Adaptive Fuzzy Output Feedback Dynamic Surface Control Of Nonlinear Systems

In recent years,because of the improvement of the scientific technology,the actual systems have become more and more complicated.A lot of novel control theories have been set up as a result of control objectives getting increasingly complicated.With respect to the control method for sophisticated nonlinear systems,fuzzy control method is one of the most effective methods.With its fast development,fuzzy control means are more and more general in actual places.Within the available methods,FLS is getting a great deal of attention.The major merits of this method is that in the process of controller designing and stability analysis,it can integrate the previous knowledge from experts.Nevertheless,the major drawback is that because of the feature of the method,there has been a little systematic stability analysis and it has been a obstacle in the investigation.Chapters of this thesis are shown below.1.The problem of the adaptive fuzzy tracking control via output feedback for a class of uncertain single-input and single-output strict feedback nonlinear systems with unknown time-delay functions is investigated.Dynamic surface control technique is used to avoid the problem of “explosion of complexity,” which is caused by repeated differentiation of certain nonlinear functions in the backstepping design process.In addition,the fuzzy logic systems are utilized to approximate the unknown nonlinear functions.The designed controller can guarantee all the signals in the closed-loop system to be semi-globally uniformly ultimately bounded and the tracking error to converge to a small neighborhood of the origin.Simulations are provided to demonstrate the effectiveness of the proposed methods.2.For this part,an adaptive fuzzy decentralized output feedback control design is presented for a class of interconnected nonlinear pure-feedback systems.The considered nonlinear systems contain unknown nonlinear uncertainties and the states are not necessary to be measured directly.Fuzzy logic systems are employed to approximate the unknown nonlinear functions,and then a fuzzy state observer is designed and the estimations of the immeasurable state variables are obtained.Based on the adaptive backstepping dynamic surface control design technique,an adaptive fuzzy decentralized output feedback control scheme is developed.It is proved that all the variables of the resulting close-loop system are semi-globally uniformly ultimately bounded,and also that the observer ad tracking errors are guaranteed to converge to a small neighborhood of the origin.Some simulation results and comparisons with the existing results are provided to illustrate the effectiveness and merits of the proposed approach.3.For this part,an adaptive control technique is developed for a class of uncertain nonlinear parametric systems.The considered systems can be viewed as a class of nonlinear pure-feedback systems and the full state constraints are strictly required in the systems.One remarkable advantage is that only less adjustable parameters are used in the design.This advantage is first to take into account the pure-feedback systems with the full state constraints.The characteristics of the considered systems will lead to a difficult task for designing a stable controller.To this end,the mean value theorem is employed to transform the pure-feedback systems to a strict-feedback structure but nonaffine terms still exist.For the transformed systems,a novel recursive design procedure is constructed to remove the difficulties for avoiding non-affine terms and guarantee that the full state constraints are not violated by introducing barrier Lyapunov function with the error variables.Moreover,it is proved that all the signals in the close-loop system are global uniformly bounded and the tracking error is remained in a bounded compact set.Two simulation studies are worked out to show the effectiveness of the proposed approach.