Adaptive Tracking Control For Nonlinear Stochastic Feedback Systems

The system is affected by ambient temperature,resistance and capacitance,external vibration,noise and other disturbances,thus affecting the stability of the system.Therefore,for stochastic nonlinear systems,It is of great scientific and practical significance to study the design of system stability control based on FLSs or NNs.In the practice of automation engineering,the nonlinear function in the system is approximated by fuzzy logic systems(FLSs)and neural networks(NNs)is an effective and feasible methods.On the one hand,because FLSs can approach any unknown nonlinear function in the system,the existing results are based on the FLSs to do a very extensive study of the stability of the system.However,the existing results is not given for simultaneous time-varying delay and dead zone in stochastic nonlinear systems.On the other hand,NNs has been widely studied and applied in control system modeling,control scheme design,system parameter identification and system pattern recognition.In the field of control theory,the results show that NNs also have the approximation characteristic of approaching arbitrary nonlinear continuous functions.However,the existing results have not yet used the NNs method to give a suitable stability control strategy for any switched stochastic nonlinear system with unknown direction hysteresis.Two kinds of adaptive fuzzy tracking control schemes are proposed for stochastic feedback systems with time-varying delay and dead zone and stochastic non-strict feedback switching system with unknown hysteresis.The main work and research results of this thesis are summarized as follows:Firstly,an adaptive tracking control method is proposed for stochastic feedback systems with time-varying delay and dead zone by back-stepping techniques and fuzzy logic systems.In this thesis,an adaptive fuzzy controller is designed by combining Lyapunov stability theory and back-stepping technique.By means of the mean value theorem,the complex problem of design controller in pure feedback structure is solved.The fuzzy logic system can approximate the unknown smooth nonlinear functions in the system.The designed controller can ensure that the input of the closed-loop system to the state is actually stable,and all the control signals in the system are semi-globally uniformly ultimately bounded.The tracking error of the system converges to a small neighborhood of the origin.In order to reduce the operation cost,the controller is designed to reduce the number of adaptive parameters.Thus the tracking control schemes greatly improve the system operation efficiency.Then,an adaptive fuzzy tracking control scheme is designed for stochastic non-strict feedback switching system with unknown hysteresis.The nonlinear system studied has the characteristics of backlash-like hysteresis and uncertain hysteresis direction.By using the method of variable separation,the state variables can be decomposed to a simple smooth function under arbitrary switching conditions.The neural network is used to approximate the unknown virtual control signal.The control system tracking error converges to a compact set by designing suitable parameter adaptive law and actual control signal.Compared with the traditional adaptive neural network control method,the control scheme proposed in this thesis can effectively reduce the adaptive parameters of online adjustment of neural network,so as to reduce the computational burden and improve the operating efficiency of the system.