Neural Network Observer Based Optimal Tracking Control for Multi-motor Servomechanism with Backlash

Abstract

In this paper, a new neural network observer based optimal tracking control is presented to attenuate the effect of backlash and other uncertainty for the position tracking of multi-motor servomechanism (MMS). By adopting a continuously differentiable function instead of the non-differential dead-zone model of the backlash, the state space representation of MMS is set up by using a linear part of the differentiable function. Based on the state space representation, the optimal neural network (NN) observer is used to estimate the uncertainties and unmeasured states, which combines with the optimal state feedback to synthesis the actual control law. Finally, Lyapunov theory is utilized to certify that the tracking error, the observed error and neural network weights are all semi-globally uniformly ultimately bounded (SGUUB). Simulation results validate the effectiveness of this method.