Event-triggered fuzzy learning control for uncertain robotic manipulators via gradient descent approach

This paper develops an event-triggered fuzzy learning control scheme for robotic manipulators subject to uncertain dynamics and unknown control gains. A novel gradient descent strategy is put forward to balance the trade-off between the event-triggered updating of motor driving signals and tracking performance, by which the control design dependence on prior knowledge from manipulator dynamics is simultaneously alleviated. The constructed gradient descent-based fuzzy learning mechanism improves the approximation accuracy of fuzzy logic systems (FLSs) to uncertain manipulator dynamics. And an error compensating-based command filter design technique is introduced to reduce the computation burden. Lyapunov analysis theory rigorously proves the semi-global uniform ultimate boundedness (SUUB) of the closed-loop system. In the end, a simulation case and some comparison results are illustrated to demonstrate the efficacy of the proposed gradient descent-based event-triggered control scheme.