This study introduces an adaptive model predictive controller (AMPC) to enhance ride comfort in vehicles equipped with a hydraulically interconnected suspension (HIS). To accomplish this, a mathematical model for the HIS is created, and the model predictive controller (MPC) algorithm is designed accordingly. The nonlinear model is linearized by the application of small perturbation theory. Two types of MPC controllers—Predictive Hydraulic Interconnected Suspension (PHIS) and Predictive Load Leveling HIS (PLLHIS)—are used. Each uses a different cost function (a mathematical expression that quantifies ride discomfort). Ride comfort is measured using the International Organization for Standardization (ISO) 2631-1:1997 standard for calculating safe exposure time. Load leveling aims to reduce vehicle height changes, roll, and pitch angles. The controller’s performance was tested using vehicle dynamics simulations. It was compared to traditional passive suspension systems. The cost function includes vertical acceleration, roll, and pitch values influencing passenger ride comfort. The tests are performed on symmetric and asymmetric roads and half-sine bumps. An experiment is also done on a real road to test how well the controller works in actual driving conditions. Despite advances in active and semi-active suspension technologies, there remains a gap in integrating predictive control strategies with load leveling objectives to simultaneously enhance both ride comfort and vehicle stability. This study addresses that gap by proposing an adaptive MPC framework that incorporates both ride comfort and body leveling goals. The findings indicate that the proposed controller significantly enhances ride comfort. On average, PHIS improves vertical acceleration by 10%, 40%, and 43% across symmetric, asymmetric, and experimental conditions. PLLHIS further improves these results by an additional 20%, 25%, and 7%, respectively.
Damavandi et al. (Mon,) studied this question.