Optimizing active suspension systems with robust h∞ control and adaptive techniques under uncertainties

Abstract

This study presents a transformative approach to active suspension control through the development of a hybrid robust-adaptive framework that synergistically combines three advanced techniques: μ-synthesis enhanced H∞ control, model reference adaptation, and real-time frequency-domain optimization. The novel architecture overcomes fundamental limitations in conventional systems by simultaneously addressing (i) parametric uncertainties through structured robustness margins (μ < 1 for ±25 % variations in mass/stiffness), (ii) unstructured road disturbances via adaptive gain scheduling, and (iii) resonant vibrations using closed-loop FFT analysis with 50 ms spectral updates. The controller's dual-degree-of-freedom design introduces a breakthrough solution where the H∞ core guarantees stability while the adaptive module dynamically adjusts damping ratios and stiffness coefficients through Lyapunov-based parameter estimation, achieving 40 % faster convergence than fixed-gain alternatives. Comprehensive simulations under ISO-standardized road profiles demonstrate unprecedented performance: 87.1 % reduction in suspension travel (0.113 m to 0.015 m) and 49.3 % decrease in body acceleration (6.38m/s² to 3.73m/s²) versus passive systems, while maintaining 18 % lower energy consumption than traditional H∞ implementations. The frequency-domain optimization proves particularly effective, reducing resonant peak magnitudes by 62–75 % in the critical 1–4 Hz comfort range and 55 % at the 15 Hz wheel-hop frequency. Practical implementation advantages include compatibility with standard automotive sensors (requiring only accelerometers and displacement sensors), modest computational load (executable on 100 MHz automotive-grade processors), and self-calibrating capability that eliminates manual tuning. These advancements position the framework as an ideal solution for next-generation vehicles, with demonstrated applicability to electric platforms (through regenerative damping integration) and autonomous systems (via V2X communication-enabled predictive adaptation). The research establishes new theoretical foundations for uncertainty management in vehicle dynamics while delivering a commercially viable control strategy validated under realistic operating conditions.