PD control of robot with velocity estimation and uncertainties compensation

In this paper the normal PD control of the two-link robot is modified in following two ways: (1) A high-gain observer is applied to estimate the joint velocities; (2) The RBF neural networks are used to compensate the gravity and friction. The new PD control may overcome the two drawbacks of the normal PD control. The main contributions of this paper are; a new proof of high-gain observer gives a direct relation between observer gain and observer error. Based on Lyapunov-like analysis, we prove the stability of the closed-loop system if the weights of RBF have certain learning rules and the observer is faster enough.