基于干扰观测器的一类不确定非线性系统自适应二阶动态terminal滑模控制

An adaptive second order terminal sliding mode control for a class of uncertain nonlinear systems using disturbance observer

中文关键词  非线性系统  二阶动态terminal 滑模控制  自适应控制  干扰观测器  模糊神经网络

英文关键词  Nonlinear system  second order dynamic terminal sliding mode control  adaptive control  disturbance observer  fuzzy neural networks(FNN)

中文摘要

      针对一类不确定非线性系统的跟踪控制问题, 在考虑建模误差、参数不确定和外部干扰情况下, 以其拥有良好的跟踪性能以及强鲁棒性为目标, 提出基于回归扰动模糊神经网络干扰观测器(recurrent perturbation fuzzy neural networks, RPFNNDO)的鲁棒自适应二阶动态terminal滑模控制策略. 将回归网络、模糊神经网络和sine-cosine扰动函数各自优势相结合, 给出一种回归扰动模糊神经网络结构, 提出RPFNNDO设计方法, 保证干扰估计准确性; 构造基于带有指数函数滑模面的二阶快速terminal滑模面, 给出其控制器设计过程, 避免了滑模到达阶段、传统滑模的抖振问题, 采用具有指数收敛的鲁棒项抑制干扰估计误差对系统跟踪性能的影响, 利用Lyapunov理论证明闭环系统的稳定性; 将该方法应用于混沌陀螺系统同步控制仿真实验, 结果表明所提方法的有效性.

英文摘要

      A robust adaptive second order terminal sliding mode control method, based on recurrent perturbation fuzzy neural networks disturbance observer(RPFNNDO), is proposed for a class of uncertain nonlinear systems which exist the modeling error, parameter uncertainty and external disturbance aiming at tracking performance and strong robustness. Firstly, to have better approximation ability, RPFNNDO is presented to approximate the unknown disturbance in the systems, which combines the advantages of the recurrent networks, fuzzy neural networks and sine-cosine function. Then, in order to overcome the chattering problem in traditional sliding mode, the design process of second-order dynamic terminal sliding mode controller is given by constructing global sliding mode surface based on fast terminal sliding mode surface. At the same time, an exponential robust term can offset the impact of the approximation errors for the systems. And the stability of the closed-loop system is proved by the Lyapunov theory. Finally, the method is applied to control the chaotic synchronization of gyro systems. The results show that the proposed method is effective.