Motion control of two-link flexible-joint robot with actuator nonlinearities, using backstepping and neural networks

Abstract

We present a state-feedback control of a two-link flexible-joint robot. The control algorithm does not require the mathematical model representing the robot. Three-layer neural networks approximate the unknown plant functions. The neural network weights are adapted on-line. We use backstepping control structure. We use variable structure control to provide robustness to all uncertainties. For simulation, we obtain parameter values of the Euler-Lagrange model from real experiment. We, then, add backlash, deadzone, and additive disturbances to the Euler-Lagrange model to closely replicate the actual robot. We show through simulation that our controller can handle these actuator nonlinearities effectively.