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The unending pursuit of enhanced ride comfort, vehicle stability, and road adaptability has prompted significant research into semi-active suspension systems, with Magnetorheological (MR) dampers emerging as a feasible option. Magnetorheological (MR) dampers offer swift, reversible, and customisable dampening capabilities due to the unique qualities of magnetorheological fluids, which modify their viscosity in response to magnetic fields. Their tunability and minimal power requirements make them suitable for real-time suspension control; however, leveraging their full potential is challenging due to their nonlinear and hysteretic properties. Integrating machine learning techniques, which enable real-time, data-driven decision-making, significantly improves the capacity to optimise vehicle performance. This article examines the operating principles of MR dampers and the role of machine learning (ML) algorithms in enhancing damper performance. The study elaborates on advancements in MR fluid technology and machine learning (ML) techniques, challenges in system integration, and offers insights into prospective research directions. The report concludes with recommendations for future research and concepts to bridge the gap between theoretical achievements and practical, scalable automotive applications.
Aadil Arshad Ferhath (Thu,) studied this question.