Research On Trajectory Tracking Control Of Wheeled Autonomous Mobile Robot

With the rise of factory intelligent manufacturing models,mobile robots have begun to play an important role in many areas of human society,especially in the industrial field with important application requirements.Therefore,the research on autonomous mobile robot technology has broad application prospects,and trajectory tracking control is one of the important research hotspots and difficulties in the field of autonomous mobile robots.Autonomous mobile robot systems are inherently sensitive to highly time-varying parameter uncertainties,un-modeled dynamics and external disturbances.This paper focuses on this problem to carry out research on trajectory tracking control of mobile robots.The main research work of this article is as follows:1.Aiming at the problem that the use of sliding mode control in an autonomous mobile robot system will cause adverse effects on the system due to chattering,improved super-twisting sliding mode trajectory tracking control strategy is proposed.This control strategy is designed based on a mobile robot dynamically,while effectively eliminating chattering,it also accelerates the response speed of the system.2.To solve the problem of parameter uncertainty in the super-twisting sliding mode control,a robust adaptive law is designed to estimate the uncertain parameters in the super twist sliding mode control on-line,which improves the tracking accuracy of autonomous mobile machine and the ability to track different tracks.3.The complex operational environment of the autonomous mobile robot is studied.Considering the uncertainty of the localization of the autonomous mobile robot,a high gain observer is designed to estimate the pose and velocity signals during the operation of the autonomous mobile robot,and the output feedback control of the system is realized.In addition,considering that the autonomous mobile robot is easily affected by various unknown disturbances,disturbance observer is designed to estimate all kinds of disturbances of the mobile robot and human in real time,which enhances the robustness of the system.4.In view of the extremely difficult to obtain accurate mathematical models of autonomous mobile robots in the real environment,using the self-learning ability of the neural network,combining the error back propagation principle and the gradient descent method,a BP neural network is designed to identify the dynamic function of the mobile robot online,avoiding the problem of reduced control performance caused by inaccurate modeling effectively improves the control accuracy and robustness of the system.A series of MATLAB simulation experiments verify that the improved adaptive super-twisting sliding mode controller based on neural network and two kinds of state observers has the advantages of fast response speed,strong anti-interference ability,strong robustness,high control accuracy and still effective for different expected trajectories.