Dynamic modeling and tracking control strategy for flexible telescopic space manipulator based on neural network compensation

Flexible telescopic space manipulator (FTSM) is playing an increasingly important role in assisting astronauts to accomplish space missions. The telescopic structure leads to time-varying dynamical parameters of the FTSM, thereby reducing the tracking control accuracy. At the same time, the lightweight and large length-diameter ratios are very likely to cause the vibration phenomenon of FTSM during the motion. To solve the above problems, in the dynamics modeling, this paper uses the assumed modal method (AMM) and Lagrange principle to establish the dynamics model considering two-dimensional deformation and disturbance torque. Furthermore, the dynamics models are simplified and derived by ignoring the nonlinear terms (INTs). Besides, the influence of the simplified dynamics models on the accuracy of FTSM’s deformation is analyzed by simulation. It is found that the INTs simplified dynamics model has higher modeling accuracy. Based on the INTs simplification model, the control law is designed. More importantly, the RBF neural network is used to identify and compensate for the time-varying terms and disturbance torque in the FTSM. Then the sliding mode control strategy is proposed by the hyperbolic tangent function as the approximation rate. Finally, the effectiveness of the RBF neural network compensated sliding mode control strategy is illustrated by simulation and ground control experiments of the FTSM.