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ADP-based fault-tolerant consensus control for multiagent systems with irregular state constraints
Zijie Guo1, Qi Zhou2, Hongru Ren2
1School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou, 510665, Guangdong, China.
Summary
This study develops an adaptive dynamic programming (ADP) method for nonlinear multiagent systems (MASs) with irregular state constraints and actuator faults. The approach ensures fault-tolerant consensus control and optimal tracking performance.
Area of Science:
- Control Theory
- Artificial Intelligence
- Robotics
Background:
- Nonlinear multiagent systems (MASs) face challenges with state constraints and actuator faults.
- Existing methods often overlook irregular, time-varying, or emergent state constraints.
Purpose of the Study:
- To address consensus control for MASs with irregular state constraints and actuator faults.
- To develop a fault-tolerant control strategy using adaptive dynamic programming (ADP).
Main Methods:
- System transformation to convert constrained MASs into unconstrained ones.
- Finite-time distributed observer for leader state estimation.
- Critic-only ADP-based fault-tolerant control strategy with online fault compensation.
- Improved weight learning law for neural networks (NNs) using historical data.
Main Results:
- Achieved optimal tracking control for each agent despite irregular constraints and actuator faults.
- Ensured that actuator faults do not propagate to neighboring agents.
- Demonstrated convergence of NN weights under finite excitation conditions.
- Validated the method with a multiple manipulator systems example.
Conclusions:
- The proposed ADP-based method effectively handles irregular state constraints and actuator faults in MASs.
- The approach ensures robust and optimal consensus control.
- The method enhances learning efficiency and stability of the control system.
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