Intelligent Adaptive Control And Stability Analysis For Uncertain Strict-Feedback Nonlinear Systems

In the past two decades,adaptive control has been widely concerned by scholars because of its successful application in the field of industrial system.In general,most industrial systems can be described as nonlinear systems.Due to the existence of nonlinear uncertainties in nonlinear systems,it is difficult to study the control prob-lems of nonlinear systems.With the development of fuzzy logic system and neural network,the above problem can be solved.A large number of literatures show that fuzzy logic system and neural network can approximate an unknown nonlinear func-tion with arbitrary precision by properly adjusting parameters.Compared with the traditional control method,adaptive control can not only control a known system,but also can control an unknown system.And the adaptive control can adjust the parameters of the controller according to the requirements of performance index,so that the whole system can automatically run in the optimal or suboptimal working state.Firstly,the dissertation studies the design of an adaptive controller for a class of uncertain strict-feedback nonlinear systems with unknown disturbance,which not only satisfy the required performance and stability,but also solve the disturbance problem of the system.Secondly,based on the generalized fuzzy hyperbolic model,the adaptive control problem of switched nonlinear systems is studied.Finally,sim-ulation examples are given to verify the validity and applicability of the proposed method and results.The main contributions of this dissertation can be summarized as follows:(1)An adaptive dynamic surface control is investigated for a class of uncertain nonlinear systems with unknown bounded disturbances in strict-feedback for-m.Dynamic surface control technique is connected with radial basis function neural networks(RBFNNs),the above control framework can avoid the ex-plosion problem of complexity.The composite adaptive laws are constructed by prediction error and compensated tracking error between system state and serial-parallel estimation model for NN weights updating.Using Lyapunov techniques,the uniformly ultimate boundedness stability of all the signals in the closed-loop systems is guaranteed.(2)A hybrid adaptive output feedback fault-tolerant control is investigated for a class of uncertain nonlinear systems with unmeasured states.The generalized fuzzy hyperbolic model is used to approximate the unknown nonlinear func-tions of the systems,and the fuzzy state estimator(FSE)is established for estimating the unmeasured states.Based on the backstepping and dynam-ic surface control technique,a novel adaptive control method is proposed by introducing the prediction errors between FSE and serial-parallel estimation model.It is proved that all the variables of the closed-loop are semi-globally uniformly ultimately bounded by Lyapunov approach,and the tracking errors can converge to a small neighborhood.(3)A synthetic adaptive fuzzy tracking control method is studied for a class of multi-input multi-output(MIMO)uncertain nonlinear systems with time-varying disturbances.The unknown nonlinear functions are approximated by employing generalized fuzzy hyperbolic model.A synthetic adaptive fuzzy control is designed by dynamic surface control,serial-parallel estimation mod-el and the disturbance observer.Then by using Lyapunov stability theory,it is guaranteed that all the variables of the closed-loop systems are semi-globally uniformly ultimately bounded(SGUUB).Finally,a satisfactory tracking per-formance with faster and higher accuracy can be obtained by adjusting the parameters appropriately.(4)An effective method for designing an adaptive controller for a class of uncertain strict-feedback nonlinear systems with unknown bounded disturbances.Dur-ing the controller design process,all of the unknown functions are accumulated at the intermediate steps until the last step.In addition,only one generalized fuzzy hyperbolic model is used to approximate the total unknown nonlinear functions for the whole systems.Thus,only the actual control law needs to be implemented and one adaptive law is proposed for the overall controller design process.As a result,the controller design is much simpler and the computa-tional burden is reduced greatly.Using Lyapunov techniques,all the signals for the closed-loop system is uniformly ultimately bounded stability.(5)A novel adaptive fuzzy tracking control is investigated for a category of MIMO switched uncertain nonlinear systems with strict-feedback form.The unknown nonlinear function and switching signals with average dwell time are contained in the systems.The control method is designed by generalized fuzzy hyper-bolic model and backstepping technique.Through adding a first-order filter into conventional backstepping method,the phenomenon of "explosion of complexity" is eliminated.In the controller design process of each subsys-tem,only one adaptive parameter is needed to adjust online.Compared with other existing control methods,the proposed control design is more simple and calculated amount is greatly decreased.It is guaranteed that the whole closed-loop system is stable by using Lyapunov function and average dwell time methods.