Optimal fault-tolerant control of modular manipulators based on hybrid triggering mechanism

This paper proposes a fault tolerant control method based on a hybrid mechanism combining event-triggered and time-triggered strategies for modular manipulators. By setting the threshold, this method enables intelligent switching between triggering modes when the threshold is reached, ensures trajectory tracking, reduces communication load, and improves fault tolerance. Furthermore, an adaptive fault observer is designed for effectively estimating faults in real time. The adaptive dynamic programming (ADP) algorithm transforms the fault tolerant control problem into an optimal control problem and the Hamilton-Jacobi-Bellman (HJB) equation is solved by the critic neural network (NN) to generate an approximate optimal fault tolerant control strategy. The Lyapunov stability theory verifies that the trajectory tracking error of the closed-loop system is uniformly ultimately bounded (UUB) despite the occurrence of actuator faults. Finally, the effectiveness and reliability of the proposed method are confirmed using the experimental platform.