Predefined-time Intelligence Tracking Control Algorithm For Unknown Nonlinear Systems

Due to the important application in military,aerospace,agriculture,industry and other fields,automation and intelligent control theories have been paid more attention by all countries.However,with the continuous improvement of the performance requirements of control systems in all walks of life,the traditional linear feedback control has been difficult to meet all kinds of practical control requirements.The reason is that most practical control systems are nonlinear in nature,such as mass-spring-damper systems,power systems,robot systems and so on.In addition,many real-time application platforms require strict time scheduling to limit the response time of the controlled systems,for example,a robot must arrive at a desired location within each specified time to grab an object moving on a conveyor belt,and the spacecraft needs to dock within a specified time.Therefore,the predefined-time intelligent control problem of nonlinear systems has been a hot research topic at home and abroad.Based on the above problems,this thesis focuses on the predefined-time adaptive intelligent tracking control of unknown nonlinear systems based on further understanding of nonlinear system theory and intelligent control technology.The research contents of this thesis are as follows:(1)The predefined-time adaptive intelligent tracking control algorithm for a class of non-strict-feedback nonlinear systems subjected to unknown external disturbances is proposed.Compared with the finite time or fixed time control methods in the relevant literature,the predefined-time adaptive intelligent control algorithm proposed in this paper has the following outstanding advantage: the algorithm can specify the convergence time of the systems in advance according to the users requirements.To get the desired predefined-time controller,an important structural property of RBF NNs is firstly introduced so that the difficulty caused from the non-strict-feedback structure can be easily solved.Then,under the proposed predefined-time controller,it is strictly proved that all signals of the closed-loop system are bounded,and the zero tracking error is achieved within the predefined time.Finally,an actual example is given to verify the feasibility of the proposed predefined-time intelligent tracking control scheme.(2)The neural-network-based global adaptive intelligent tracking control algorithm for disturbed pure-feedback nonlinear systems is proposed to achieve zero tracking error in a predefined time.Different from the traditional works which only solve the semi-global bounded tracking problem for pure-feedback systems,this work not only achieves that the tracking error globally converges to zero but also guarantees that the convergence time can be predefined according to the actual needs.In the design process,in order to get the desired predefined-time controller,first of all a mild bound assumption for non-affine functions is skillfully proposed so that the design difficulty caused by the structure of pure-feedback can be easily solved.Then,we apply the property of RBF NNs and Young’s inequality to derive the unknown upper bound of the term which contains the nonlinear function as well as external disturbances,and design adaptive parameters to estimate the derived upper and robust control gain.A predefined-time neural network controller is designed in the neural network attraction domain,and a predefined-time robust controller is designed outside the neural network attraction domain.Finally,an actual example is proposed to verify the feasibility of the proposed globally predefined-time intelligent tracking control scheme.