The maglev train has been successful in practice as a new type of ground transportation. Owing to the inherent nonlinearity and open-loop instability of the electromagnetic suspension (EMS) system, an analogue or a digital controller is used to control the maglev trains’ stability. With the rapid development of embedded systems and artificial intelligence, intelligent digital control has begun to replace the conventional analogue control technology creating a new approach to the EMS control system. This paper proposes a hardware module for an embedded levitation controller based on digital signal processor and field programmable gate array, hence producing an open loop mathematical model of the embedded maglev control system. The deep learning controller is then developed based on a deep belief network (DBN) algorithm and a proportional integral derivative feedback controller. The simulations are conducted in the MATLAB environment after training the DBN. Simulation results are compared with those obtained from the conventional controller. Finally, experiments are implemented to examine the feasibility in practice of the application of the DBN into a maglev embedded control system. The system, with the proposed controller, can accurately track the target airgap of 8 mm. The maximum tracking error of sinusoidal trajectory is 0.17 mm and the maximum tracking error of step trajectory is 0.98 mm. Both simulation and experimental results are included in this paper to show that the proposed deep learning controller can be more robust and less complicated to implement in maglev control applications.

磁浮列车作为一种新型地面交通已在实践中获得成功。由于电磁悬浮（EMS）系统固有的非线性和开环不稳定性，采用模拟或数字控制器来控制磁悬浮列车的稳定性。随着嵌入式系统和人工智能的快速发展，智能数字控制开始取代传统的模拟控制技术，为EMS控制系统开辟了新的途径。本文提出了一种基于数字信号处理器和现场可编程门阵列的嵌入式悬浮控制器的硬件模块，从而产生了嵌入式磁悬浮控制系统的开环数学模型。然后基于深度置信网络（DBN）算法和比例积分微分反馈控制器开发深度学习控制器。训练 DBN 后，在 MATLAB 环境中进行仿真。仿真结果与传统控制器获得的结果进行了比较。最后通过实验验证了DBN应用于磁悬浮嵌入式控制系统的可行性。该系统采用所提出的控制器，可以精确跟踪 8 mm 的目标气隙。正弦轨迹最大跟踪误差为0.17 mm，步进轨迹最大跟踪误差为0.98 mm。本文包含仿真和实验结果，以表明所提出的深度学习控制器在磁悬浮控制应用中实现起来更加鲁棒且简单。