On Neural Network Switched Stabilization of SISO Switched Nonlinear Systems with Actuator Saturation

摘要

As we know, saturation, deadzone, backlash, and hysteresis are the most common actuator nonlinearities in practical control system applications. Saturation nonlinearity is unavoidable in most actuators. In this paper, we address the Neural Network saturation compensation for a class of switched nonlinear systems with actuator saturation. An actuator saturation compensation switching scheme for switched nonlinear systems with its subsystem in Bru-novsky canonical form is presented using Neural Network. The actuator saturation is assumed to be unknown and the saturation compensator is introduced into a feed-forward path. The scheme that leads to switched stability and disturbance rejection is rigorously proved. The tracking performance of switched nonlinear system is guaranteed based on common Lyapunov approach under the designed switching strategy.