Adaptive Neural Network Control Of Non-strict Feedback Nonlinear Systems With Input Delays

In this thesis,an adaptive neural network control is performed on the nonlinear system with input delay.According to the adaptive neural network control method,backstepping design idea and Lyapunov stability theory,the state feedback controller is first designed via state feedback for the systems with state delays and input delay.And then,observer-based output feedback control strategy is further developed for the systems with input delay.This thesis mainly addresses control design and stability analysis for the nonlinear non-strict feedback system with input delay.According to the methods of integral transformation and the introduction of auxiliary system respectively,two kinds control schemes have been developed for the non-strict feedback nonlinear systems with input delay.The specific research contents are as follows:1.Chapter 1 introduces the research background,research status of the time delayed systems,and also introduces the research content of this thesis.Chapter 2 introduces the basic knowledge related to nonlinear systems,the application of neural network systems in approximating nonlinear functions,the principle of backstepping method,and the Lyapunov stability theory.2.Chapter 3 studies a class of non-strict feedback nonlinear systems with state delays and input delays.With the feature of the basis vector functions in neural networks,backstepping approach is applied to non-strict feedback systems for control design,and through the combination of integral transformation and adaptive neural network.An adaptive neural control scheme is presented based on backstpping method.The designed controller ensures that all signals in the closed-loop system remain bounded,and the tracking error converges the neighborhood of the origin.3.Chapter 4 studies the non-strictly feedback nonlinear system with both state delay and input delay.A new auxiliary system is introduced to solve the impact of input delay,and the interference of state delay is handled through the integral Lyapunov function.With the help of the neural network function can approximate all unknown nonlinear functions.Combining the backstepping method and the adaptive control method,an adaptive neural network controller is designed.4.Chapter 5 focuses on observer-based adaptive neural tracking control of non-strict feedback systems with input delay.The observer is first set up to estimate the unknown system states.The estimation states will be used for controller design.The convex combination technique is used to transform the nonlinear matrix inequality into a set of linear matrix inequalities in order to solve the observer gain matrix.Integral transformation is employed to deal with the input delay.Then,adaptive neural control approach and backstepping are put together to construct tracking controller.The proposed adaptive neural controller is shown to ensure that all the closed-loop signals are bounded and the tracking error converges to a small neighborhood of the origin.5.Chapter 6 addresses adaptive neural output feedback control for a class of nonlinear non-strict feedback systems with input delay.First,a state observer is constructed to estimate the unmeasurable state variables,and then the estimation states are utilized to design the controller.An auxiliary system is introduced to deal with the input delay.By combining adaptive neural control approach with backstepping,an adaptive neural tracking controller is proposed.It is shown that the suggested controller ensures the boundedness of all the closed-loop signals and the achievement of good tracking performance.6.Chapter 7 provides a summary and outlook.It summarizes the works in the thesis,and points out the limitations of the current research and the problems to be solved current research.