Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks

Haowei Lin; Bo Zhao; Derong Liu; Cesare Alippi

doi:10.1109/JAS.2020.1003225

Volume 7 Issue 4

Jun. 2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2020 > 7(4): 954-964

Haowei Lin, Bo Zhao, Derong Liu and Cesare Alippi, "Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 954-964, July 2020. doi: 10.1109/JAS.2020.1003225

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Haowei Lin, Bo Zhao, Derong Liu and Cesare Alippi, "Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 954-964, July 2020. doi: 10.1109/JAS.2020.1003225

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Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks

doi: 10.1109/JAS.2020.1003225

Funds: This work was supported in part by the National Natural Science Foundation of China (61533017, 61973330, 61773075, 61603387), the Early Career Development Award of SKLMCCS (20180201), and the State Key Laboratory of Synthetical Automation for Process Industries (2019-KF-23-03)

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Author Bio:
Haowei Lin received the B.S. degree at the School of Automation, Guangdong Polytechnic Normal University, in 2017. He is currently pursuing the M.S. degree at the School of Automation, Guangdong University of Technology. His research interests include adaptive dynamic programming and fault tolerant control

Bo Zhao (M’16) received the B.S. degree in automation, and Ph.D. degree in control science and engineering, all from Jilin University, in 2009 and 2014, respectively. He was a Post-Doctoral Fellow with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, from 2014 to 2017. Then, he joined the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, from 2017 to 2018. He is currently an Associate Professor with the School of Systems Science, Beijing Normal University, Beijing, China. He has authored or coauthored over 80 journal and conference papers. His research interests include adaptive dynamic programming, robot control, fault diagnosis and tolerant control, optimal control, and artificial intelligence-based control. He was the secretary of Adaptive Dynamic Programming and Reinforcement Learning Professional Committee of Chinese Automation Association (CAA), and the secretary of 2017 the 24th International Conference on Neural Information Processing. He is IEEE Member, Asian-Pacific Neural Network Society (APNNS) Member and CAA Member

Derong Liu (S’91−M’94−SM’96−F’05) received the Ph.D. degree in electrical engineering from the University of Notre Dame, Notre Dame, IN, USA, in 1994. He was a Staff Fellow with General Motors Research and Development Center, Warren, MI, USA, from 1993 to 1995. He was an Assistant Professor with the Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA, from 1995 to 1999. He joined the University of Illinois at Chicago, Chicago, IL, USA, in 1999, and became a Full Professor of Electrical and Computer Engineering, and of Computer Science, in 2006. He served as the Associate Director of the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing, China, from 2010 to 2015. He is currently a Full Professor with the School of Automation, Guangdong University of Technology, Guangzhou, China. He has authored or co-authored 19 books. Prof. Liu was a recipient of the Michael J. Birck Fellowship from the University of Notre Dame, in 1990, the Harvey N. Davis Distinguished Teaching Award from the Stevens Institute of Technology, in 1997, the Faculty Early Career Development Award from the National Science Foundation in 1999, the University Scholar Award from the University of Illinois, from 2006 to 2009, and the Overseas Outstanding Young Scholar Award from the National Natural Science Foundation of China in 2008. He was the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems from 2010 to 2015. He is the Editor-in-Chief of Artificial Intelligence Review (Springer). He is a Fellow of the International Neural Network Society and the International Association of Pattern Recognition

Cesare Alippi (F’06) is a Professor with the Politecnico di Milano, Milano, Italy and Università della Svizzera Italiana, Lugano, Switzerland. Alippi is a visiting professor at the Guangdong University of Technology in Guangzhou, China and Consultant Professor at the Northwestern Polytechnical Univerisity in Xi’an, China. He has been a Visiting Researcher/Professor at UCL (UK), MIT (USA), ESPCI (F), CASIA (PRC), A*STAR (SIN), UKobe (JP). Alippi is an IEEE Fellow, Member of the Administrative Committee of the IEEE Computational Intelligence Society, Board of Governors member of the International Neural Network Society, Board of Directors member of the European Neural Network Society, Past Vice-President education of the IEEE Computational Intelligence Society, past associate editor of the IEEE Transactions on Emerging Topics in Computational Intelligence, the IEEE Computational Intelligence Magazine, theIEEE Transactions on Instrumentation and Measurements, the IEEE Transactions on Neural Networks (and Learning Systems). He received the 2018 IEEE CIS Outstanding Computational Intelligence Magazine Award, the 2016 Gabor Award from the International Neural Networks Society and the IEEE Computational Intelligence Society Outstanding Transactions on Neural Networks and Learning Systems Paper Award; in 2013 the IBM Faculty award; in 2004 the IEEE Instrumentation and Measurement Society Young Engineer Award. Current research activity addresses Adaptation and Learning in Non-stationary Environments, Graph Learning and Intelligence for Embedded, IoT and Cyber-physical Systems. He holds 8 patents, has published one monograph book, 7 edited books and about 200 papers in international journals and conference proceedings
Corresponding author: B. Zhao is with the School of Systems Science, Beijing Normal University, Beijing 100875, China (e-mail: zhaobo@bnu.edu.cn)
Received Date: 2020-03-03
Revised Date: 2020-04-18
Accepted Date: 2020-05-13

Available Online: 2020-06-11

Abstract

Abstract

In this paper, a data-based fault tolerant control (FTC) scheme is investigated for unknown continuous-time (CT) affine nonlinear systems with actuator faults. First, a neural network (NN) identifier based on particle swarm optimization (PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network (PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation (HJBE) more efficiently. Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.
- Adaptive dynamic programming (ADP),
- critic neural network,
- data-based,
- fault tolerant control (FTC),
- particle swarm optimization (PSO)

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References

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Highlights

A data-based fault tolerant control scheme is investigated.
The unknown system dynamics is approximated by PSO-NN identifier.
The HJB equation is solved with a high successful rate by the PSOCNN.
The online fault tolerant control is shown to be optimal.

Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks

doi: 10.1109/JAS.2020.1003225

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