Metaheuristic tuned decentralized PID controller based active suspension system for railway vehicle

Abstract A high‐speed railway system is one of the sustainable alternatives to other modes of transportation and may connect the most congested urban cities with minimum carbon emissions. However, the vibration intensity increases as the train's operating speed increases, resulting in deteriorated ride comfort and stability. Hence, this article investigates a 27‐degree‐of‐freedom (DOF) dynamic model of railway vehicles with an improved active suspension system. The decentralized control structure performs the controlling action with five optimized Proportional Integral Derivative (PID) controllers that suppress the vehicle body's vertical, lateral, pitch, roll, and yaw motions. Further, to optimize the PID parameters, three metaheuristic optimization techniques, Genetic algorithm (GA), Grey Wolf Optimization (GWO), and Flower Pollination Algorithm (FPA), are utilized, and their simulated results are compared with the passive system as well as other conventional tuning technique. Moreover, the performance of the proposed control strategy is evaluated in the frequency domain under random track irregularities, and the results are characterized in terms of power spectral densities (PSDs). The simulated results show that among the proposed metaheuristic algorithms, FPA outperforms with a significant reduction in vehicle vibration compared to other tuning methods. The percentage reduction of the vertical, lateral, pitch, rolls, and yaw accelerations is 71.4%, 35.1%, 52.8%, 48.1%, and 38.2%, respectively, ensuring enhanced vehicle ride comfort.