Event-triggered learning-based robust tracking control for robotic manipulators with uncertain dynamics and non-zero equilibrium

Abstract

This paper presents a novel optimal tracking control method for robotic manipulators (RMs) with system uncertainties and non-zero equilibrium points. The existing tracking control methods typically require the precise system dynamics and assume the existence of a zero equilibrium point, which may not always be true in practice. To overcome this issue, a new cost function with respect to the first derivative of system states and a known upper bound on the system’s mismatched disturbances is designed, and then an optimal control problem of RMs is formulated. Furthermore, to solve this optimization, an event-triggered critic learning control framework is proposed to estimate the solution of Hamilton–Jacobi-Bellman equation, reducing the computational burden. In the critic learning control designs, novel weight updating laws related to system stability information are proposed to train the critic neural networks, which eliminates the initial stabilization requirement. Theoretical analysis shows that the stability of the closed-loop system in the sense of uniform ultimate boundedness is ensured. It should be emphasized that the proposed control method offers a simplification of the tracking controller design process and removes the restriction on system dynamics. Finally, simulation results are given to demonstrate the effectiveness of the proposed method.