Vibrations control of railway vehicles using decentralized proportional integral derivative controller with flow direction optimization algorithm

Abstract

The reduction of vibration-induced discomfort in vehicles is an important goal in the field of transportation engineering. Several mathematical models with various controlling techniques, from classical to modern, have been employed to achieve better ride comfort. Still, no comprehensive solution has yet been found. Therefore, this paper proposes a 17-degree-of-freedom (minimum number of coordinates) dynamic model of a full-scale railway vehicle integrated with wheel-rail contact forces and an active suspension system. Two controllers, termed system and force tracking controllers, suppress the vehicle body's vibrations. Based on a multi-loop control structure, three optimally tuned Proportional Integral Derivative controllers evaluate the desired control forces and performs the system controller’s action. While the force-tracking controller generates the command voltage to track that forces. The parameters of controllers are tuned with a novel metaheuristic optimization algorithm known as the flow direction algorithm (FDA), and the results are compared with two other optimization techniques, i.e., particle swarm optimization and ant colony optimization. The simulated results show that the ride comfort of the vehicle is improved with FDA, as the root mean square values of the lateral, roll, and yaw accelerations are reduced by 42.01%, 33.12%, and 48.24%, respectively. Moreover, the simulated results of the proposed model are validated with the experimental results of accelerations. The simulated results show that the proposed system tuned with the metaheuristic algorithm outperforms with a significant reduction in vehicle vibrations.