Adaptive Prescribed Performance Motion Control of Servo Mechanisms with Friction Compensation

Abstract

This paper proposes an adaptive control for a class of nonlinear mechanisms with guaranteed transient and steady-state performance. A performance function characterizing the convergence rate, maximum overshoot, and steady-state error is used for the output error transformation, such that stabilizing the transformed system is sufficient to achieve the tracking control of the original system with a priori prescribed performance. A continuously differentiable friction model is adopted to account for the friction nonlinearities, for which primary model parameters are online updated. A novel high-order neural network with only a scalar weight is developed to approximate unknown nonlinearities and to dramatically diminish the computational costs. Comparative experiments on a turntable servo system are included to verify the reliability and effectiveness.