Dynamic Event-Triggered Fault-Tolerant Control for a Delayed Flexible Manipulator With Actuator Failures

This study develops a novel dynamic event-triggered fault-tolerant boundary control strategy for the flexible manipulator governed by the partial differential equation (PDE) and subject to actuator failures, time delays, and dynamic uncertainties. First, a dynamic compensation scheme employing adaptive neural networks is developed to approximate the unknown dynamics of the system, thereby effectively handling time-delay effects, dynamic uncertainties, and potential actuator faults. Second, to reduce communication burden and computational load, a novel dynamic event-triggered mechanism is introduced. Furthermore, based on the backstepping technique, the neural network-based boundary control law is obtained. Through Lyapunov-based analysis, the dynamic event-triggered boundary strategy guarantees the boundedness of all signals in the closed-loop system, which not only suppresses the elastic deformation but also adjusts the flexible manipulator to track the desired joint angle. Finally, the effectiveness of the developed algorithm is well demonstrated through the numerical simulation results.