Adaptive Robust Control of an Uncertain Robotic Manipulator with Guaranteed Transient and Steady State Performance

Guaranteeing human-specified performance in robot control subject to complex uncertainties is challenging. This study takes time-varying (possibly fast), bounded with unknown bounds uncertainties into consideration in controlling an uncertain robotic manipulator system (URMS). We employ an adaptive law to on-line estimate the unknown bounds for uncertainty compensation, and prescribed transient and steady state performance (TSSP) are guaranteed by using a state transformation technique. As a result, a class of adaptive robust control with prescribed TSSP is proposed. Simulation results are provided to validate the effectiveness of the proposed approach.