Optimization of LQR and FLC Controllers by Genetic Algorithm for Two and Three-Axles Active Suspension Systems for Off-Road Vehicles
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Abstract
This paper presents the optimization and control techniques for an off-road vehicle’s active suspension system using two-axle and three-axle half-vehicle models. The optimization and control techniques used in this work are fuzzy logic control (FLC) and linear quadratic regulator (LQR). The results of the active suspension systems were analyzed and compared with the passive system. The pitch and vertical accelerations of the sprung mass were recorded to assess the ride quality and handling. Also, the responses of the suspension and tires are recorded. The results demonstrated that the LQR and FLC could effectively regulate the active suspension, enhancing the cars handling and ride quality without sacrificing their ability to maintain road-holding capability or the rattle-space requirement. The results also showed that using LQR and FLC caused reductions in settling time of 15% and 30%, respectively. The suspension deflection stabilization was much faster than those of the passive system. Furthermore, FLC control needs about half the actuator force of LQR control to attain the intended performance.
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