Fixed Time Adaptive Control For Nonlinear Systems Based On Event Triggering

For the control of nonlinear systems,required them reach a stable state in a finite time,so the finite time control is studied,but the convergence time of the finite time control is related to the initial state of the system.Fixed time control is designed to eliminate this correlation.The convergence time of fixed time control has no connection with the initial state of the system.When the nonlinear system with large initial value is controlled,the fixed time control can effectively reduce the stable convergence time.The fixed time control problem of a class of strict feedback nonlinear systems with output constraints is studied in this these;By Utilizing the characteristics of neural network and the advantages of event triggered control.The adaptive control problem of the system is discussed by combining with the event triggering method and neural network theory.The main work of this these are as follows:(1)Fixed time control for a class of strict feedback nonlinear systems with external disturbances and output constraints is studied.Considering the output constraints of the system,the log barrier Lyapunov function is designed,and constrain the output of the system by using its characteristics;The fixed time controller is designed by combining the backstepping method and the fixed time control,and the stability of the system is analyzed.The controller ensures the convergence of the system in a fixed time,and ensures tall closed-loop signals are bounded,and the convergence time of the system has no connection with the initial value of the system,which reduces the convergence time and improves the stability of the system.(2)For a class of strict feedback nonlinear systems,a fixed-time neural network adaptive control method based on event-triggered nonlinear systems is designed.Firstly,the unknown function of the system is approximated by neural network;Then,the tracking signal can be tracked to the reference signal in a fixed time by designing Lyapunov function,virtual control law and adaptive control law.According to the features of the system,a new event triggering mechanism is designed to reduce the waste of network resources,reduced the number of information samples;Finally,the Lyapunov stability theory is used to prove that all signals in the closed-loop system are bounded.(3)According to the control method proposed above,the computer simulation verification analysis is carried in combination with a class of strict feedback nonlinear system,and the effectiveness of the method proposed in this these is verified.The simulation results show that the nonlinear system can still converge in a fixed time with output constraints,and the convergence time does not change with the initial value;Under the combination of event triggered control and neural network adaptive control,the system can accurately and quickly track the reference signal,reduce the number of controller triggers,and save network resources.