Constrained H ∞ control for a half-car model of an active suspension system with actuator time delay by predictor feedback

Abstract

In this paper, constrained memory state-feedback H_∝ control for a half-car model of an active vehicle suspension system with input time-delay in the presence of external disturbance has been investigated. Its prime goal is to improve the inherent trade-offs among power consumption, handling performance, ride quality, and suspension travel. The tire deflections and the suspension deflections are constrained by their peak response values in time domain using the generalized H₂ (GH_∝) norm (energy-to-peak) performance, while the ride comfort performance of the suspension system is optimized by notion of the H_∝ control (energy-to-energy) to measure the body accelerations including both the heaving and the pitching motions. Similar to the well-known prediction-based methods, the prediction vector of the system is achieved to construct the memory state-feedback controller. Using the prediction vector, sufficient conditions guaranteeing closed-loop system stability as well as disturbance attenuation are obtained as some delay-dependent linear matrix inequalities (LMIs). In addition, some LMIs are added to limit the gain of the controller. In the case of feasibility, obtained LMIs provide the stabilizing gain of the memory controller. The proposed approach is applied to a half-car model of an active suspension system considering the actuator time-delay to illustrate the effectiveness of the proposed method.