KEYWORDS: Education and training, Transportation, Intelligence systems, Systems modeling, Computer programming, Wireless communications, Telecommunications, Switching, Resistance, Process control
Train in the virtually coupling train group operates at the same speed and a small interval, and is prone to be disturbed by adjacent trains and generate control fluctuations, which also brings greater pressure to communication. The self-triggering control can relieve the pressure of communication and calculation and reduce the control frequency, but the calculation cost at the trigger time will inevitably rise. Therefore, a cruise control method based on improved self-triggered model predictive control is proposed. The results show that this method can greatly reduce the calculation time while having good convergence.
The trajectory tracking is the core function to realize automatic operation of high-speed train. This paper designed a model predictive controller under consideration of multiple optimal objectives to achieve comprehensive control performance of accurate tracking, passenger comfort and energy conservation. By linearizing the cost function and constraints, the control optimization model is transformed into a mixed-integer quadratic programming model, which is solved by the Gurobi solver. There are several stages in the whole process of train operation with different priorities of multiple objectives. To adapt the different priorities of multiple objectives, this paper proposed a multi-objective and multi-stage (MOMS) MPC controller based on multi-objective particle swarm optimization algorithm and a selection strategy of optimal weight coefficient. The simulation results show that the controller designed in this paper can optimize the weight in different scenarios to improve comprehensive control performance.
The train operations are subject to several uncertain factors, which pose challenges to achieving the train’s multi-objective optimal control. To improve the service quality and transportation efficiency of high-speed railways, this paper proposes a control method to realize the multi-objective operation of high-speed trains in terms of safety, punctuality, energy efficiency, and driving smoothness considering the uncertainty. Firstly, by studying the kinematic model under uncertainty and operational constraints of the train, and combining them with multi-objective optimization theory, an optimal control model for the multi-objective operation of high-speed trains is established. Secondly, to solve the optimal control problem under the uncertainty, the nonlinear and non-convex optimal control problem for multi-objective operations of the train is reformulated as a robust economic model predictive control framework, which is then convexified into a computationally tractable convex model, and then the corresponding algorithm is designed to give the solutions. Finally, the proposed method is validated through simulation on a real high-speed rail line, and compared with tracking-type robust model predictive control. The simulation results demonstrate that the proposed method is capable of achieving safe, punctual, energy-efficient, and smooth operation of high-speed trains in the presence of uncertain factors.
The increasing speed and operation density have raised higher challenge to train trajectory tracking problem. In this paper, an event-triggered Tube Model Predictive Control (ETTMPC) controller is proposed, which can handle large frequent external disturbances with lower computational complexity. First, a classic MPC model of High-Speed Train (HST) trajectory tracking is established, then a Tube MPC (TMPC) model is introduced using the robust reachable set method, which improves computational time and space efficiency comparing to the traditional minimum robust positive invariant set (mRPI) method. In order to reduce the trigger frequency, an event-triggered strategy and the ETTMPC control structure are introduced, which only triggers computation when the trajectory deviation exceeds the threshold set. The simulation result shows that the proposed TMPC has higher robustness in large disturbance scenarios, while the ETTMPC has similar tracking performance as the TMPC but with a lower online computational cost.
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