| As the actual control system always has the uncertainty,this kind of uncertainty generally falls into two kinds:One kind is the external uncertainty,like from the external disturbance;The other kind is to control the system internal uncertainty,Such as the estimation error of the measured parameters and the unmodeled dynamics of the controlled object.If you ignore these uncertainties when designing your system,you will inevitably deteriorate the quality of the system.Therefore,in the design must take full account of these uncertainties,so that the system does not have a significant impact on the dynamic performance,which requires the system is robust.The main contents of this paper are as follows:Firstly,a class of single-input/single-output stochastic nonlinear systems with strict feedback are used to study the adaptive neural network stabilization problem based on backstepping method.In the controller design,RBF neural network is used to approximate approximate unknown nonlinear function and backstepping method is used to design The system's adaptive neural network controller ensures that all states of the closed-loop system are probabilistically bounded.Simulation results prove the effectiveness of the proposed method.A new adaptive neural network robust control method is proposed for a class of stochastic nonlinear large-scale systems.Based on Backstepping technique,this method combines stochastic differential theory,Lyapunov stability theory,large-system theory and adaptive neural Network control theory,the robust controller design method of the stochastic nonlinear interconnected large-scale system is studied.The design scheme of the controller for the stochastic nonlinear interconnected large-scale system is given.The obtained controller can guarantee that every The states of all subsystems are probability-bounded and have interference rejection performance.Simulation results prove the effectiveness of the proposed method. |
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