Comprehensive performance and robustness analysis of an active half-vehicle suspension with roll dynamics using an enhanced static output-feedback H _∞ control framework

This paper presents a comprehensive design, performance evaluation, and robustness analysis of an enhanced Static Output-Feedback (SOF) H ∞ control framework for an active half-vehicle suspension system incorporating roll dynamics. While active suspensions promise superior ride comfort and handling, implementation-oriented assessment is often hindered by the need for full-state feedback, computational complexity, and sensitivity to system delays. To address these challenges, a half-vehicle model with four degrees of freedom, including vertical and roll motions, is developed. Two H ∞ controllers are synthesized via Linear Matrix Inequalities: a Conventional SOF (C-SOF) controller and a proposed Novel SOF (N-SOF) controller. Extensive frequency-domain analysis demonstrates the superiority of the N-SOF controller, achieving a 58.3% reduction in the H ∞ norm and effectively suppressing both vertical and roll mode resonances compared to a passive baseline. Time-domain simulations under standard bump and random road excitations reveal substantial improvements in ride comfort, with the N-SOF controller providing a 60.4% reduction in vertical acceleration RMS and a 61.1% reduction in roll acceleration RMS, while simultaneously requiring 9.5% less control effort than the C-SOF controller. An important aspect of this work is the detailed time-delay robustness assessment under representative delayed-network operating implementation conditions, which shows that the proposed N-SOF controller maintains stable and high-performance operation with only a 7.2% performance degradation at a 100 ms delay, representing a 35% higher delay tolerance compared to the conventional approach. The study establishes that the proposed N-SOF H ∞ control framework demonstrates favorable comparative robustness and vibration attenuation characteristics for active suspension control applications, balancing high performance, implementation-oriented simplicity, and robustness under the adopted simulation assumptions.