Neural network model based control of a flexible link manipulator

This paper addresses the control of a manipulator with link flexibilities. The increased complexity in its dynamics presents challenges to controllers based on non-colocated sensing. In this paper a nonlinear predictive control approach is presented using a discrete time multilayered perceptron network model for the plant. The neural network model is trained to predict future outputs based on the available past measurements. At each sampling instant, the discrete time control input is calculated by minimizing a performance criterion. The method is compared to non-model based colocated PD control. Simulation results are presented.