Event-triggered-based adaptive command-filtered asymptotic tracking control for flexible robotic manipulators

This paper investigates the event-triggered adaptive asymptotic tracking control problem for flexible robotic manipulators by using command-filtered backstepping method. Firstly, during the design of the controller, intelligent estimation technique is used to estimate the unknown nonlinear functions existing in the manipulator system. Then, the command-filtered backstepping technique is cleverly employed to reduce the computational burden and solve the “explosion of complexity” problem. Furthermore, the communication burden is greatly alleviated and the intrusion tolerance is enhanced by adopting the event-triggered strategy. By using the classical Barbalat’s lemma, the designed controller not only ensures that all the variables in the closed-loop system are bounded but also insures the tracking error converges to zero asymptotically. In the end, the simulation results considering the different parameters of the manipulator are presented to demonstrate the effectiveness of our control scheme.