Stochastic Nonlinear Multi-input Multi-output Adaptive Neural Network Control Delay Systems

The nonlinear system is widely used in the modern control. The uncertainty in systems brings difficulty to the analysis and integration of systems. In addition, stochastic disturbance exists in industrial systems. So the stochastic nonlinear system is important in the research of system control.The research of stochastic nonlinear single-input/single-output system has made many achivements, but it is rare on the research of the stochastic nonlinear multi-input/multi-output (MIMO) system. In this thesis, with neural network and adaptive control, we propose a new method to ensure the stability and tracking performance in systems. The main content is listed as follows:First., we will consider a class of nonlinear multi-input/multi-output strict-feedback stochastic systems. Neural networks and backstepping are used to design controller. We will construct a class of state feedback controller,and prove the stability of Lyapunov in systems. Finally,we use MATLAB in simulation.Second, we will consider a class of nonlinear multi-input/multi-output strict-feedback stochastic time-delay systems. Neural networks and backstepping are used to design controller., We will develop a new adaptive neural network control scheme. It only contains less adaptive parameter. We prove the stability of Lyapunov in systems. Finally,we use MATLAB in simulation.