Abstract
This study presents a systematic investigation of air suspension control strategies for vehicular applications, employing computational optimization techniques to enhance both ride comfort and road holding performances. A comprehensive simulation framework was developed, incorporating a quarter-truck model with nonlinear air spring dynamics subjected to spectrally rich road excitations. The research methodology integrates conventional PID control with multi-objective genetic algorithm optimization to determine Pareto-optimal solutions for conflicting performance criteria. Extensive numerical simulations reveal that the optimized controller achieves significant improvements in vibration attenuation (58.4% reduction in sprung mass displacement) while maintaining superior road contact characteristics (41.5% enhancement in tire-road contact force) compared to a passive system. The analysis provides quantitative insights into the fundamental trade-space between ride comfort-oriented and stability-focused control objectives, demonstrating that intelligent optimization approaches can effectively navigate these design compromises. Furthermore, the results establish practical boundaries for control system performance under realistic operating conditions, offering valuable guidelines for automotive suspension system design. The technical contributions include novel control architectures, advanced performance evaluation metrics, and a rigorous methodology for controller optimization in nonlinear air suspension systems. These findings advance the state-of-the-art in vehicle dynamics control and identify several promising avenues for future research in adaptive control systems and hybrid optimization algorithms.
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