一种应用于自主车辆的新型自适应模糊控制器

Adaptive control for a class of nonlinear systems is discussed in this paper,We use fuzzy systems to approximate the ideal optimal corntroller by adjusting the parameters of fuzzy systems,In order to ture these parameters,linear relationship between approximation error and parameters is established first.Then we desing the adaptive laws of these parameters based on Lyapunov synthesis approach.The advantage of our method is that we can tune not only the parameters of the consequences of fuzzy rules,but also the parameters of the membership functions.As a result,a stable and more flexible controller is achieved.The performance of the adaptive scheme is demonstrated through the longitudinal vehicle control.     

Covariance control with observed-state feedback gains for continuous nonlinear systems using T-S fuzzy models

The design problem of state variance constrained control for stochastic systems has received rather extensive attention in recent years. This paper solves the state variance constrained controller design problem by using the covariance control theory, with observed-state feedback gains for continuous Takagi-Sugeno (TS) fuzzy models. By incorporating the technique of state estimation into the practical covariance control theory, a variance constrained control methodology is developed for the continuous TS fuzzy models. Finally, a numerical example is shown to demonstrate the efficiency and applicability of the proposed approach.     

A Novel Adaptive Fuzzy Controller for Application in Autonomous Vehicles

１INTRODUCTIONMostofthecurrentresearchonadaptivefuzzycontrolonlytunestheparametersoftheconsequencesoffuzzyrules．Thismaycausetheapproximationpropertyoffuzzysystemsnottobegood熏andaffecttheperformanceofthecon鄄troller．Aimingatthisproblem熏wehopetotuneallparam鄄etersoffuzzyrules．Inordertotunetheseparameters熏lin鄄earrelationshipbetweenapproximationerrorandallparam鄄etersoffuzzyrulesisestablishedfirst．ThenwedesigntheadaptivelawsoftheseparametersbasedonLyapunovsyn鄄thesisapproach．Theadva…     

Robust covariance control for discrete system by Takagi-Sugeno fuzzy controllers

Variances of the system states or outputs often play vital roles in the problem for performance requirements of many stochastic control systems. For linear stochastic systems, the covariance control technique has been applied to deal with the variance constrained design problem. This paper extends this technique to a class of discrete-time nonlinear perturbed stochastic systems, which are modeled by the Takagi-Sugeno (TS) fuzzy systems. By fuzzy IF-THEN rules, which represent local linear input-output relations, the nonlinear systems can be described by TS fuzzy models. According to the parallel distributed compensation (PDC) concept, the discrete-time nonlinear perturbed stochastic systems can be driven by the linear feedback gains. The purpose of this paper is to provide a method to design an output feedback fuzzy controller, which is based on the upper bound state covariance control technique and PDC concept, for the discrete-time perturbed stochastic systems using TS fuzzy models.