Adaptive Neural Network Control For Smart Materials Robots Using Singular Perturbation Technique

Abstract

ABSTRACT In this paper, an adaptive neural network controller is presented for smart materials robots using Singular Perturbation techniques by modeling the flexible modes and their derivatives as the fast variables and link variables as slow variables. The neural network (NN) controller is to control the slow dynamics in order to eliminate the need tor the tedious dynamic modeling and the error prone process in obtaining the regressor matrix. In addition, inverse dynamic model evaluation is not required and the time‐consuming training process is avoided except for initializing the NNs based on the approximate function values at the initial posture at time t =0. The smart materials bonded along the links are used to active suppress the residue vibration. Simulation results have shown that the controller can control the system successfully and effectively.