基于径向基函数神经网络的空间漂浮机械臂装配控制

A compensation control method based on a radial basis function neural network (RBFNN) is proposed. RBFNN focuses on the problem of the accurate control of space floating manipulators in orbit assembly when the dynamic model is inaccurate and the quality characteristics of the module to be assembled are unknown. First, the configuration design and dynamic model description of the space assembly manipulator are given. Then, a compensation control rate based on the principle of RBFNN is designed, and stability and convergence are analyzed. Finally, the RBFNN-based adaptive assembly control law of space floating base manipulators is derived. Simulation cases of in-orbit assembly under different working conditions show that the control method based on the RBFNN compensation principle can control the manipulator end effector to track the desired trajectory quickly and accurately under the action of heavy load with unknown characteristics. However, in the absence of compensation, the performance of the controller declines seriously or even fails. The method proposed in this paper has high engineering practical value and can provide an effective reference for future space assembly tasks.