基于状态全反馈的动态迟滞非线性系统自适应控制

State-based Adaptive Control for Systems with Hysteresis Nonlinearity

该文针对不平滑、多映射动态迟滞非线性系统,提出了一种基于神经网络自适应控制方案。在该方案中,通过利用神经网络来逼近模型误差,避免了目前常用逆模型补偿方案中,需求取复杂逆模型的问题。应用Lyapnov稳定定理,证明了整个闭环系统的跟踪误差及神经网络权值将收敛到零点一个有界邻域内。仿真结果表明,所提出的控制方案能够有效补偿迟滞非线性对系统的影响。

In this paper, a control scheme based on neural networks (NN) is proposed for a class of SISO nonlinear plant preceded by unknown hysteresis , and the weights of NN are adjusted on-line. Using Lyapnov theory also proves that the system is semi-global stability and the tracking errors converge to a small set of origin. The simulation results confirm the effectiveness of the proposed scheme.