Deep learning controller design of embedded control system for maglev train via deep belief network algorithm
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Deep learning controller design of embedded control system for maglev train via deep belief network algorithm Ding-gang Gao1,2 · You-gang Sun2 · Shi-hui Luo1 · Guo-bin Lin2 · Lai-sheng Tong3 Received: 18 October 2019 / Accepted: 28 March 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Embedded control system · Deep belief network · Maglev train · Deep learning
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You-gang Sun [email protected]
1
Traction Power State Key Laboratory, Southwest Jiaotong University, Chengdu 610031, Sichuan, China
2
Maglev Transportation Engineering R&D Center of Tongji University, Shanghai 201804, China
3
CRRC Zhuzhou locomotive Co., Ltd., Zhuzhou 412001, Hunan, China
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D. Gao et al.
1 Introduction As an application technology with broad development prospects, magnetic levitation technology has the advantages of no contact, no friction, low noise, less pollution and easy maintenance [1–3]. At present, this technology has been widely used in ground transportation [4, 5], a maglev wind tunnel [6], active magnetic bearing (AMB) [7] and other high-tech fields. The maglev train, as a new means of land rail transit, has widely gained attention from scholars and engineers. The magnetic levitation control system is one of the core components of the maglev train. The earlier controller was an analog controller with high hardware dependence. However, after the system design, if the parameters of
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