Dynamic-Memory Event-Triggered Fuzzy Adaptive Fault-Tolerant Control for Robotic Manipulators: A New Switching Mechanism

In order to eliminate the impact of actuator faults on manipulator control accuracy and save the system communication resources, this paper proposes a dynamic-memory event-triggered fuzzy adaptive fault-tolerant control algorithm based on a novel switching mechanism. First, according to the dynamic evolution characteristics of tracking error, a new actuator switching mechanism is constructed by combining the proposed performance indicator function. As the tracking error exceeds the preset convergence interval, the designed switching mechanism can automatically search for and switch to the optimal actuator operating mode without relying on the fault detection and isolation module, which eliminates the influence of the faults and ensures the control accuracy of the manipulator system. On this basis, a prescribed-time function is introduced to improve the convergence rate of the tracking error, and the settling time can be set in advance. Meanwhile, a dynamic-memory event-triggered control strategy is designed to extend the trigger interval by integrating the historical information of dynamic variables, which can effectively save the system communication resources under the premise of ensuring the control effect. Finally, the superiority of the proposed control algorithm is illustrated by the experimental platform of a 2-degree-of-freedom manipulator.