Neural network based tracking control of a flexible macro-micro manipulator system.

Abstract

In this paper, we address the problem of stable tracking control of a flexible macro-micro manipulator (M(3)) system. A two-layer neural network is utilized to approximate the nonlinear robot dynamic behavior of the M(3) system, and the controllers for the macro and micro arms are developed without any need for prior knowledge of the dynamic model of the controlled M(3) system. A learning algorithm for the neural network using Lyapunov stability theory is derived. It is shown that both the tracking error and the weight-tuning error are uniformly ultimately bounded under this new control scheme. Simulation results are presented and compared to those obtained using a PD controller.