Levitation control is a core technique of magnetic levitation (Maglev) trains, whose performance has a significant impact on the driving safety and ride comfort during the operation of Maglev trains. With the rapid development of high temperature superconducting (HTS) materials, a novel hybrid levitation system for Maglev trains, composed of HTS and normal conducting electromagnets, is becoming a more promising alternative to the conventional levitation system, due to a lager levitation air gap and lower energy consumption. The controller of the hybrid levitation system is hard to design due to strong nonlinearity, open-loop instability and fast-response requirements. In this study, we propose a robust model predictive control (MPC) strategy for the novel hybrid levitation system in which state constraints are taken into account, including the air gap, and the velocity and acceleration of the hybrid magnet. In the MPC strategy, the prediction model employs a stabilized model through nonlinear and linear state feedback. With regard to the online computational load, the primal-dual interior-point (PDI) algorithm is adopted to solve the optimization problem with high efficiency to deal with constraints in a real-time manner. Finally, simulation results are demonstrated to verify the effectiveness of the proposed MPC-PDI control strategy for the hybrid levitation system, and the significances considering state constraints are revealed. This study provides an engineering realization solution to the novel hybrid levitation system of a Maglev train.

Traffic flow in a road network is mutually interactive and interdependent with each other. It is challenging to describe the dynamics of traffic network flow by using analytical methods. The deep convolutional neural network (DCNN) model is employed to address traffic network flow prediction. To improve the parameter learning efficiency confronting traffic big data, a parallel training approach is developed for the DCNN prediction model. The theoretical foundation is developed for the parallel training algorithm of the DCNN model. A master-slave parallel computing solution for traffic network flow prediction is implemented on the Spark cloud. Real data of traffic network flow is applied to verify the effectiveness of the DCNN prediction model and the parallel training algorithm. The experimental results demonstrate that the DCNN prediction model for traffic network flow outperforms the typical prediction models based on backpropagation neural networks, support vector regressions, radial basis functions, and decision tree regressions. The proposed parallel training method can improve the training efficiency and obtain global features of the entire dataset from local learning with regard to the respective data subsets.

When dealing with traffic big data under the background of Internet of Things (IoT), traffic control under the single-machine computing environment is difficult to adapt to the massive and rapid analysis and decision-making. To tackle this problem, we propose a parallel computing approach of traffic network flow control based on the mechanism of model predictive control (MPC). A non-analytical rule-based traffic flow model is developed to forecast vehicle movements in the prediction horizon according to the real-time feedback information of traffic flow, and evaluate the performances of candidate control strategies. Furthermore, to accelerate the solution process of obtaining the optimal control schemes in the prediction horizon, a two-level hierarchical parallel genetic algorithm (HPGA) based on Spark cloud computing is designed. Through the parallel computing architecture, the computationally intensive optimization tasks are decomposed into multiple parallel sub-tasks with the aid of resilient distributed datasets (RDDs), which improves the computational efficiency. The simulation results demonstrate the validity of the proposed methodology for traffic network flow predictive control with respect to unsaturated and oversaturated traffic scenarios. The Spark-based parallel optimization approach has the potential to satisfy the computing requirements of online optimization when dealing with the big data of traffic network flow control while keeps favorable control performances.

The magnetic levitation system is a critical part to guarantee safe and reliable operations of a maglev train. In this paper, the control strategy is proposed for the magnetic levitation system based on the model predictive control incorporating two-level state feedback. Taking advantage of the measurable state variables, that is, air gap, electromagnet acceleration, and control current through high-resolution sensor measurement, the first-level nonlinear state feedback is to linearize the unstable nonlinear magnetic levitation system, and the second-level linear state feedback is to further stabilize the system and improve the dynamic performances, which together provide a stable prediction model. The simulation results demonstrate that the proposed control strategy can ensure high-precision air gap control and favorable disturbance resistance ability.

With the development of urban rail transit, energy conservation has been highly concerned by the worldwide railway industry. The existing energy-saving control methods are hard to meet the requirements of large-scale multi-train intelligent cooperation. Furthermore, in order to optimize the overall energy consumption and make effective utilization of regenerative braking energy (RBE), the micro train driving strategies must be taken into account in the macro train schedule regulation. This paper aims to establish a systematic optimization model to describe the train traffic environment and design a deep reinforcement learning (DRL) approach using multi-agent cooperative actor-critic (MACAC) to reschedule multiple trains for energy saving. Due to the high dimensional characteristic of control actions, the action representation technique is applied for generalization. The multiple agents are deployed on the Spark cloud platform to effectively improve the learning efficiency through multi-node parallel computing. The proposed MACAC approach on Spark cloud is capable of dealing with the intelligent learning and computing efficiency of large-scale train operation synergy, and saving energy to the maximum extent on the premise of satisfying various time constraints. The evaluation was carried out through the field data of Beijing Yizhuang subway line. The results show that the proposed MACAC approach can effectively learn to improve the energy efficiency, and the parallel computing method on Spark cloud is suitable for practical applications.