Event-Triggered Adaptive Asymptotic Tracking Control of Flexible Robotic Manipulators: A Command Filtered Backstepping Method

Abstract This paper formulates an event-triggered adaptive asymptotic tracking control scheme for flexible robotic manipulators via command filtered backstepping method. Firstly, in the proposed design algorithm, the unknown nonlinear functions are firstly approximated by using intelligent estimation technique. Then, the “explosion of complexity” problem existing in the traditional backstepping procedure is solved by cleverly applying the command filtered backstepping method. In addition, an event-triggered mechanism is adopted so that the control input is updated irregularly following the occurrence of an event. The advantages of the proposed adaptive design scheme are as follows: (i) the Barbalat’s Lemma is used to asymptotically drive the tracking error to zero; (ii) all the variables in the closed-loop system are bounded; (iii) the utilized event-triggered mechanism reduces the transmission frequency of computer and saves computer resources. Finally, the simulation results of the robotic system are given to illustrate the effectiveness of our design scheme.