Adaptive Intelligent Command Filtered Control Of Nonlinear Systems And Its Application

Because practical engineering systems usually have essential nonlinearity,the research and analysis of nonlinear systems not only has important theoretical significance,but also has important practical application value.Adaptive intelligent control method has become one of the important methods to solve the control design problem of complex nonlinear systems.Based on the research of adaptive control at home and abroad.In this thesis,for several nonlinear systems,the controller design method and the convergence and stability of the closed-loop system are investigated by combining the backstepping technology and the adaptive intelligent control method.The main content of this paper is divided into the following three parts:1.The problem of finite-time adaptive fuzzy command filtered control is investigated for non-strict-feedback nonlinear systems.The finite-time tracking control scheme via utilizing the command filter backstepping control technique and the adaptive fuzzy control approach is developed.The presented scheme ensures that all the closed-loop variables are semi-global practical finite-time stable and the tracking error goes into an adjustable neighborhood around the origin in a finite time.Simulation results prove the availability of the proposed scheme.2.An adaptive neural command filtered tracking control method is presented for nonlinear systems with multiple actuator constraints.By combining the command filter technique with the backstepping design algorithm,an adaptive neural tracking backstepping control scheme is developed with the help of neural network approximation.The designed strategy ensures the boundedness of all control variables in the closed-loop system and the output signal can follow the given desired trajectory as close as possible.Simulations illustrate effectiveness of the presented strategy.3.An adaptive neural network command filtered tracking control method is proposed for a flexible manipulator model with input dead-zone.An adaptive neural network controller is designed by utilizing the approximation property of neural network and the backstepping design,which guarantees that all variables of the closed-loop system remain bounded and the outputsignal eventually follows the given desired trajectory.Simulation results are given to testify the availability of the designed scheme.