Adaptive Neural Controller Design Of Stochastic Nonlinear Interconnected Systems

In recent years,interconnected stochastic systems are hot research topic in the field of control,due to their complex properties and wide applications in actual engineering.Although interconnected stochastic systems have attracted the interest of researchers from different background,there are still many control problems worthy of studying for such systems.This thesis is targeted towards several classes of interconnected stochastic nonlinear systems.By employing the backstepping technology and the adaptive neural network(NN)control method,the correlative controllers of these systems are designed,and the stability and the convergence problems of the corresponding closed-loop systems are analyzed.The main results of this paper are as follows:For the decentralized adaptive approximation-based neural output-feedback control issue for a kind of uncertain switched stochastic interconnected nonlinear systems.Firstly,the switched state observer is designed to estimate the immeasurable states and the dynamic surface control(DSC)technique is adopted to ensure the computation burden is greatly reduced.Then,neural network(NN)combined with adaptive backstepping method are applied to model the unknown nonlinear functions of the switched stochastic nonlinear interconnected system.Moreover,the proposed controllers guarantee the semi-global bounded of the resulting closed-loop system under any switching laws.Finally,simulation results show the effectiveness of the proposed control scheme.In this paper,an adaptive neural-network-based dynamic surface control(DSC)method is proposed for a class of stochastic interconnected nonlinear non-strict-feedback systems with unmeasurable states and dead zone input.In the design procedure,the state observer is first constructed,and then with the help of a variable separation technique,the design difficulty caused by the nonstrict-feedback structure is solved,and an appropriate state observer is constructed to estimate the unmeasured state variables of the stochastic interconnected system.Then radial basis function(RBF)neural networks(NNS)combined with adaptive backstepping technique are applied to model the unknown nonlinear system functions of the stochastic interconnected system.Furthermore,the proposed controllers guarantee that the closed-loop stochastic interconnected system is semi-globally bounded stable in probability.In the end,two simulation examples are provided to show the effectiveness and practicability of the proposed control scheme.Although the adaptive control design for several types of interconnected stochastic nonlinear systems are studied in this dissertation,the study of interconnected stochastic systems are still preliminary.There are many relevant problems can be explored for interconnected stochastic systems,such as interconnected system with unmodeled dynamics control,constraint control of nonlinear interconnected systems,control design of pure-feedback nonlinear interconnected systems,and so on.