The research on the dynamic characteristics of vehicles requires wind tunnel tests. The natural mass frequency of the support mechanism and the interference of the support mechanism on the flow field will greatly reduce the repeatability and accuracy of the experimental results. The magnetic suspension and balance system (MSBS) uses non-contact electromagnetic force instead of mechanical support to eliminate the support mechanism’s interference with the test results. The MSBS needs to actively adjust the electromagnetic force to control the position of the tested model. At present, its control strategy is PID control. However, when the wind tunnel is opened, there will be large aerodynamic loads, leading to large changes in displacement. We propose a disturbance compensation control algorithm framework that can quickly estimate the external force disturbance and quickly compensate it to the control loop, reduce the influence of the disturbance on the control system, and provide stability and accuracy of displacement control.

Magnetic suspension balance systems (MSBSs) need to allow vehicle models to levitate stably in different attitudes, so it is difficult to ensure the stable performance of the system under different levitation gaps using a controller designed with single balance point linearization. In this paper, a levitation controller based on linear model predictive control and a back-propagation neural network (LMPC-BP) is proposed and simulated for single-point magnetic levitation. The deviation of the BP network is observed and compensated by an expansion state observer (ESO). The iterative BP neural network model is further updated using current data and feedback data from the ESO, and then the performance of the LMPC-BP controller is evaluated before and after the update. The simulation results show that the LMPC-BP controller can achieve stable levitation at different gaps of the single-point magnetic levitation system. With further updating and iteration of the BP network, the controller anti-jamming performance is improved.

The Magnetic Suspension Balance System (MSBS) serves as a core apparatus for interference-free aerodynamic testing in wind tunnels, where its high-precision levitation control performance directly determines the reliability of aerodynamic force measurements. This paper addresses the strong coupling issues induced by rigid-body motion in the MSBS vertical suspension system and proposes a decoupling control framework integrating classical decoupling methods with geometric feature transformation. First, a nonlinear dynamic model of the six-degree-of-freedom MSBS is established. Through linearization analysis of the vertical suspension system, the intrinsic mechanism of displacement-pitch coupling is revealed. Building upon this foundation, a state feedback decoupling controller is designed to achieve decoupling among dynamic channels. Simulation results demonstrate favorable control performance under ideal linear conditions. To further overcome its dependency on model parameters, a decoupling strategy based on geometric feature transformation is proposed, which significantly enhances system robustness in nonlinear operating conditions through state-space reconstruction. Finally, the effectiveness of the proposed method in vertical suspension control is validated through both numerical simulations and a physical MSBS experimental platform.