Mixed Sky-Hook and ADD: Approaching the Filtering Limits of a Semi-Active Suspension

Abstract

The problem considered in this paper is the design and analysis of control strategies for semiactive suspensions in road vehicles. The most commonly used control algorithm is the well-known sky-hook (SH) damping. Recently, a new control approach named acceleration driven damping (ADD) has been developed, using optimal-control theory. It has been shown that SH and ADD have complementary characteristics: SH provides large benefits around the body resonance; otherwise performs similarly to a passive suspension; instead, ADD provides large benefits beyond the body resonance. The first goal of this paper is to show that—in their specific frequency domains—SH and ADD provide quasi-optimal performances, namely, that it is impossible to achieve (with the same semi-active shock-absorber) better performances. This result has been obtained using the framework of the optimal predictive control, assuming full knowledge of the disturbance. This result is very interesting since it provides a lower-bound to semi-active suspension performances. The second goal of the paper is to develop a control algorithm which is able to mix the SH and ADD performances. This algorithm is surprisingly simple and provides quasi-optimal performances.

A methodology for optimal semi-active suspension systems performance evaluation

“…[1], [2], [3], [4], [5], [6], [7], [8], [9]), semi-active suspensions have received a lot of attention since they seem to provide the best compromise between cost (energy-consumption, actuators/sensors hardware) and performance. The concept of semi-active suspensions can be applied over a wide range of application domains: roadvehicle, cabin in trucks or tractors, seat, trains, appliances (e.g.…”

An LPV control approach for comfort and suspension travel improvements of semi-active suspension systems

“…This paper is based on the extension of ADD control rule [6]. The ADD is based on a linear model of the electronic shock absorber, F = cż de f whereż de f is the suspension deflection velocity and c is the damping coefficient that may vary from a minimum value c min to a maximum value c max .…”

“…The following frequency response analysis is done by using the "variance gain" algorithm (see [6]) for nonlinear systems. The "variance gain" is simple and provides a good approximation to frequency response.…”

“…(13), the role of u is strongly emphasized when α ≈ 0.5 (i.e when the suspension is going to reach its structural limits). The aim of the H ∞ /LPV design is to minimize the frequency response z de f /z r in high frequencies [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] Hz (the suspension deflection is normally large around the "tyrehop frequency" k t /m us ≈ 12 Hz) while not deteriorating muchz s /z r . Therefore the weighting functions for the H ∞ controller synthesis have been chosen as:…”

“…[1] for a detailed classification), semi-active suspensions have received a lot of attention in the last two decades, since they provide the best compromise between cost (energy-consumption and actuators/sensors hardware) and performance (see e.g. [2], [3], [4], [5], [6], [7]). …”