Model-based hybrid control of combined active–passive vertical zero-gravity system

Abstract

Ground-based full-scale physical experiments on spacecraft have become an essential part of ground simulations. However, achieving a vertical translational degree of freedom for zero-gravity simulation has become a key technological bottleneck for six-degree-of-freedom ground simulations. The combined active–passive vertical zero-gravity system discussed in this paper can achieve zero-gravity simulation in the vertical direction; however, it exhibits deficiencies such as inaccurate modeling, low control precision, and significant control delays. Addressing the issue of inaccurate modeling, this study uses interpretable ODE neural networks to identify the dynamic model of the system. For resolving the problems of poor control precision and significant delays, this study proposes a hybrid control algorithm based on the ODE dynamic model. The controller includes nominal model feedforward, PID feedback, and learning-based feedforward components. Finally, comparative experiments are designed to validate the proposed model-based hybrid control algorithm. Compared to PID-Feedforward controller, the hybrid controller exhibits a higher response speed, while compared to model-based feedforward controller, it exhibits a better control accuracy. Therefore, the proposed hybrid control algorithm based on the ODE dynamic model effectively enhances the control response speed and improves the control precision of the system.