Journal Article

Adaptive neural control for a class of time-delay systems in the presence of backlash or dead-zone non-linearity

Xue, Jianping, Dong, Xinmin, Chen, Yong, Liu, Zongcheng

2014

Overview

This study addresses the adaptive tracking control problem for a class of time-delay systems in strict-feedback form with unknown control gains and uncertain actuator non-linearity. The actuator non-linearity can be either backlash or dead zone, and the proposed approach does not require the knowledge of the bounds of non-linearity parameters. By applying an appropriate Lyapunov–Krasovskii functional and utilising the property of the well-defined trigonometric functions, the problems of time delay and controller singularity are avoided. The feasibility of using a static neural network to attenuate the effect of actuator non-linearity is proved with the aid of intermediate value theorem. Furthermore, it is proved that all closed-loop signals are bounded and the tracking error converges to a small residual set asymptotically. Two simulation examples are provided to demonstrate the effectiveness of the designed method.

• Publisher: The Institution of Engineering and Technology,John Wiley & Sons, Inc
• Subject: Actuators / adaptive control / Adaptive control systems / adaptive neural control / adaptive tracking control problem / asymptotic convergence / Asymptotic properties / Backlash / backlash nonlinearity / closed loop signals / closed loop systems / Computer simulation / control gains / control nonlinearities / Control systems / controller singularity / dead‐zone nonlinearity / delay systems / feedback / intermediate value theorem / Lyapunov methods / Lyapunov‐Krasovskii functional / Neural networks / neurocontrollers / Nonlinearity / nonlinearity parameter bounds / static neural network / strict‐feedback form / time‐delay systems / tracking error / trigonometric functions / uncertain actuator nonlinearity / uncertain systems