Deterministic Learning Based Adaptive Network Control of Robot in Task Space

Abstract

Deterministic learning can achieve locally-accurate approximation of the unknown closed-loop system dynamics while attempting to control a class of nonlinear systems producing recurrent trajectories. Based on deterministic learning, an adaptive neural control algorithm is proposed for unknown robots in task space using radial basis function (RBF) networks. The designed adaptive neural controller can not only guarantee all signals in the closed-loop system uniformly ultimately bounded, but also achieve convergence of partial network weights to their optimal values. It can also learn the unknown closed-loop system dynamics in a stable control process along recurrent tracking orbits. The learned knowledge stored as constant network weights can be reused in a same or similar control task to improve the control performance and to save time and energy. Simulation results demonstrate the effectiveness of the proposed approach.