A novel metaheuristic algorithm based FOPID approach for decentralized control of vibration of a railway vehicle

Abstract

This article investigates a 27-degree-of-freedom dynamic model of a full-scale railway vehicle. The controlling action is performed by developing a decentralized control structure with five independent Fraction Order Proportional Integral Derivative (FOPID) controllers three types of random track irregularities, vertical profile, lateral alignment, and cross-level. A novel metaheuristic optimization technique named hybrid Particle Swarm Optimization-Gray Wolf Optimization (hybrid PSO-GWO) is proposed to optimize active force for an active suspension system. The simulated results show that the hybrid metaheuristic algorithm outperforms with a significant reduction in vehicle vibration ensuring enhanced ride comfort of the car body.

Keywords:

• Active suspension
• decentralized control
• FOPID controller
• metaheuristic algorithms
• railway vehicle
• ride comfort

Disclosure statement

No potential conflict of interest was reported by the author(s).