H_? Control Research For Several Classes Of Pure-Feedback Nonlinear Systems

It is well known that controlled systems are always inevitably affected by external disturbances,which will affect the stability and control accuracy of the system,so it is especially important to consider the stability of the system when it is subject to external disturbances.Therefore,this paper combines backstepping technique,Lyapunov stability theory,neural network control theory with H_? control theory to study the control problem of several classes of pure-feedback nonlinear systems.The main content of this paper is divided into the following three parts:1.An adaptive neural bounded-H_? quantized control approach based on backstepping is proposed for a class of stochastic pure-feedback nonlinear systems with external disturbances and quantized input.In the design process,the non-affine function is first transformed into the affine form by using the mean-theorem.The RBF neural network is used to approximate the packed nonlinear functions,and the constant term in the stability analysis is processed by Gronwall inequality,the H_? controller design for stochastic nonlinear systems is completed.The designed controller can ensure that all the signals of the system are bounded,and the system has the H_? performance for external interferences.A simulation example proves the effectiveness of the proposed scheme.2.For a class of pure-feedback nonlinear stochastic switched large-scale systems with arbitrary switching signals,quantized input and external disturbances,A decentralized adaptive bounded-H_? control scheme is proposed by the concept of bounded-H_?,the common Lyapunov function method and Gronwall inequality.The designed controller can ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded under the condition of arbitrary switching signals,and the system has H_? performance for external disturbances.A simulation experiment demonstrates the feasibility of the proposed control scheme.3.A problem of construction expansion online is studied for a class of nonaffine nonlinear interconnected large-scale systems.That is,a new pure-feedback nonlinear subsystem is added online to expand the original structure system.A robust H_? adaptive neural decentralized connective stabilization method is proposed for expansion online of large-scale systems based on backstepping technique.By using neural network adaptive technique,the decentralized controller of the newly added subsystem was designed under the condition that the decentralized control laws and adaptive laws of the original system were kept to be unchanged.The interconnection parts caused by adding a new subsystem was dealt with in the controller of the new subsystem.An robust H_? adaptive decentralized connective stabilization controller for the newly added subsystem was obtained via H_? control theory.The controller could ensure that all the signals both in the newly added closed-loop nonaffine subsystem and the resultant expanded closed-loop large-scale system were uniformly ultimately connective bounded,and the system has H_? performance for external disturbances.The simulation results were given to verify the effectiveness of the proposed control method.