An ARX Model-Based Predictive Control of a Semi-Active Vehicle Suspension to Improve Passenger Comfort and Road-Holding

Abstract

Passenger comfort and vehicle stability are key aspects that must be guaranteed on ground vehicles, and semi-active suspensions have offered an outstanding solution to meet these opposite objectives. This contribution describes a novel autoregressive with exogenous input (ARX) model-based predictive control strategy handled by a driver block applied on a semi-active vehicle suspension to improve passenger comfort and road holding when compared against a passive vehicle suspension system and another more complex control designs reported in the literature. The ARX model employs a driver block to reduce the computational load of the closed-loop semi-active suspension. In addition, the controller’s formulation and the case study consider the actuator’s physical constraints to achieve more realistic results. This case-study includes a one-quarter semi-active suspension with two degrees-of-freedom, and the numerical data comes from a real magnetorheological damper characterization. The results, in frequency-domain and time-domain, are measured based on specific performance criteria. A substantial improvement against a passive suspension is quantified and discussed. For a broader perspective of the findings, the results are compared against another reported work. This research effort could be the basis of further studies to achieve more robust solutions such as adaptive/optimal predictive controllers to improve vehicle’s comfort and stability.