Optimized Virtual Model Reference Control for Ride and Handling Performance-Oriented Semiactive Suspension Systems

This paper proposes an optimized virtual model reference (OVMR) control synthesis method for semiactive suspension control based on ride and vehicle handling characteristics. First, we present the semiactive Macpherson suspension system as an H_∞ robust output feedback-oriented control model. Then, by using the combination of a set of linear matrix inequalities (LMIs) and genetic algorithm (GA), the desired internal states for the tracking control problem of the semiactive suspension can be obtained via an OVMR. To achieve the H_∞ performance of ride comfort and vehicle handling against the influence of parameter uncertainties and external disturbances of the system, a robust adaptive controller is designed so that the controlled system can track the desired states generated from OVMR. The tracking control can be converted into a stabilization problem with asymptotic convergence in the sense of Lyapunov stability theorem. To validate the effectiveness of the proposed approach, the cosimulation technique is employed to bridge the gap between the mathematically well-defined system model and the optimization quality of control. It can be confirmed that the designed control system can achieve performance-effective suspension control through the confident software-in-the-loop (SITL) simulation.