基于未建模动态补偿的非线性自适应切换控制方法

针对一类不确定的离散时间零动态不稳定的单输入-单输出(Single-input single-output, SISO)非线性系统,提出了一种基于未建模动态补偿的非线性控制器. 采用自适应神经模糊推理系统(Adaptive-network-based fuzzy inference system, ANFIS)和一一映射相结合的方法估计未建模动态.在此基础上,提出了由线性自 适应控制器、非线性自适应控制器以及切换机制组成的自适应切换控制方法.该方法通过对上述两种控制器的切换, 保证闭环系统输入输出信号有界的同时,改善系统性能.本文将要求未建模动态全局有界的条件放宽为线性增长, 建立了所提自适应控制方法的稳定性和收敛性分析.通过仿真比较和水箱的液位控制实验,验证了所提方法的有效性.

Nonlinear Adaptive Switching Control Method Based on Unmodeled Dynamics Compensation

This paper presents a nonlinear controller based on unmodeled dynamics compensation for a class of uncertain and discrete-time single-input single-output (SISO) nonlinear systems with unstable zero-dynamics. By combining an adaptive-network-based fuzzy inference system (ANFIS) with "one-to-one mapping", a compensator for unmodeled dynamics is constructed. With the above development, an adaptive switching control method is proposed that consists of a linear adaptive controller, a nonlinear adaptive controller and a switching mechanism. By using switching between the above two controllers, it has been shown that both an improved performance and stability can be achieved simultaneously. The paper assumes the unmodeled dynamics of the systems to satisfy a linear growth condition, which relaxes the widely used global boundedness condition on the unmodeled dynamics. The analysis on stability and convergence of the adaptive control method are established. Finally, through the simulation based comparative study and the experiment of the proposed control on a tank level adaptive control system, the effectiveness of the proposed method is justified.