Observer-Based H Tracking Control Scheme and Its Application to Robot Arms

Abstract

In this paper, an adaptive dynamic programming (ADP)-based H∞ tracking control method is proposed for unknown continuous-time nonlinear systems. By constructing a neural network (NN) observer with system input and output data, the dynamics of completely unknown systems can be identified online. Then, an augmented system composed of tracking error and reference trajectory dynamics is formulated, and the H∞ tracking control problem is regarded as a zero-sum game problem which is solved by using ADP technique. A critic NN is employed to approximate the cost function to solve the Hamilton-Jacobi-Isaacs equation. Furthermore, the ultimate uniform boundedness of observation errors, critic NN weights, and tracking errors are all proved through Lyapunov’s direct method. Finally, the proposed method is applied to a single link robot arm with unknown dynamics to demonstrate its effectiveness.