Event-triggered Decentralized Fault-tolerant Control for Multi-fault Modular Manipulator with Input Constraints

In this paper, a fault-tolerant controller based on event-triggered is proposed for multi-fault modular manipulator with input constraints. The joint torque feedback (JTF) technique is used to establish dynamic model of subsystem. The performance index function designed in this paper included the compensation term for model uncertainty, and the observer for fault estimation and tracking error. The hyperbolic tangent function is introduced to input the system. Adaptive dynamic programming (ADP) is introduced to reduce the dimensionality disaster of the system, and combined with the event-triggered mechanism to reduce the update frequency of the system. Hamilton Jacobi Bellman (HJB) equation is mainly solved by neural network. The stability of the closed-loop system is proved by Lyapunov theorem. The effectiveness of this control method is verified by simulation.