Sliding Mode Tracking Control Theory ANG Application For Nonlinear Uncertain System

Nonlinear uncertain systems control system is often encountered in the actual production to solve control problems of such a system has high practical needs and theoretical significance. Sliding Mode Control for its good robustness in solving control problems showing strong vitality, but the actual project, sliding mode control is often accompanied by excessive gain switching, buffeting and other issues. In order to solve these problems and achieve better tracking control effect, the paper studies tracking control of nonlinear uncertain systems, sliding mode control and adaptive inverse control method, RBF neural network control methods combining tracking controller design, and the tracking control algorithm in practical engineering, vehicles and other roadway split nonlinear systems, simulation results show the effectiveness of the algorithm. Contents of this paper the following aspects:Firstly, reviewed research status uncertain nonlinear system analyzes the concept of sliding mode control theory, design ideas, there is chattering problems and solutions to the problem, and introduces the inversion control method and RBF neural network control method, for the following research lay the foundation.Secondly, the advantages and disadvantages terminal sliding mode control and non-singular terminal sliding mode control based on this paper proposes a new power reaching law of sliding mode control, which addresses the problem of singular terminal sliding presence and improve the speed of convergence of the system state.Thirdly, for uncertain nonlinear systems, adaptive inverse control method combining sliding mode control, the controller designed to effectively reduce the chattering, and to achieve a good tracking effect. The inversion of thought and non-singular terminal sliding mode tracking controller design function combined with the roadway split truck, and the circular trajectory for the reference trajectory simulation, simulation results show the effectiveness of the controller design.Finally, nonlinear systems with uncertain parameters and interference unknown by the application of RBF network, the unknown function is approximated designed RBF neural network sliding mode controller can track the desired trajectory. Using manipulator dynamics model, the inversion combined with RBF neural network controller is designed to ensure that the system is stable, good simulation results, illustrate the design method has a strong practical significance.These findings enrich the study of control theory for nonlinear uncertain systems. And it provides a theoretical guarantee for the actual application of the project.