基于无模型方法的混沌系统自适应控制

传统的混沌控制方法大多需要获知混沌系统的模型知识, 但是工业实际中系统的参数经常是未知的,与此同时系统建模过程当中经常会不可避免地存在未建模的动态不确定性, 这种情况下常规的混沌控制方法不能取得优化的控制性能指标．为解决此问题, 提出了一类基于无模型方法的混沌系统自适应控制算法．该算法基于数据驱动, 无需混沌系统的先验知识, 无需训练过程, 在线调整参数较少, 是一种低成本的控制器．数学证明了该控制系统的稳定性, 仿真结果说明了这种理论的有效性． 关键词： 混沌控制 自适应 无模型 数据驱动

Adaptive control of chaotic systems based on model free method

Conventional chaos control methods need model information of the chaos system, but model parameter is always unknown in engineering practice and the dynamic uncertainty of model-building often appears inevitably. In this case it is impossible to acquire best control performance index of optimization. To resolve this problem an adaptive control algorithm of chaotic systems based on model free method is proposed. It is a low cost controller. With this method, we can easily know a priori knowledge of chaos system only be adjusting a few parameters online without training before hand. We prove that this algorithm has a good stability mathematically. Simulation results show the effectiveness of this theory.
Keywords： chaotic control adaptive model free data-drive