Adaptive Finite Time Control For A Class Of Nonlinear Systems Based On Prescribed Performance

In real life,due to different requirements and restrictions in different scenarios,many physical models cannot meet the conditions of linear systems,and a large part of the system is nonlinear.Therefore,it is very necessary to study nonlinear systems,which can better solve practical problems,but also can describe the physical model more humanized,and play a good role in improving system performance.Based on this,this thesis studies a class of strict feedback system,taking this as the entry point,combining with a variety of control methods,under the premise of ensuring the stability of the system,the control scheme is studied in detail and verified.The main work of this thesis is as follows:(1)An adaptive control scheme of preset performance based on output feedback is designed for a nonlinear system with unmeasurable system state.The design of the system is roughly divided into several parts.Firstly,to eliminate the concern of unknown items in the system,we design a new filter to estimate the actual value,so as to get a more appropriate data,and solve the problem of unmeasurable system as far as possible.The second step is to make full use of the advantages of the preset performance and introduce an error function to make the error value of the system perfectly within the range of the constraint function,so as to improve the performance of the system.Finally,the finite time control method is used to make the system reach the stable state in the finite time.(2)For a kind of strict feedback system with external interference,on the basis of the above,the event triggering control is added,and the finite time and neural network control are combined to ensure the performance index and stability of the system to a great extent.On this basis,combined with the event triggering mechanism of fixed threshold strategy,the controller designed in this thesis can be well applied in real life,and then save network resources.(3)The above methods are verified by matlab experiments.We adopted a mechanical arm for model simulation.Through the simulation waveform,we can see that the output signal of the system can accurately track the reference signal,the estimation of the filter is also more accurate,the system error can perfectly fall within the constraint function,and the system performance is obviously improved under the support of various methods.Finally,the event triggering mechanism is used to verify the effectiveness of the controller designed in this thesis.