Multilayer Neuroadaptive Output Feedback Control of Constrained Robotic Manipulators With Disturbance Compensation

Abstract

This brief focuses on the motion control issue for uncertain robotic manipulators with unknown nonlinear dynamics and exogenous disturbances simultaneously via the output position measurement only. In detail, the extended state observer is employed to obtain the estimations of immeasurable system state information and lumped disturbances simultaneously. Meanwhile, the multilayer neural network with good approximation performance is incorporated into the observer-controller scheme to identify the unknown nonlinear dynamics. As a result, both large nonlinear dynamics and strong lumped disturbances can be compensated feedforwardly. Significantly, prescribed tracking performance and asymmetric time-varying state constraints can be realized simultaneously.