Pointwise‐constrained optimal control of a semiactive vehicle suspension

Abstract

SummaryThis article presents an optimal control strategy (OCS) for semiactive vehicle suspensions with road profile sensors. The suspension is modeled as a quarter‐car model with a magnetorheological (MR) damper. The OCS main objective is to minimize the fourth‐power acceleration of the sprung mass. In addition, three pointwise constraints of the model are taken into account when the optimal control problem is solved: suspension travel limits (upper and lower) and tyre vertical force. In order to deal with a large number of constraints, we implement the gradient optimization method based on the method of moving asymptotes routine, which shows very good performance reaching optimal controls while satisfying the constrains. The solution has been compared with two passive MR damper configurations (low and high damping) as well as Skyhook and Balance control strategies for three different road inputs. Results show that OCS fulfills the constraints and reduces the sprung mass acceleration peak and the root‐mean‐quad acceleration up to 59%, in comparison to passive strategies.

H∞optimal preview tracking control problem with disturbance attenuation

“…then the system (2) is derived based on (5), where 𝜙 r (s) is the minimum polynomial of the matrix S. An appropriate matrix F is selected so that y r (t) is the output of system (2). Besides, when S is unstable, according to Zheng [43], 𝜙 r (s) is the unstable part of y r (t).…”

“…Preview control theory has received a lot of attention in theory since it was put forward in the 1960s [2,3]. There are also a wide range of applications in practice, such as the control of semiactive/active vehicle suspensions [4,5] and autonomous driving technologies [6][7][8][9].…”

Optimal Skyhook and Groundhook Control for Semiactive Suspension: A Comprehensive Methodology

“…is controller joints the benefits from the skyhook and the groundhook. Combining equations ( 23) and (24), it is possible to determine the semiactive hybrid controller logic represented in equations (25) to (27). Where new variables appear, C hbd represents the controller's gain and α represents the proportion of each controller to be used.…”

“…In the different control strategies investigated, an effort has been made to optimize a performance index as the minimization of root-mean-quad weighted acceleration [27] or the use of an optimal feedback controller through linear quadratic approaches [28][29][30]. In this last approach, the control strategy was complemented with an inverse model of the MR damper and Artificial Neural Networks.…”

Study on the integrated structure/control design method of magnetorheological damper for improving vehicle posture

“…Through related experiments, the effects of the controller and the control strategy were analyzed and verified. 16,17 In order to minimize the fourth power of sprung mass acceleration, Palomares et al 18 proposed an optimal control strategy for a semi-active suspension with a road profile sensor. The solution was compared with two different MR dampers and three different road input control strategies.…”