Research On Mobile Manipulators Trajectory Tracking Control With Uncertainty

In view of the advantages of mobile platform's large workspace and the dexterity of industrial robotic arm, the mobile manipulator system has been widely used in industrial production, national defense military and family services. In the process of mobile manipulator control, model parameter uncertainties and external disturbances, torque saturation problem always affect the mobile manipulator control property. In view of the above factors, designing a trajectory tracking controller to satisfy the requirement of high precision and high speed is the emphasis and difficulty in the research of mobile manipulator.Firstly, the kinematics and dynamics model of six degrees of freedom nonholonomic mobile manipulator are established uniformly. According to torque saturation problem, an adaptive neural network combing backstepping control with sliding mode control is proposed. Neural network is used to compensate the dynamic uncertainty of system, and the neural network approximation error and torque saturation part are compensated by an parameter adaptive compensation.Secondly, Given that it is difficult to obtain the velocity and acceleration of the manipulator joints, an Backstepping dynamic surface controller based on fuzzy wavelet network observer is proposed. Considering the nonlinear dynamic characteristics of the drive motor, an control method of the output voltage easier to be realized. Fuzzy wavelet networks are used to compensate the dynamic uncertainty of mobile manipulators and drive motor model. To estimate joint speed, an observer is designed, and dynamic surface control is applied to avoid the problems of large amount of calculation.Finally, in order to avoid complex trajectory planning before trajectory tracking in the joint space and make it simple and efficient, a mobile manipulator trajectory tracking controller is proposed in task space. Online modeling and adaptive compensation of kinematics uncertainties and dynamic uncertainty is completed respectively with the RBF neural network. An backstepping controller based on dynamic surface control is designed, to achieve end-effector position tracking and don't need to measure the end-effector speed signal.Based on lyapunov stability theorem, The stabilities of the proposed algorithms are proved, and the effectiveness of the algorithm is verified by simulationexperiment. Meanwhile, the advantages and disadvantages of the algorithm are analyzed.