Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 51, Issue 3, Pages 1163-1174Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.2982168
Keywords
Reinforcement learning; Artificial neural networks; Actuators; Fault tolerance; Fault tolerant systems; Estimation; Multi-agent systems; Discrete-time multiagent systems (MASs); fault-tolerant control; neural networks (NNs); reinforcement learning algorithm
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Funding
- Local Innovative and Research Teams Project of Guangdong Special Support Program of 2019
- Innovative Research Team Program of Guangdong Province Science Foundation [2018B030312006]
- Science and Technology Program of Guangzhou [201904020006]
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This article investigates the adaptive fault-tolerant tracking control problem for a class of discrete-time multiagent systems via a reinforcement learning algorithm. The direct adaptive optimal controllers are designed by combining the backstepping technique with the reinforcement learning algorithm to reduce computational burden, and adaptive auxiliary signals are established to compensate for the influence of dead zones and actuator faults.
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.
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