Pd Control of robot with velocity estimation and uncertainties compensation

Normal industrial PD control of robot has two drawbacks, it needs joint velocity sensors; it cannot guarantee zero steady state error. In this paper we make two modifications to overcome these problems. High-gain observer is applied to estimate the joint velocities, and a 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.