Adaptive Fault-Tolerant Tracking Control for Discrete-Time Multiagent Systems via Reinforcement Learning Algorithm

This article investigates the adaptive fault-tolerant tracking control problem for a class of discrete-time multiagent systems via a reinforcement learning algorithm. The action neural networks (NNs) are used to approximate unknown and desired control input signals, and the critic NNs are employed t...

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Vydané v:IEEE transactions on cybernetics Ročník 51; číslo 3; s. 1163 - 1174
Hlavní autori: Li, Hongyi, Wu, Ying, Chen, Mou
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.03.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Actuators
Adaptive algorithms
Adaptive control
Algorithms
Artificial neural networks
Control stability
Cost function
Discrete time systems
Discrete-time multiagent systems (MASs)
Estimation
Fault tolerance
Fault tolerant systems
fault-tolerant control
Feedback control
Machine learning
Multi-agent systems
Multiagent systems
Neural networks
neural networks (NNs)
Reinforcement learning
reinforcement learning algorithm
Tracking control
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí:This article investigates the adaptive fault-tolerant tracking control problem for a class of discrete-time multiagent systems via a reinforcement learning algorithm. The action neural networks (NNs) are used to approximate unknown and desired control input signals, and the critic NNs are employed to estimate the cost function in the design procedure. Furthermore, the direct adaptive optimal controllers are designed by combining the backstepping technique with the reinforcement learning algorithm. Comparing the existing reinforcement learning algorithm, the computational burden can be effectively reduced by using the method of less learning parameters. The adaptive auxiliary signals are established to compensate for the influence of the dead zones and actuator faults on the control performance. Based on the Lyapunov stability theory, it is proved that all signals of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, some simulation results are presented to illustrate the effectiveness of the proposed approach.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2020.2982168