The precise tracking control of high-speed train is an essential prerequisite to ensure the safety and comfort of the train. In this paper, an adaptive iterative learning control (ILC) scheme for the velocity and displacement tracking of high-speed train is proposed to handle the unknown time-varying parameters and lumped uncertainties. The composite energy function (CEF) method is used to analyze the stability of closed-loop system. Since the train usually runs on the same railway periodically, such as the same tunnels, slopes, bridges, etc., ILC is an inherent method for designing the tracking controller that is able to improve the operation performance of train iteratively. To the best of our knowledge, it is the first time that the multi-agent framework and ILC methodology are considered simultaneously in a single train, which can better reveal the coupled characteristic of adjacent cars and impose the repetitive operation pattern of train. The results of numerical simulations show that the tracking performance of the train toward the reference trajectory is significantly improved along with the increase of the number of operations.

In this paper, the speed and position tracking problem of high-speed trains is investigated in the presence of actuator faults. A multi-agent system model is constructed, where each vehicle of the train is regarded as a controllable agent. A distributed adaptive controller is then proposed for healthy train system based on terminal sliding mode control. Further, a distributed adaptive fault-tolerant controller (DAFC) is designed for high-speed trains considering actuator faults, and an auxiliary system is introduced to cope with the influence of input saturation. To the best of our knowledge, it is the first time that the distributed fault-tolerant control is considered in a multi-agent system model, which can better utilize the residual power of each vehicle when actuator faults occur. The stability of the closed-loop control system is analyzed through Lyapunov theory, and it is proven that the speed and position tracking errors of all vehicles are cooperative convergence. The simulation results are presented to demonstrate the effectiveness of the proposed DAFC. Compared with the existing method, the proposed DAFC reduces the average speed and position control errors by 54.3% and 55%, respectively.

Aiming to minimize the total electricity usage in realistic operation of urban rail transit (URT) trains, this paper establishes the coupled model for the train-line-electric network-timetable (TLET) comprehensive system, and proposes a multi-train cooperative eco-driving method to achieve the system energy optimal. The dynamic power flow within the DC railway system with multiple trains is analyzed by modeling the traction power supply system (TPSS), and an improved power flow calculation (PFC) method is proposed to calculate the dynamic power flow distribution. To achieve the total substation energy minimization, a space-time-speed (STS) three-dimensional network is presented to transform continuous train motion process into discrete states on space-speed plane, and a multiple individuals dynamic programming (MIDP) algorithm is throughly developed to cooperatively optimize the multi-train trajectories along the time horizon, with which the optimal speed profiles and timetables of each train can be obtained simultaneously. Moreover, a computationally efficient heuristic algorithm together with a train-to-train (T2T) communication policy are developed as a comparative study. Numerical experiment with field data from Guangzhou Metro Line 8 is implemented, to illustrate the energy-saving performance of the proposed methods.

The Multi-train comprehensive optimization (MTCO) combined with the AC traction power supply system (TPSS) can achieve energy savings of traction substations. However, traditional energy-saving optimization methods can cause continuous voltage fluctuations in the power grid, which may endenger train security. To address this issue, this paper proposes a comprehensive optimization method for energy-saving and voltage-stability in multiple trains. Firstly, this paper establishes the Train-Track-Power grid (TTP) comprehensive optimization model for freight trains, which is composed of Train dynamics model, traction drive model, and TPSS. Considering the dynamic complex power and network power loss, a multi-objective function for train energy saving and voltage offset is constructed. Due to the strong coupling and complexity of the nonlinear TTP model, it is difficult to optimize algorithm solution and convergence accuracy is low, as well as the deficiencies in the distribution of non-dominated solution set during iterations, an adaptive multi-strategy multi-objective particle swarm optimization algorithm (amMOPSO) is proposed. Then, the effectiveness of the TTP model is validated through the measured data from the Shuohuang Railway in China. Finally, several classical and state-of-the-art algorithms are employed for experimental comparisons. Experimental results show that amMOPSO outperforms the other algorithms. And the feasibility of the freight multi-train MTCO method is verified through three comparative experiments. The energy consumption of the trains is reduced by 53.97%, the total voltage offset is reduced by 66.96%, and the utilization efficiency of RBE (Regenerative Braking Energy) is increased by 75.3% as demonstrated through the comprehensive optimization experiments involving three trains.

A tram with on-board hybrid energy storage systems based on batteries and supercapacitors is a new option for the urban traffic system. This configuration enables the tram to operate in both catenary zones and catenary-free zones, and the storage of regenerative braking energy for later usage. This paper presents a multiple phases integrated optimization (MPIO) method for the coordination of speed profiles and power split considering the signal control strategy. The objective is to minimize the equivalent total energy consumption of all the power sources, which includes both the energy from the traction substation and energy storage systems. The constraints contain running time, variable gradients and curves, speed limits, power balance and signal time at some intersections. The integrated optimization problem is formulated as a multiple phases model based on the characters of the signalized route. An integrated calculation framework, using hp-adaptive pseudospectral method, is proposed for the integrated optimization problem. The effectiveness of the method is verified under fixed time signal (FTS) control strategy and tram priority signal (TPS) control strategy. Illustrative results show that this method can be successfully applied for trams with hybrid energy storage systems to improve their energy efficiency.