基于在线误差修正自适应SVR的非线性不确定分数阶混沌系统滑模控制

提出了一种基于在线误差修正自适应SVR的滑模控制方法, 用于解决一类非线性不确定分数阶混沌系统的控制问题. 分别通过对混沌系统非线性函数的离线SVR估计和基于增量学习的状态跟踪误差在线SVR预测, 解决了不确定分数阶混沌系统模型难以预测的问题. 同时根据Lyapunov稳定性理论设计出SVR权值自适应调整律. 本文以分数阶Arneodo 系统为例进行仿真, 仿真结果表明了, 对于带有外界噪声扰动的非线性不确定分数阶混沌系统, 该方法可以在有限时间内将系统稳定至期望状态, 提高对非线性函数的预测精度, 改善控制性能.

Sliding mode control of fractional order chaotic system based on an online error correction adaptive SVR

In this paper, a sliding mode control based on an online error correction adaptive SVR is put forward for a class of fractional order chaotic system with nonlinear uncertainty. In order to solve the problem that the uncertainty of the fractional order chaotic system model is difficult to predict, so the nonlinear function of the system is estimated by the offline SVR and the state trace error is forecasted by using incremental learning adaptive online SVR. In addition, the adaptive parameter adjustment law is selected by using the Lyapunov stability theory. Result of simulation of the fractional order Arneodo system shows that the sliding mode control based on the online error correction adaptive SVR can stabilize the nonlinear uncertain fractional order chaotic system with external noise disturbance to an expected state within a limited time. At the same time, both the control performance and the prediction precision of the system's nonlinear function can be improved.