Semi-Active Suspension Control Using “Fast” Model-Predictive Techniques

Abstract

The problem considered in this paper is the design and the analysis of a control strategy, for semiactive suspensions in road vehicles, based on Model Predictive Control (MPC) techniques. The computed control law, using predictive techniques aims to optimize, the suspension performance, by minimizing a quadratic cost function while ensuring that the magnitude of the forces generated by the control law satisfies the physical constraints of passive damping. The on line computation difficulties related to the predictive control law are overcome by means of a "Fast" implementation of the MPC algorithm (FMPC). The estimation of the system state variable needed for control law computation is provided by a suitable "robust" observer, whose accuracy is not affected by variation of the system parameters (i.e. masses, damping and stiffness coefficients, etc.). A performance comparison with the well established semi-active Sky-Hook strategy is presented. The achievable performance improvements of the proposed design procedure over Sky-Hook control law are showed by means of simulation results.This research was supported in part by funds of Ministero dell'Istruzione dell'Università e della Ricerca under the Project "Robustness and optimization techniques for control of uncertain systems".

The impact of suspension control on the controllability of the lateral vehicle dynamics

“…The summary of the semi-active suspension control considering the comfort criterion is presented in [13], [14]. Sky-Hook and clipped control design laws based on model predictive control technique are used in [15]. LPV-based robust control design methods to improve the motion of the chassis are found in [16].…”

Robust Control for Ride Comfort Improvement of an Active Suspension System considering Uncertain Driver's Biodynamics

“…Using this freedom in the phase of each block of D, the frequency-dependent scaling matrix D ω can be restricted to be a real-rational, stable, minimum-phase transfer function, , and not affect the value of the minimum. Hence the new optimization is (25) This optimization is currently solved by an iterative approach, referred to as D -K iteration. A block diagram depicting the optimization is shown in Figure 7.…”

“…The weight of the sprung mass is supported by the spring. The equations of motion for this half-car model, which are bounce, pitch and each unsprung motion are as follows [25], (1) where H p is the distance from the pitch center to the CoG of the vehicle body. The spring coefficient of each wheel is different from another one, and were set to K sf…”

Active Adaptive Estimation and Control for Vehicle Suspensions With Prescribed Performance

“…In the past decades, various suspension systems have been investigated, e.g. passive suspensions, semi-active suspensions and active suspensions [4][5][6][7][8][9]. Among these methods, active suspensions are able to add and dissipate energy from systems by using extra actuators placed between the car body and the wheelaxle [10].…”