不确定非线性系统的模糊鲁棒跟踪控制

提出了一种基于T-S模糊型的鲁捧自适应跟踪控制方法.整个控制方案在结合所有 的局部线性状态反馈控制器的基础上,引入了基于自适应神经网络的鲁棒控制器.所提出的 模糊自适应鲁棒控制器设计方法不需要求取李亚普诺夫方程的公共解,不要求系统的不确定 性项满足任何匹配条件或约束条件所提出的带有补偿项的完全自适应RBF神经网络,通过 在线自适应调整RBF神经网络的权重、函数中心和宽度,提高了神经网络的学习能力,可以 有效地对消系统的未知不确定性的影响.同时通过自适应补偿项来在线估计神经网络的近似 误差边界,弥补了神经网络的不足.所提出的方案保证了闭环系统的稳定性,有效地提高了 系统的鲁棒性和跟踪性能.仿真实例表明了所提出方法的有效性.

Fuzzy Robust Tracking Control for Uncertain Nonlinear Systems

A robust adaptive tracking control method is presented based on T-S fuzzymodel.The overall control scheme is constructed by combining all local state feedbackcontrollers and robust adaptive neural network based controllers.By the proposed fuzzyadaptive robust controller there is no need to find a common positive definite matrix sat-isfying matrix Lyapunov equation,and no constraint or matching conditions are required.The learning ability of the proposed full adaptive RBF neural network with compensatingcomponent is improved through adaptive tuning of the weights,centers and widths on line.The adaptive compensating component is designed to compensate the shortcoming of theneural network through online estimating the bound on the neural network approximateerror.With the proposed method,the stability of the closed-loop system is guaranteed andimproved robustness and tracking performance are obtained.Simulation example is givento illustrate the effectiveness of the proposed method.