Adaptive Fault-Tolerant Tracking Control for MIMO Discrete-Time Systems via Reinforcement Learning Algorithm With Less Learning Parameters

This paper is concerned with a reinforcement learning-based adaptive tracking control technique to tolerate faults for a class of unknown multiple-input multiple-output nonlinear discrete-time systems with less learning parameters. Not only abrupt faults are considered, but also incipient faults are...

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Vydané v:IEEE transactions on automation science and engineering Ročník 14; číslo 1; s. 299 - 313
Hlavní autori: Liu, Lei, Wang, Zhanshan, Zhang, Huaguang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adaptive critic design
Adaptive systems
Algorithms
Control systems
Cost function
Discrete-time systems
Fault tolerance
fault tolerant control
MIMO
multiple-input multiple-output discrete-time systems
Neural networks
reinforcement learning algorithm
Tracking control systems
ISSN:1545-5955, 1558-3783
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Shrnutí:This paper is concerned with a reinforcement learning-based adaptive tracking control technique to tolerate faults for a class of unknown multiple-input multiple-output nonlinear discrete-time systems with less learning parameters. Not only abrupt faults are considered, but also incipient faults are taken into account. Based on the approximation ability of neural networks, action network and critic network are proposed to approximate the optimal signal and to generate the novel cost function, respectively. The remarkable feature of the proposed method is that it can reduce the cost in the procedure of tolerating fault and can decrease the number of learning parameters and thus reduce the computational burden. Stability analysis is given to ensure the uniform boundedness of adaptive control signals and tracking errors. Finally, three simulations are used to show the effectiveness of the present strategy.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2016.2517155