Dynamic modeling and RBF neural network compensation control for space flexible manipulator with an underactuated hand

Abstract

In space operation, flexible manipulators and gripper mechanisms have been widely used because of light weight and flexibility. However, the vibration caused by slender structures in manipulators and the parameter perturbation caused by the uncertainty derived from grasping mass variation cannot be ignored. The existence of vibration and parameter perturbation makes the rotation control of flexible manipulators difficult, which seriously affects the operation accuracy of manipulators. What’s more, the complex dynamic coupling brings great challenges to the dynamics modeling and vibration analysis. To solve this problem, this paper takes the space flexible manipulator with an underactuated hand (SFMUH) as the research object. The dynamics model considering flexibility, multiple nonlinear elements and disturbance torque is established by the assumed modal method (AMM) and Hamilton's principle. A dynamic modeling simplification method is proposed by analyzing the nonlinear terms. What's more, a sliding mode control (SMC) method combined with the radial basis function (RBF) neural network compensation is proposed. Besides, the control law is designed using a saturation function in the control method to weaken the chatter phenomenon. With the help of neural networks to identify the uncertainty composition in the SFMUH, the tracking accuracy is improved. The results of ground control experiments verify the advantages of the control method for vibration suppression of the SFMUH.