Space assistant robots can help astronauts or their assistants perform certain tasks. A ground microgravity simulation environment is built for the space assistant robot AAR-2. The hardware requirements of the ground simulation by the 3-DOF microgravity air flotation platform. An algorithm is designed for this simulation system. By using momentum and RMSprop methods to improve the PID neural network, the challenging problem of strong coupling between system nonlinearity and variables is solved. Firstly, the paper introduces the hardware system and deduces the dynamic model of the system. Then, the algorithm is calculated and simulated. Through simulation, the effectiveness and feasibility of the algorithm are compared and proved. Finally, the control system is simulated by MATLAB/Simulink and compared with other advanced algorithms. The simulation results show that the designed neural network controller can quickly and accurately control the 3-DOF of freedom motion of AAR-2.
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