Adaptive Finite-time Tracking Control For Pure-feedback Systems Based On Event-triggered

With the continuous development of network science and technology,network control is widely used in practical systems with its advantages of high efficiency and low cost.But in the classical network system,the control signal acts on the system at any time,and the traditional periodic sampling wastes a lot of network resources due to frequent sampling and large amount of data transmission.Therefore,in the case of limited communication capacity of the system,an event-triggered control strategy appears.The controller signal will only send the actuator when meeting the eventtrigger conditions,which saves a lot of network resources.Meanwhile,In practical systems,pure-feedback nonlinear systems are more general,and the required controller must ensure that the closed-loop system meets the state constraints when controlling it,which makes the design of system controller difficult to meet the control requirements.Therefore,based on the event-triggered strategy and backstepping method,this thesis studies the finite-time adaptive control problem of a class of pure feedback systems with full-state constraints in detail.The main work of this thesis is as follows:(1)An event-triggered control scheme is proposed for tracking control of a class of pure-feedback nonlinear systems.First,the mean value theorem is used to transform the input non-affine pure-feedback system into a strict-feedback system;Secondly,the fuzzy logic system is introduced to approximate the unknown nonlinear functions in the design process;Finally,combining the relative threshold event-triggered control strategy with Backstepping method,an adaptive controller based on event-triggered is designed.The proposed control scheme can effectively reduce the communication burden between the controller and the actuator,and all signals in the closed-loop system are uniformly bounded.(2)For pure-feedback nonlinear systems with full state constraints,a finite-time control scheme based on a relative threshold event-triggered strategy is proposed.The obstacle Lyapunov function is used to deal with the state constraint problem,and the radial basis function neural network is introduced to approximate the unknown nonlinear functions in the design process;The controller of the system is designed by combining the relative threshold event-triggered control strategy,finite time stability theory and Backstepping technology.The proposed control scheme ensures that all signals in the closed loop are bounded in finite-time and all state constraints are satisfied.(3)According to the above control scheme,simulation analysis is carried out.The results show that the event-triggered control strategy effectively reduces the waste of network resources and avoids Zeno phenomenon.The designed controller has good tracking performance,the tracking error can converge rapidly in a finite time and all states of the system do not violate the set constraint boundary.