PD CONTROL OF ROBOT WITH VELOCITY ESTIMATION AND UNCERTAINTIES COMPENSATION

W. Yu and X. Li

Keywords

PD control, highgain observer, RBF neural network

Abstract

Normal industrial PD control of Robot has two drawbacks: it needs joint velocity sensors, and it cannot guarantee zero steadystate error. In this paper we make two modiļ¬cations to overcome these problems. High-gain observer is applied to estimate the joint velocities, and an RBF neural network is used to compensate gravity and friction. We give a new proof for high-gain observer, which explains a direct relation between observer gain and observer error. Based on Lyapunov-like analysis, we also prove the stability of the closed-loop system if the weights of RBF neural networks have certain learning rules and the observer is fast enough.

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