Adaptive Robust Control-Based Ride Comfort Enhancement for Nonlinear Suspension–Seat–Driver Systems

Ride comfort is a critical issue in vehicle dynamics, as excessive vibrations adversely affect passenger comfort and human health. This paper presents a comparative performance analysis of a passive suspension system, fuzzy logic control (FLC), and a newly designed adaptive robust control (ARC) strategy applied to a nonlinear quarter-car suspension–seat–driver model. The primary objective is to improve ride comfort while maintaining vibration levels within accepted health criteria. First, the nonlinear dynamic model of the suspension–seat–driver system is established. The FLC structure and rule base are determined based on heuristic knowledge. Passive and FLC-based systems, while effective to some extent, suffer from limited adaptability to external disturbances and modeling uncertainties, slower convergence, and suboptimal vibration attenuation. The main contribution of this study is the design and implementation of a novel adaptive robust controller that effectively handles modeling uncertainties, external disturbances, and parameter variations. Different controller placement approaches within the system are also investigated. Numerical simulations are conducted under identical operating conditions for the uncontrolled system and all control strategies. The results demonstrate that although the FLC improves ride comfort compared to the passive system, the proposed ARC achieves the best overall performance, providing superior vibration attenuation, faster convergence, and enhanced robustness for nonlinear vehicle suspension systems. Quantitatively, the ARC reduces head acceleration RMS from 0.1693 m/s2 (passive) and 0.1422 m/s2 (FLC) to 0.0705 m/s2, and upper torso RMS from 0.1689 m/s2 (passive) and 0.1417 m/s2 (FLC) to 0.0703 m/s2, corresponding to approximately 58% reduction relative to passive and 50% improvement over FLC.