Xiaohe Liu, Ruikun Zhang, Zhongsheng Hou and Biao Huang, Constrained data-driven optimal iterative learning control, Journal of Process Control, 55, 10–29, 2017

[9] Qiongxia Yu, Zhongsheng Hou and Jian-Xin Xu, D-type ILC based dynamic modeling and norm optimal ILC for high-speed trains", IEEE Transactions on Control Systems Technology, 26(2), pp652-663, March, 2018. Regular Paper

[10] R Chi, Z Hou, S Jin, B Huang, Computationally-Light Non-Lifted Data-Driven Norm Optimal Iterative Learning Control, Asian Journal of Control, DOI: 10.1002/asjc.1569

[11] Ronghu Chi, Zhongsheng Hou, Biao Huang, "Computationally Efficient Data-Driven Higher Order Optimal Iterative Learning Control", IEEE Transactions on Neural Networks and Learning Systems, 29(12), pp5971-5980, 2018

[12] Xuhui Bu, Zhongsheng Hou, Lizhi Cui and Junqi Yang, Stability analysis of quantized iterative learning control systems using lifting representation, International Journal of Adaptive Control and Signal Processing, April 2017, DOI: 10.1002/acs.2767

[13] N Lin, R Chi*, B Huang, Z Hou, Iterative Dynamic Linearization and Identification of a Nonlinear Learning Controller: A Data-driven Approach, Journal of the Franklin Institute, 356 (13), 7009-7027, 2019

[14] Qi Meng, Zhongsheng Hou*, Data-driven multi-inverter cooperative control for voltage tracking and current sharing in islanded AC microgrids, Transactions of the Institute of Measurement and Control, 41 (11), 3145-3157

[15] R Cao*, Z Hou, Y Zhao, B Zhang, Model Free Adaptive Iterative Learning Control for Tool Feed System in Noncircular Turning, IEEE Access, 7, 113712-113725. 2019

[16] Qiongxia Yu, Zhongsheng Hou, X Bu, J Yang, Observer-based Data-driven Constrained Norm Optimal Iterative Learning Control for Unknown Non-affine Non-linear systems with both Available and Unavailable System States, Journal of the Franklin Institute, 2020, 357, p5852–5877

[17] R Chi*, Y Lv, Z Hou, Compensation-based data-driven ILC with input and output package dropouts, International Journal of Robust and Nonlinear Control, 30 (3), 950-965, 2020

[18] Ronghu Chi*, Yu Hui, Biao Huang and Zhongsheng Hou, Adjacent-agent Dynamic Linearization based Iterative Learning Formation Control, IEEE Transactions on Cybernetics, 50(10), p4358-4369, 2020

[19] Yu Hui, Ronghu Chi*, Biao Huang, and Zhongsheng Hou, 3-Dimensional Learning Enhanced Adaptive ILC for Iteration-varying Formation Tasks, IEEE Transactions on Neural Networks and Learning Systems, 31(1), 89-99, 2020

[20] J Xing, N Lin, R Chi, B Huang, Z Hou, Data-driven Nonlinear ILC with Varying Trial Lengths, Journal of the Franklin Institute, 357 (15), 10262-10287, 2020

[21] Xuhui Bu , Zhongsheng Hou, Qiongxia Yu , and Yi Yang, Quantized Data Driven Iterative Learning Control for a Class of Nonlinear Systems With Sensor Saturation, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(12), 2020, pp5119-5129.

[22] Y Hui, R Chi*, B Huang, and Z Hou, Extended State Observer-Based Data-Driven Iterative Learning Control for Permanent Magnet Linear Motor With Initial Shifts and Disturbances, IEEE Transactions on Systems, Man, and Cybernetics: Systems. 51(3), 2021, pp1881-1891

[23] Ronghu Chi, Yu Hui, Chiang-Ju Chien, Biao Huang, Zhongsheng Hou, Convergence Analysis of Sampled-data ILC for Locally Lipschitz Continuous Nonlinear Nonaffine Systems with Non-repetitive Uncertainties, IEEE Transactions on Automatic Control, 66(7), July 2021, p3347-3354

[24] Na Lin, Ronghu Chi, Biao Huang, Zhongsheng Hou, Event-triggered Nonlinear Iterative Learning Control, IEEE Trans on Neural Networks and Learning Systems. 32(11), 2021, p5118-5128

[25] R Chi, Y Wei, R Wang, Z Hou, Observer based Switching ILC for Consensus of Nonlinear Nonaffine Multi-agent Systems, Journal of the Franklin Institute, 358 (2021) 6195–6216

[26] H Zhang, R Chi, Z Hou, B Huang, Quantisation compensated data-driven iterative learning control for nonlinear systems, Inter. Journal of Systems Science, DOI: 10.1080/00207721.2021.1950232. 2021.

[27] J Liang, X Bu, L Cui, Z Hou, Finite time asymmetric bipartite consensus for multi-agent systems based on iterative learning control, International Journal of Robust and Nonlinear Control, 2021;31:5708–5724.

[28] Qi Meng and Zhongsheng Hou, Active Disturbance Rejection Based Repetitive Learning Control with Applications in Power Inverters, IEEE Transactions on Control System Technology, 29(5), 2021. 2038-2048

DDPILC

[29] Qiongxia Yu and Zhongsheng Hou, Data-Driven Predictive Iterative Learning Control for a Class of MIMO Nonlinear Systems, Transactions of the Institute of Measurement and Control, DOI: 10.1177/ 0142331215592692, 2015, sagepub.co.uk/journalsPermissions.nav

[30] Q Yu*, Z Hou, X Bu, Q Yu, RBFNN-Based Data-Driven Predictive Iterative Learning Control for Nonaffine Nonlinear Systems, IEEE transactions on neural networks and learning systems. 31(4), 1170-1182, 2020

[31] G Jiang, Z Hou, Iterative learning model predictive control approaches for trajectory based aircraft operation with controlled time of arrival, International Journal of Control, Automation and Systems, 18(10), 2020, 2641-2649

DDTILC and DDPTPILC

[32] Ronghu Chi, Zhongsheng Hou, Shangtai Jin, Danwei Wang and Jiangru Jian, "Enhanced Data-driven Optimal Terminal ILC Using Current Iteration Control Knowledge", IEEE Transactions on Neural Networks and Learning Systems, 26(11), pp2939-2948, 2015

[33] Ronghu Chi; Yu Liu; Zhongsheng Hou; Shangtai Jin, Data-driven terminal iterative learning control with high-order learning law for a class of non-linear discrete-time multiple-input–multiple output systems, IET Control Theory & Applications, 9 (7), pp1075-1082, 2015

[34] Ronghu Chi, Yu Liu, Zhongsheng Hou, Shangtai Jin, High-order Data-driven Optimal TILC Approach for Fed-batch Processes, The Canadian Journal of Chemical Engineering, 93(8), pp1455–1461, 2015.

[35] Chi, R., D. Wang, F. L. Lewis, Z. Hou, and S. Jin, Adaptive Terminal ILC for Iteration-varying Target Points, Asian Journal of Control, 17 (3), pp 952-962, 2015

[36] Ronghu Chi, Yu Liu, Zhongsheng Hou, Shangtai Jin, A Novel Data-driven Terminal ILC with High-order Learning law for a Class of Nonlinear Discrete-time MIMO Systems, IET Control Theory and Applications, 9(7), pp1075-1082, 2015. DOI:  10.1049/iet-cta.2014.0754.

[37] S Jin, Z Hou, R Chi, Optimal Terminal Iterative Learning Control for the Automatic Train Stop System, Asian Journal of Control, 17 (5), 1992-1999, 2015

[38] Y Liu, R H Chi, Z S Hou, Neural network state learning based adaptive terminal ILC for tracking iteration-varying target points, International Journal of Automation and Computing, 12 (3), 266-272, 2015

[39] Ronghu Chi, Biao Huang, Zhongsheng Hou and Shangtai Jin, Data-driven high-order terminal iterative learning control with a faster convergence speed, Int J Robust Nonlinear Control. 28 (1), 103-119, 2018

[40] Ronghu Chi, Zhongsheng Hou, Shangtai Jin, and Biao Huang, An Improved Data-Driven Point-to-Point ILC Using Additional On-Line Control Inputs With Experimental Verification, IEEE Transactions On Systems, Man, and Cybernetics: Systems, 49(4), APRIL 2019, 687-696 [Beijing Jiaotong Univ]

[41] X Bu*, P Zhu, Z Hou, J Liang, Finite-Time Consensus for Linear Multi-agent Systems Using Data-driven Terminal ILC, IEEE Transactions on Circuits and Systems II: Express Briefs. 67(10), 2020, p2029-2033.

[42] X Bu, J Liang, Z Hou, R Chi, Data-Driven Terminal Iterative Learning Consensus for Nonlinear Multiagent Systems With Output Saturation, IEEE Transactions on Neural Networks and Learning Systems, 32(5), May 2021, p1963- 1973

AILC

[43] Ronghu Chi, Zhongsheng Hou, and Jianxin Xu, A discrete-time adaptive ILC for systems with iteration-varying trajectory and random initial condition, Automatica, 44,2207–2213, 2008.

[44] Ruikun Zhang, Zhongsheng Hou, Ronghu Chi and Honghai Ji,Adaptive Iterative Learning Control for Nonlinearly Parameterized Systems with Unknown Time-varying Delays and Input Saturations, International Journal of Control, 88 (6), pp1133-1141, 2015

[45] Ronghu Chi, Zhongsheng Hou, et al, A Data-driven Adaptive ILC for a Class of Nonlinear Discrete-time Systems with Random Initial States and Iteration-varying Target Trajectory, Journal of the Franklin Institute, 352 (6), 2407-2424, 2015.

[46] Ronghu Chi and Zhongsheng Hou, etc., A Unified Data-driven Design Framework of Optimality-based Generalized Iterative Learning Control, Computers and Chemical Engineering, 77, pp10-23, 2015

[47] B Xuhui, W Taihua, H Zhongsheng, C Ronghu, Iterative learning control for discrete-time systems with quantised measurements, IET Control Theory & Applications, 9(9), 2015, p1455-1460

[48] Honghai Ji, Zhongsheng Hou, and Ruikun Zhang, Adaptive Iterative Learning Control for High-speed Trains with Unknown Speed Delays and Input Saturations, IEEE Transactions on Automation Science and Engineering, 13(1), 2016, 260-273

[49] Honghai Ji, Zhongsheng Hou, Lingling Fan, Frank L. Lewis, Adaptive iterative learning reliable control for a class of nonlinearly parameterized systems with unknown state delays and input saturation, IET-Control theory and applications. 10(17), 2016, pp 2160 – 2174

[50] Qiongxia Yu and Zhongsheng Hou, Adaptive Iterative Learning Control for Nonlinear Uncertain Systems with Both State and Input Constraints, Journal of the Franklin Institute, 353 (15), 3920-3943, 2016

[51] Zhang Ruikun, Hou Zhongsheng*, Ji Honghai and Yin Chenkun, “Adaptive iterative learning control for a class of nonlinearly parameterized systems with input saturations,” International Journal of Systems Science. 47(5), pp1084-1094, 2016

[52] Xuhui Bu , Zhongsheng Hou, Adaptive Iterative Learning Control for Linear Systems with Binary-Valued Observations, IEEE Transactions on Neural Networks and Learning Systems, 29(1), 2018 . pp232-237.

[53] Xuhui Bu, Lizhi Cui, Zhongsheng Hou and Wei Qian, Formation control for a class of nonlinear multiagent systems using model-free adaptive iterative learning, Int J Robust Nonlinear Control. 2017, DOI: 10.1002/rnc.3961

[54] Xuhui Bu, Qiongxia Yu, Zhongsheng Hou, Wei Qian. Model Free Adaptive Iterative Learning Consensus Tracking Control for a Class of Nonlinear Multiagent System. IEEE Transactions on Systems, Man, and Cybernetics: Systems, VOL. 49, NO. 4, APRIL 2019, 677-686 [Beijing Jiaotong Univ]

[55] X Bu*, S Wang, Z Hou, W Liu, Model Free Adaptive Iterative Learning Control for a Class of Nonlinear Systems with Randomly Varying Iteration Lengths, Journal of the Franklin Institute, 356 (5), 2491-2504, 2019

[56] N Lin, R Chi*, B Huang, Z Hou, Multi-lagged-input iterative dynamic linearization based data-driven adaptive iterative learning control, Journal of the Franklin Institute 356 (1), 457-473, 2019

[57] Qiongxia Yu and Zhongsheng Hou, Adaptive Fuzzy Iterative Learning Control for High-Speed Trains with Both Randomly Varying Operation Lengths and System Constraints, IEEE Transactions on Fuzzy Systems, 29(8), 2021, 2408-2418

[58] G Liu, Z Hou*, RBFNN-Based Adaptive Iterative Learning Fault-Tolerant Control for Subway Trains With Actuator Faults and Speed Constraint, IEEE Transactions on Systems, Man, And Cybernetics: Systems, 51(9), 2021, p5785- 5799

[59] G Liu*, Z Hou, Adaptive Iterative Learning Control for Subway Trains Using Multiple-Point-Mass Dynamic Model Under Speed Constraint, IEEE Transactions on Intelligent Transportation, 22(3), 2021, 1388-1400.

[60] G Liu, Z Hou*, Cooperative Adaptive Iterative Learning Fault-Tolerant Control Scheme for Multiple Subway Trains, IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2020.2986006

[61] R Chi, H Zhang, B Huang, Z Hou, Quantitative data-driven adaptive iterative learning control: From trajectory tracking to point-to-point tracking, IEEE Transactions on Cybernetics, Digital Object Identifier 10.1109/TCYB.2020.3015233

[62] Ronghu Chi, Yu Hui, Biao Huang, Zhongsheng Hou and Xuhui Bu, Spatial Linear Dynamic Relationship of Strongly Connected Multiagent Systems and Adaptive Learning Control for Different Formations, IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2020.2977391

[63] R Chi, Y Hui, R Wang, B Huang, Z Hou, Discrete-Time-Distributed Adaptive ILC With Nonrepetitive Uncertainties and Applications to Building HVAC Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Digital Object Identifier 10.1109/TSMC.2021.3113090

[64] H Li, R Chi, Z Hou, B Huang, Double Dynamic Linearization-Based Higher Order Indirect Adaptive Iterative Learning Control, IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2021.3125374

[65] Ronghu Chi, Yu Hui, Biao Huang, Zhongsheng Hou, Xuhui Bu, Data-driven Adaptive Consensus Learning from Network Topologies, IEEE Transactions on Neural Networks and Learning Systems, https://doi.org/10.1109/TNNLS.2021.3053186.

[66] Wei Yu, Rui Wang, Xuhui Bu, Zhongsheng Hou, Zhonghua Wu, Resilient Model-Free Adaptive Iterative Learning Control for Nonlinear Systems Under Periodic DoS Attacks via a Fading Channel, IEEE Transactions on Systems, Man, and Cybernetics: Systems, DOI: 10.1109/TSMC.2021.3091422

[67] X Bu, W Yu, Q Yu, Z Hou, J Yang, Event-Triggered Model-Free Adaptive Iterative Learning Control for a Class of Nonlinear Systems Over Fading Channels, IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2021.3058997

Direct DDILC

[68] Z. S. Hou, X. Yu, and C. K. Yin, A data-driven iterative learning control framework based on controller dynamic linearization, in Proceedings of the 2018 Annual American Control Conference, Milwaukee, USA, 2018, pp5588–5593.

[69] Na Lin, Ronghu Chi, Biao Huang, and Zhongsheng Hou, Iterative dynamic linearization and identification of a nonlinear learning controller: A data-driven approach, Journal of the Franklin Institute, 356(13), 2019, p7009-7027

[70] Xian Yu, Zhongsheng Hou*, Marios M. Polycarpou, and Li Duan, "Data-Driven Iterative Learning Control for Nonlinear Discrete-Time MIMO Systems" , IEEE Transactions on Neural Networks and Learning Systems. DOI: 10.1109/TNNLS.2020.2980588

[71] Xian Yu, Zhongsheng Hou*, Marios M. Polycarpou, A Data-Driven ILC Framework for a Class of Nonlinear Discrete-Time Systems, IEEE Transactions on Cybernetics，DOI: 10.1109/TCYB.2020.3029596

[72] Xian Yu, Zhongsheng Hou*, Marios M. Polycarpou, Distributed Data-Driven Iterative Learning Consensus Tracking for Nonlinear Discrete-Time Multi-Agent Systems, IEEE Transactions on Automatic Control, DOI: 10.1109/TAC.2021.3105653

[73] Xian Yu, Zhongsheng Hou*, Marios M. Polycarpou, Controller-Dynamic-Linearization-Based Data-Driven ILC for Nonlinear Discrete-Time Systems With RBFNN, IEEE Trans. on Systems, Man, and Cybernetics: Systems, DOI: 10.1109/TSMC.2021.3110790

[74] Xian Yu, Zhongsheng Hou*, Marios M. Polycarpou, Model-Free Adaptive Learning Formation Control of Nonlinear Non-Affine Multi-Agent Systems, 2020 59th IEEE Conference on Decision and Control (CDC), 4037-4042. 2020

3. Pioneering works on DDILC Based Road/Freeway/Train Traffic Controls since 2003

The traffic systems have two outstanding characteristics, that is, accurate plant modeling is very difficult, and the traffic systems operate in a repetitive pattern in daily or weekly manner. Previous studies of traffic control methods use the model based modern control theory to design traffic control systems which will leads to the designed system hard to obtain the desired performance in actual traffic application due to the inaccurate model. Further, the existing Road/Freeway/Train traffic control methods do not have the ability to make use of the repeatability and periodicity, which purposely ignores the outstanding characteristics of traffic systems, consequently will cause the designed traffic system be incapable to the ultilizing and learning ability of traffic data. The proposed systematic data driven learning control methods with applications in traffics control fields have opened a new way for utilization of traffic data and traffic repetitive patterns instead of traffic system models. By the way, Prof. Hou was the first researcher introducing the learning control methods into traffic (road/freeway/train) control field.

Selected Papers on Freeway Traffic Control

[1]  Zhongsheng Hou and Jian-Xin Xu, Freeway traffic density control using iterative learning control approach, The IEEE 6th International Conference on Intelligent Transportation Systems, Shanghai, China, October 12-15, 2003

[2]  Zhongsheng Hou, Jian-Xin Xu and Hongwei Zhong, Freeway Traffic Control Using Iterative Learning Control Based Ramp Metering and Speed Signaling, IEEE Transactions on Vehicular Technology, Regular paper. 56(2), 466-477, 2007

[3]  Zhongsheng Hou, Xin Xu, Jingwen Yan, Jian-Xin Xu, Gang Xiong, A Complementary Modularized Ramp Metering Designing Approach based on Iterative Learning Control and ALINEA, IEEE Trans. on Intelligent Transportation Systems, 12(4), 2011. pp1305-1318

[4]  Zhongsheng Hou, Jingwen Yan and Jianxin Xu, Iterative Learning Control Based Local Ramp Metering with Iteration-dependent Factors, IEEE Transactions on Intelligent Transportation Systems, 13( 2), 2012, pp606-618.

[5]  Ronghu Chi, Zhongsheng Hou, Shangtai Jin, Danwei Wang, Jiangen Hao, A Data-driven Iterative Feedback Tuning Approach of ALINEA for Freeway Traffic Ramp Metering with PARAMICS Simulations, IEEE Trans. on Industrial Informatics, 9(4), pp2310-2317, 2013

[6]  Zhongsheng Hou and Xingyi Li, Repeatability and Similarity of Traffic flow and Long Term Prediction, IEEE Trans. on Intelligent Transportation Systems, 17 (6), pp1786-1796, 2016

[7]  Zhongsheng Hou, Jian-Xin Xu and Jingwen Yan, An Iterative Learning Approach for Density Control of Freeway Traffic Flow Via Ramp Metering, Transportation Research, Part C, 16(1) (2008) 71–97

[8]  侯忠生 金尚泰 赵明，宏观交通流模型参数的迭代学习辨识方法，自动化学报，2008，34(1), 64-71.[Hou Z S, Jin S T, Zhao M. Iterative learning identification method for the macroscopic traffic flow model. Acta Automatica Sinica, 2008, 34(1),64-71.]

[9]  侯忠生 晏静文，带有迭代学习前馈的快速路无模型自适应入口匝道控制，自动化学报，35(5), 588-595, 2009. [Hou Z S, Yan J W, Model Free Adaptive Control Based Freeway Ramp Metering with Feedforward Iterative Learning Controller, Acta Automatica Sinica, 35(5), 588-595, 2009]

[10] Chi Ronghu and Hou Zhongsheng, A model-free periodic adaptive control for freeway traffic density via ramp metering, Acta Automatica Sinica, 2010, 36(7)，1029-1032

[11] Zhongsheng Hou, Jian-Xin Xu, The iterative learning control based traffic volume control approach via local ramp metering, 44th IEEE Conference on Decision and Control and European Control Conference ECC 2005, Seville, Spain, Dec.12-15,2005

Selected Papers on Road Traffic Signal Control

[12]  Zhongsheng Hou and Ting Lei, Constrained Model Free Adaptive Predictive Perimeter Control and Route Guidance for Multi-Region Urban Traffic Systems, IEEE Transactions on Intelligent Transportation Systems, DOI: 10.1109/TITS.2020.3017351

[13]  T Lei, Z Hou, Y Ren, Data-Driven Model Free Adaptive Perimeter Control for Multi-Region Urban Traffic Networks With Route Choice, IEEE Transactions on Intelligent Transportation Systems. 21(7), 2020, p2894-2905

[14]  Dai Li and Zhongsheng Hou, Perimeter Control of Urban Traffic Networks Based on Model-Free Adaptive Control, IEEE Trans.on Intelligent Transportation, 22(10), 2021, p6460-6472

[15]  D Li, Z Hou, Data-driven urban traffic model‐free adaptive iterative learning control with traffic data dropout compensation, IET Control Theory & Applications, 2021;15:1533–1544.

[16]  Y Ren, Z Hou, Robust model-free adaptive iterative learning formation for unknown heterogeneous non-linear multi-agent systems, IET Control Theory & Applications, 2020, 14(4), pp654-663

[17]  Ye Ren, Zhongsheng Hou, Isik Ilber Sirmatel, and Nikolas Geroliminis, Data driven Model Free Adaptive Iterative Learning Perimeter Control for Large scale Urban Road networks, Transprotation Research, Part C. 115, 2020, 102618

[18] Qi C, Hou Z S, Jia Y. Optimal signal timing strategy based on the equilibrium of queue length. Control and Decision, 2012, 27(8).

Selected Papers on Train Operation Control

[19]  Y Wang, Z Hou, and X Li, A novel automatic train operation algorithm based on iterative learning control theory, 2008 IEEE International Conference on Service Operations and Logistics, and Informatics, 12-15, Beijing, China. Oct. 2008

[20]  Zhongsheng Hou and Yi Wang, Terminal Iterative Learning Control Based Station Stop Control of a Train, International Journal of Control, 84(7), 1263-1274, 2011

[21]  Hairong Dong, Bin Ning, Baigen Cai, and Zhongsheng Hou, Automatic train control system development and simulation for high-speed railways, IEEE Circuits and Systems Magazine, 10(2), pp6-18, 2010

[22]  Heqing Sun, Zhongsheng Hou, and Dayou Li, Coordinated Iterative Learning Control Schemes for Train Trajectory Tracking with Overspeed Protection, IEEE Transactions on Automation Science and Engineering, 10(2), p323-333, 2013

[23]  S Jin, Z Hou, R Chi, Optimal Terminal Iterative Learning Control for the Automatic Train Stop System, Asian Journal of Control, 17 (5), 1992-1999, 2015

[24]  Zhenxuan Li, Zhongsheng Hou, and Chenkun Yin, Iterative Learning Control for Train Trajectory Tracking under Speed Constrains with Iteration-Varying Parameter, Transactions of the Institute of Measurement and Control, 2015, 37(4) 485–493

[25]  Zhenxuan Li and Zhongsheng Hou, Adaptive iterative learning control based high speed train operation tracking under iteration-varying parameter and measurement noise, Asian Journal of Control, 17(5), pp1779-1788, 2015

[26]  Honghai Ji, Zhongsheng Hou, and Ruikun Zhang, Adaptive Iterative Learning Control for High-speed Trains with Unknown Speed Delays and Input Saturations, IEEE Transactions on Automation Science and Engineering, 13(1), 260-273, 2016

[27]  Qiongxia Yu, Zhongsheng Hou and Jian-Xin Xu, D-type ILC based dynamic modeling and norm optimal ILC for high-speed trains, IEEE Transactions on Control Systems Technology, 26(2), pp652-663, 2018

[28]  Qiongxia Yu and Zhongsheng Hou, Adaptive Fuzzy Iterative Learning Control for High-Speed Trains with Both Randomly Varying Operation Lengths and System Constraints, IEEE Transactions on Fuzzy Systems, DOI: 10.1109/TFUZZ.2020.2999958

[29]  G Liu, Z Hou, Adaptive Iterative Learning Control for Subway Trains Using Multiple-Point-Mass Dynamic Model Under Speed Constraint, IEEE Transactions on Intelligent Transportation, DOI: 10.1109/TITS.2020.2970000

[30]  G Liu, Z Hou, RBFNN-Based Adaptive Iterative Learning Fault-Tolerant Control for Subway Trains With Actuator Faults and Speed Constraint, IEEE Transactions on Systems, Man, And Cybernetics: Systems, DOI: 10.1109/TSMC.2019.2957299

[31]  G Liu, Z Hou, Cooperative Adaptive Iterative Learning Fault-Tolerant Control Scheme for Multiple Subway Trains, IEEE Trans. on Cybernetics, DOI: 10.1109/TCYB.2020.2986006

[32]  Haojun Wang and Zhongsheng Hou, Model-Free Adaptive Fault-Tolerant Control for Subway Trains with Speed and Traction/Braking Force Constraints, IET Control Theory & Applications, 14(12), pp1557-1566, 2020

[33]  Zhenxuan Li, Chenkun Yin, Honghai Ji, Zhongsheng Hou, Constrained Spatial Adaptive Iterative Learning Control for Trajectory Tracking of High Speed Train, IEEE Transactions on Intelligent Transportation Systems, 2021, DOI: 10.1109/TITS.2021.3106653

4. Pioneering works on Data Driven Control

Professor Hou coined ‘data driven control (DDC)’ as a new branch of control theory in 2009. Data-driven controls are the control theories and methods in which the controller is designed directly using on-line or off-line I/O data of the controlled system or knowledge from the data processing without using explicit or implicit information of the mathematical model of the controlled process, and whose stability, convergence, and robustness can be guaranteed by rigorous mathematical analysis under certain reasonable assumptions. In a short word, DDC designs control system directly only from the measured I/O data of the controlled plant, meanwhile to get out of all the inherent fundamental dilemmas in modern control theory, such as the unmodeled dynamics and robustness, the accurate modeling and model reduction, and the persistence of excitation condition and closed-loop control etc.

Due to his foresight and sagacity, he was invited to be a contributor on the report “Systems & Control for the future of humanity” by IFAC Task Road Map Committee[8]. DDC is now widely accepted in system and control community. The papers [1,2] are the first academic survey paper on the topic of DDC in the world, and they often be cited as the first literature in a journal paper on data driven control research.

Papers and Book Chapters on Data Driven Control

[1] Zhongsheng Hou and Zhuo Wang, From Model Based Control to Data Driven Control: Survey, Classification and Perspective, Information Sciences, 235(20), pp3-35, 2013

[2] 侯忠生 Jian-Xin Xu, 数据驱动控制理论及方法的回顾和展望，自动化学报，35(6), 2009，pp650-667 [Hou Z S, Xu J X. On data-driven control theory: The state of the art and perspective. Acta Automatic Sinica, 2009, 35(6): 650-667]

[3] Jian-Xin Xu and Zhongsheng Hou，Notes on Data-driven System Approaches，Acta Automatic Sinica, 35(6), 2009, pp668-675

[4] 侯忠生，数据驱动的自适应迭代学习控制系统的设计和分析，“10000个科学难题，科学出版社，2011，pp716-718”.[Zhongsheng Hou, The design and analysis on data driven adaptive iterative learning control systems, 10000 Selected Problems in Sciences, Science Press, 2011, pp716-718]

[5] 侯忠生，数据驱动控制理论基础问题，“10000个科学难题，科学出版社，2011,pp722-725”.[ Zhongsheng Hou, The fundamental issues on the data driven control theory, 10000 Selected Problems in Sciences, Science Press, 2011, pp722-725”.]

[6] 池荣虎 侯忠生，第2章：学习控制。 “王飞跃、陈俊龙主编，智能控制：方法与应用，”中国科学技术出版社，2020 [Ronghu Chi and Zhongsheng Hou, Chapter 2 : Learning Control, in “Feiyue Wang and C. L. Philip Chen (Ed), Intelligent Control: Method and Application, China Science and Technology Press” , 2020]

[7] 侯忠生，第5章：数据驱动控制系统。“黄琳主编，控制理论若干瓶颈问题，科学出版社，2022。”[Zhongsheng Hou, Chapter 5: Data Driven Control System in “Lin Huang (Ed), Some fundamental Problems in control theory, Science Press, 2022”]

[8] Zhongsheng Hou, Section 4.2. In Francoise Lamnabhi-Lagarrigue, Anuradha Annaswamy, Sebastian Engell, Alf Isaksson, Pramod Khargonekar, RichardM. Murray, Henk Nijmeijer, Tariq Samad, Dawn Tilbury, Paul Van den Hof, “Systems & Control for the future of humanity, research agenda: Current and future roles, impact and grand challenges,” Annual Reviews in Control, 43, 2017, pp1–64.

[9] Yongqiang Li and Zhongsheng Hou, Data-Driven Asymptotic Stabilization for Discrete-Time Nonlinear Systems, Systems &Control Letter, 64, 79-85, 2014

[10] Yongqiang Li, Zhongsheng Hou, Yuanjing Feng* and Ronghu Chi, Data-Driven Approximate Value Iteration with Analysis of Optimality Error Bound, Automatica, 78, p79-87, 2017

[11] Y Li, C Yang, Z Hou, Y Feng*, C Yin，Data-driven approximate Q-learning stabilization with optimality error bound analysis, Automatica, 103, 435-442, 2019

[12] Y Li, C Lu, Z Hou, Y Feng*, Data-driven robust stabilization with robust domain of attraction estimate for nonlinear discrete-time systems, Automatica, 119, 109031, 2020

5. Others

Filtering and Estimation

[1] Honghai Ji, Frank L. Lewis, ZhongshengHou and Dariusz Mikulski, Distributed Information weighted Kalman Consensus Filter for Sensor Networks, Automatica, 77, 2017, pp18–30

Adaptive Control

[2] Y. Liang, Y.X. Li, W. Che, Z. Hou, Adaptive Fuzzy Asymptotic Tracking for Nonlinear Systems With Nonstrict Feedback Structure, IEEE Transactions on Cybernetics, 51(2), 853-861, 2021

[3] Xiaoyan Hu, Yuan-Xin Li, and Zhongsheng Hou, Event-Triggered Fuzzy Adaptive Fixed-Time Tracking Control for Nonlinear Systems, IEEE Transactions on Cybernetics, DOI:10.1109/TCYB.2020.3035779

[4] Xiaoyan Hu, Yuan-Xin Li, Shaocheng Tong, Zhongsheng Hou, Event-Triggered Adaptive Fuzzy Asymptotic Tracking Control of Nonlinear Pure-Feedback Systems With Prescribed Performance, IEEE Transactions on Cybernetics, 2021. DOI: 10.1109/TCYB.2021.3118835

[5] Y. X. Li, X. Hu, W. Che, Z. S. Hou, Event-Based Adaptive Fuzzy Asymptotic Tracking Control of Uncertain Nonlinear Systems, IEEE Transactions on Fuzzy Systems, 29(10), 2021, p3003-3013

[6] Xiaoyan Hu, Yuan-Xin Li, Zhongsheng Hou, Ben Niu, Event-triggered prescribed performance adaptive fuzzy asymptotic tracking of nonstrict-feedback nonlinear systems, International Journal of Robust and Nonlinear Control, 31 (12), 5776-5795, 2021

[7] Y. Liang, Y. X. Li, Z. S. Hou, Adaptive fixed-time tracking control for stochastic pure-feedback nonlinear systems, International Journal of Adaptive Control and Signal Processing, 2021;35:1712–1731.

[8] Yuanxin Li; Zhongsheng Hou, Wei-Wei Che, Zheng-Guang Wu, Event-Based Design of Finite-Time Adaptive Control of Uncertain Nonlinear Systems, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2021.3054579

[9] Y. Zheng, Y. X. Li, W.W. Che, Z.S. Hou, Adaptive NN-Based Event-Triggered Containment Control for Unknown Nonlinear Networked Systems, IEEE Transactions on Neural Networks and Learning Systems, Digital Object Identifier 10.1109/TNNLS.2021.3107623

Selected application papers of MFAC cited by other scholars published in IEEE Transactions since 2019

MFAC has been applied in more than 230 different fields or with laboratory equipment. Selected typical MFAC applications just reported in IEEE Transactions since 2019 are as follows:

[1] Panbing Wang, Dengfeng Li, Shihui Shen, and Yajing Shen, Automatic Microwaveguide Coupling Based on Hybrid Position and Light Intensity Feedback, IEEE/ASME Trans.on Mechatronics, 24(3), 2019, 1166-1175

[2] Masoud Fetanat, Michael Stevens, Christopher Hayward, and Nigel H. Lovell, A Physiological Control System for an Implantable Heart Pump That Accommodates for Interpatient and Intrapatient Variations, IEEE Trans.on Biomedical Engineering, 67(4), 2020, 1167-1175

[3] Yuan Ma, Xu Wang, Zhi Quan, and H. Vincent Poor, Data-Driven Measurement of Receiver Sensitivity in Wireless Communication Systems, IEEE Transactions on Communications, 67(5), 2019, p3665- 3676

[4] Qingsong Ai, Da Ke, Jie Zuo, Wei Meng, Quan Liu, Zhiqiang Zhang and Sheng Q. Xie, High-Order Model-Free Adaptive Iterative Learning Control of Pneumatic Artificial Muscle with Enhanced Convergence, IEEE Transactions on Industrial Electronics, 67(11), 2020, p9548- 9559

[5] Di Wu, Huabin Chen, Yiming Huang, and Shanben Chen, Online Monitoring and Model-Free Adaptive Control of Weld Penetration in VPPAW Based on Extreme Learning Machine, IEEE Transactions on Industrial Informatics, 15(5), 2019, p2732- 2740

[6] Haoran Tan, Zhiqiang Miao, Yaonan Wang, Min Wu, and Zhiwu Huang, Data-Driven Distributed Coordinated Control for Cloud-Based Model-Free Multiagent Systems With Communication Constraints, IEEE Transactions on Circuits and Systems–I, 67(9), p3187-3198, 2020

[7] Dezhi Xu, Weiming Zhang, Peng Shi, and Bin Jiang, Model-Free Cooperative Adaptive Sliding-Mode-Constrained-Control for Multiple Linear Induction Traction Systems, IEEE Transactions on Cybernetics, Digital Object Identifier 10.1109/TCYB.2019.2913983

[8] Saeid Aghaei Hashjin, Shengzhao Pang, El-Hadj Miliani, Karim Ait-Abderrahim, and Babak Nahid-Mobarakeh, Data-Driven Model-Free Adaptive Current Control of a Wound Rotor Synchronous Machine Drive System，IEEE Transactions on Transportation Electrification, 6(3), 2020. p1146-1156

[9] Ping Zhou, Shuai Zhang, Liang Wen, Jun Fu, Tianyou Chai, and Hong Wang, Kalman Filter-Based Data-Driven Robust Model-Free Adaptive Predictive Control of a Complicated Industrial Process, IEEE Transactions on Automation Science and Engineering, DOI: 10.1109/TASE.2021.3061974

[10] Haotian Liu, Wenchuan Wu and Anjan Bose, Model-Free Voltage Control for Inverter-Based Energy Resources: Algorithm, Simulation and Field Test Verification, IEEE Transactions on Energy Conversion, 36(2), 2021, p1207-1215

[11] Can Pei, Suzhi Bi, and Zhi Quan, Data-Driven Bandpass Filter Design for Estimating Symbol Rate of Sporadic Signal at Low SNR, IEEE Transactions on Wireless Communications, DOI 10.1109/TWC.2021.3114678

[12] Xi Wu, Mengting Wang, Mohammad Shahidehpour, Shuang Feng, and Xi Chen, Model-Free Adaptive Control of STATCOM for SSO Mitigation in DFIG-based Wind Farm, IEEE Transactions on Power Systems, DOI 10.1109/TPWRS.2021.3082951

[13] Yong-Sheng Ma, Wei-Wei Che, Chao Deng, and Zheng-Guang Wu, Distributed Model-Free Adaptive Control for Learning Nonlinear MASs Under DoS Attacks, IEEE Trans. on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2021.3104978

[14] Xingyu Shi, Yijia Cao, Mohammad Shahidehpour,Yong Li, Xi Wu, and Zhiyi Li, Data-Driven Wide-Area Model-Free Adaptive Damping Control With Communication Delays for Wind Farm, IEEE Transactions on Smart Grid, 11(6), 2020. p5052-5071

[15] Jun-Chao Ren, Ding Liu, and Yin Wan, Model-Free Adaptive Iterative Learning Control Method for the Czochralski Silicon Monocrystalline Batch Process, IEEE Transactions on Semiconductor Manufacturing, DOI 10.1109/TSM.2021.3074625

[16] Saeid Aghaei Hashjin, Adrien Corne, Shengzhao Pang, Karim Ait-Abderrahim, El-Hadj Miliani, and Babak Nahid-Mobarakeh, Current Sensorless Control for WRSM Using Model-Free Adaptive Control, IEEE Transactions on Transportation Electrification, 7(2), 2021. p683-693

[17] Dai Li and Bart De Schutter, Distributed Model-Free Adaptive Predictive Control for Urban Traffic Networks, IEEE Trans.on Control Systems Technology, https://doi.org/10.1109/TCST.2021.3059460

[18] Xiaojie Qiu, Yingchun Wang, Huaguang Zhang, and Xiangpeng Xie, Resilient Model Free Adaptive Distributed LFC for Multi-Area Power Systems Against Jamming Attacks, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2021.3123235

[19] Masoud Fetanat, Michael Stevens, Christopher Hayward, Nigel H. Lovell, A Sensorless Control System for an Implantable Heart Pump using a Real-time Deep Convolutional Neural Network, IEEE Transactions on Biomedical Engineering, DOI 10.1109/TBME.2021.3061405

[20] Yanda Huo, Peng Li, Haoran Ji, Jinyue Yan, Guanyu Song, Jianzhong Wu and Chengshan Wang, Data-driven Adaptive Operation of Soft Open Points in Active Distribution Networks, IEEE Transactions on Industrial Informatics, DOI 10.1109/TII.2021.3064370

Partial list of MFAC based new control theory branches

1) MFAC Based Sliding Mode Control

[1] M. L. Corradini., A Robust Sliding-Mode Based Data-Driven Model-Free Adaptive Controller, IEEE Control Systems Letters, Vol.6, 2022, p421-427.

[2]Dezhi Xu, Yan Shi, and Zhicheng Ji, Model-Free Adaptive Discrete-Time Integral Sliding-Mode-Constrained-Control for Autonomous 4WMV Parking Systems, IEEE Transactions on Industrial Electronics, 2018, 65(1): 834-843

[3] Yongpeng Weng and Xianwen Gao, Data-Driven Robust Output Tracking Control for Gas Collector Pressure System of Coke Ovens, IEEE T-IE, 2017, 64(5): 4187-4198.

[4] Dong Liu and Guang-Hong Yang, Data-Driven Adaptive Sliding Mode Control of Nonlinear Discrete-Time Systems With Prescribed Performance, IEEE T-SMC: Systems, DOI: 10.1109/TSMC.2017.2779564.

[5] Dong Liu, Guang-Hong Yang, Performance-based data-driven model-free adaptive sliding mode control for a class of discrete-time nonlinear processes, JPC, 2018, 68: 186-194.

[6] Dong Liu and Guang-Hong Yang, Prescribed Performance Model-Free Adaptive Integral Sliding Mode Control for Discrete-Time Nonlinear Systems, IEEE T-NNLS, 30(7), 2019, 2222- 2230

2) Model Free Adaptive Iterative Learning Control

[1]Xuhui Bu, Qiongxia Yu, and Zhongsheng Hou, Model Free Adaptive Iterative Learning Consensus Tracking Control for a Class of Nonlinear Multiagent System, IEEE Transactions on Systems, Man, and Cybernetics: Systems, DOI: 10.1109/TSMC.2017.2734799

[2] Jiannan Chen, Changchun Hua, and Xinping Guan, Iterative Learning Model-Free Control for Networked Systems With Dual-Direction Data Dropouts and Actuator Faults, IEEE Transactions on Neural Networks and Learning Systems, DOI:10.1109/TNNLS.2020.3027651

3) MFAC based Fault Tolerant Control

[1]Lei Liu, Zhanshan Wang, Xianshuang Yao and Huaguang Zhang, Echo State Networks- Based Data-Driven Adaptive Fault Tolerant Control with Its Application to Electromechanical System, IEEE/ASME Transactions on Mechatronics, DOI: 10.1109/TMECH.2018.2817495.

[2]Zhanshan Wang, Lei Liu, and Huaguang Zhang，Neural Network-Based Model-Free Adaptive Fault-Tolerant Control for Discrete-Time Nonlinear Systems With Sensor Fault, IEEE Transactions on Systems, Man, And Cybernetics: Systems, DOI: 10.1109/TSMC.2017.2672664

[3] Jiannan Chen , Changchun Hua, and Xinping Guan,Iterative Learning Model-Free Control for Networked Systems With Dual-Direction Data Dropouts and Actuator Faults, IEEE TNNLS, DOI: 10.1109/TNNLS.2020.3027651

4) Model Free Adaptive Predictive Control

[1]Zhongsheng Hou, Shida Liu, and Taotao Tian, Lazy-Learning-Based Data-Driven Model-Free Adaptive Predictive Control for a Class of Discrete-Time Nonlinear Systems, IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(8): 1914-1928

[2]Ping Zhou, Shuai Zhang, Liang Wen, Jun Fu, Tianyou Cha, and Hong Wang, Kalman Filter-Based Data-Driven Robust Model-Free Adaptive Predictive Control of a Complicated Industrial Process, IEEE Transactions on Automation Science and Engineering, DOI: 10.1109/TASE.2021.3061974

5) Event-Triggered MFAC

[1] Dong Liu, Guang-Hong Yang, Event-based model-free adaptive control for discrete-time non-linear processes, IET Control Theory Appl., 2017, 11(15): 2531-2538

[2] Na Lin, Ronghu Chi , and Biao Huang, Event-Triggered Model-Free Adaptive Control, IEEE Transactions on Systems, Man, And Cybernetics: Systems, DOI: 10.1109/TSMC.2019.2924356

6) MFAC Based Multiagent Systems Consensus

[1] Xuhui Bu, Zhongsheng Hou and Hongwei Zhang, Data Driven Multiagent Systems Consensus Tracking Using Model Free Adaptive Control, IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(5), 1514-1524.

[2] Haoran Tan, Zhiqiang Miao, Yaonan Wang, Min Wu, and Zhiwu Huang, Data-Driven Distributed Coordinated Control for Cloud-Based Model-Free Multiagent Systems With Communication Constraints, IEEE Transactions on Circuits And Systems–I, DOI: 10.1109/TCSI.2020.2990411

7) MFAC Based Notworked Control

[1] Zhong-Hua Pang, Guo-Ping Liu, Donghua Zhou, and Dehui Sun, Data-Driven Control With Input Design-Based Data Dropout Compensation for Networked Nonlinear Systems, IEEE Transactions on Control Systems Technology, 2017, 25(2): 628-636

[2] Truong Q. Dinh, James Marco, David Greenwood, Kyoung K. Ahn and Jong I. Yoon, Data-Based Predictive Hybrid Driven Control for A Class of Imperfect Networked Systems, IEEE Transactions on Industrial Informatics, DOI 10.1109/TII.2018.2799081.

        8) Observer Based based MFAC

[1] Dezhi Xu, Bin Jiang and Peng Shi, A Novel Model-Free Adaptive Control Design for Multivariable Industrial Processes, IEEE T-IE, 2014, 61(11), 6391-6398.

[2] Dezhi Xu, Bin Jiang and Peng Shi, Adaptive Observer Based Data-Driven Control for Nonlinear Discrete-Time Processes, IEEE T-ASE, 2014, 11(4):1037-1045.