Combining the Matter-Element Model With the Associated Function of Performance Indices for Automatic Train Operation Algorithm

Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation

“…In fact, during automatic train operation, there are extensive problems need to be considered such as real-time velocity sampled inaccurately, signal delay, and packet loss in transmission and a certain degree of unstable in tracking control system, so a certain proportion of literatures use real vehicle experiments and actual driving data (Ning'xi line, Yizhuang line, Shanghai Railway Transit in China) to verify the effectiveness of the algorithm [4,19,21]. Due to the situation of the actual automatic train operation experiment, it is difficult to implement, and traditional simulation based on pure software environment cannot truly reflect the actual automatic train operation process; hardware-in-the-loop simulation (HILS) is often used in automatic train operation due to its characteristics [25,26].…”

“…In addition, a series of manual driving strategies that will minimize energy consumption for high-speed trains have been researched [18]; an expert system that contains expert rules and a heuristic expert inference method about intelligent train operation optimization for subway has been developed [19]; an intelligent safe driving method (ISDMs) is proposed to obtain better speed-distance curves [20]. Note that preference indices of driving experience are applied in automatic train operation algorithm [21]. Preference information is widely used in multiple decision-making (MPDM) problems such as multi-objective optimization problems, plenty of research findings show that the optimization performance of multi-objective optimization algorithm can be significantly improved using incorporated appropriate preference information.…”

Model Predictive Controller Based on Online Obtaining of Softness Factor and Fusion Velocity for Automatic Train Operation

“…In recent years, many improved algorithms have been applied in the automatic train operation tracking control field, such as robust adaptive automatic control, model predictive control, online learning, iterative learning control, matter-element model, etc. [9][10][11][12][13]. An online approximation-based robust adaptive automatic train control method is proposed for the automatic train operation (ATO) system [9].…”

Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation

“…With the continuous improvement of the performance requirements for urban railway train, the manual driving mode has been unable to meet the operational requirements of train and has gradually shown its own shortcomings. Therefore, ATO system has been developed rapidly due to its rapidity, punctuality, and safety [4][5][6]. The ATO system can adjust the traction or braking forces of the train in real time according to the operating conditions so that it can run safely and efficiently [7].…”