Intelligent-PID controller design for speed track in automatic train operation system with heuristic algorithms - Havaei, Pedram; Sandidzadeh, Mohammad Ali (Journal of rail transport planning & management, 2022)
In this paper, the problem of speed tracing for automatic train operation is studied. A new Intelligent-PID controller is proposed in which four optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Imperium Colony Algorithm (ICA) for the best parameter tuning with the integration of a novel switching function are used. The algorithms are analyzed and specialized for different driving modes including: acceleration, cruising, braking and speed profile shift. By the use of a switch, the PID controller is tuned according to the best algorithm. The switching action is done through a slight change from the current position to the best values by transient values determined by the other algorithm outputs. The simulation results indicate the excellence of the proposed method. The performance of the suggested structure is compared with a single-mode optimization algorithm without use of the switch. The results of the comparison show that the proposed method can track the trajectory on all driving modes with very high accuracy. •Train speed control is a major concern in the automatic train operation system.•Top heuristic algorithms are tuned and mathematically subjected to driving modes.•A new switching function is designed for the best PID parameter tuning.•Combined switching IPID outperforms single-mode and conventional approaches.