A Novel Robust Optimal Active Control of Vehicle Suspension Systems

Fallah, Saber; Sorniotti, Aldo; Gruber, Patrick

doi:10.3182/20140824-6-za-1003.00685

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…1 − 2 is obtained from the direct measurement of the active suspension actuator displacement and consideration of the suspension installation ratio, i.e., the ratio between the actuator displacement and the relative vertical displacement between the sprung and unsprung masses [42]. ̇1 −̇2 is calculated through differentiation of 1 − 2 with the hybrid smooth derivative method [43].…”

Section: Internal Model Formulationmentioning

confidence: 99%

Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview

Theunissen¹,

Sorniotti

Gruber

et al. 2020

IEEE Trans. Ind. Electron.

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Latest advances in road profile sensors make the implementation of pre-emptive suspension control a viable option for production vehicles. From the control side, model predictive control (MPC) in combination with preview is a powerful solution for this application. However, the significant computational load associated with conventional implicit model predictive controllers (i-MPCs) is one of the limiting factors to the widespread industrial adoption of MPC. As an alternative, this paper proposes an explicit model predictive controller (e-MPC) for an active suspension system with preview. The MPC optimization is run offline, and the online controller is reduced to a function evaluation. To overcome the increased memory requirements, the controller uses the recently developed regionless e-MPC approach. The controller was assessed through simulations and experiments on a sport utility vehicle demonstrator with controllable hydraulic suspension actuators. For frequencies <4 Hz, the experimental results with the regionless e-MPC without preview show a ~10% reduction of the root mean square value of the vertical acceleration of the sprung mass with respect to the same vehicle with a skyhook controller. In the same frequency range, the addition of preview improves the heave and pitch acceleration performance by a further 8% to 21%.

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Section: Internal Model Formulationmentioning

confidence: 99%

Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview

Theunissen¹,

Sorniotti

Gruber

et al. 2020

IEEE Trans. Ind. Electron.

Self Cite

View full text Add to dashboard Cite

show abstract

“…1 − 2 is estimated through the direct measurement of the active suspension actuator displacement, and consideration of the suspension installation ratio. ̇1 −̇2 is obtained through differentiation of 1 − 2 by using the hybrid smooth derivative method [24]. represents the influence of the unknown disturbances and ̇, and is neglected during the e-MPC design.…”

Section: -Model For Control System Designmentioning

confidence: 99%

Explicit model predictive control of an active suspension system

et al. 2018

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Model predictive control (MPC) is increasingly finding its way into industrial applications, due to its superior tracking performance and ability to formally handle system constraints. However, the real-time capability problems related to the conventional implicit model predictive control (i-MPC) framework are well known, especially when targeting low-cost electronic control units (ECUs) for high bandwidth systems, such as automotive active suspensions, which are the topic of this paper. In this context, to overcome the real-time implementation issues of i-MPC, this study proposes explicit model predictive control (e-MPC), which solves the optimization problem off-line, via multi-parametric quadratic programming (mp-QP). e-MPC reduces the on-line algorithm to a function evaluation, which replaces the computationally demanding on-line solution of the quadratic programming (QP) problem. An e-MPC based suspension controller is designed and experimentally validated for a case study Sport Utility Vehicle (SUV), equipped with the active ACOCAR suspension system from the Tenneco Monroe product family. The target is to improve ride comfort in the frequency range of primary ride (< 4 Hz), without affecting the performance at higher frequencies. The proposed e-MPC implementations reduce the root mean square (RMS) value of the sprung mass acceleration by > 40% compared to the passive vehicle set-up for frequencies < 4 Hz, and by up to 19% compared to the same vehicle with a skyhook controller on the 0-100 Hz frequency range.

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“…In many modern vehicles, particularly those with active suspension, changes in suspension are reported using linear potentiometers [17, 33] or similar sensors (such as LVDT [34]), which are calibrated to report the height of reference points on the vehicle from the ground (e.g., the height of the top of the wheel arch from the ground). Traditionally, wheel‐based odometry was only used to give a planar motion estimate of the vehicle, providing an odometry estimate with only three degrees of freedom.…”

Section: Introductionmentioning

confidence: 99%

2.5D vehicle odometry estimation

Eising

Pereira²,

Horgan

et al. 2021

IET Intelligent Trans Sys

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It is well understood that in ADAS applications, a good estimate of the pose of the vehicle is required. This paper proposes a metaphorically named 2.5D odometry, whereby the planar odometry derived from the yaw rate sensor and four wheel speed sensors is augmented by a linear model of suspension. While the core of the planar odometry is a yaw rate model that is already understood in the literature, this is augmented by fitting a quadratic to the incoming signals, enabling interpolation, extrapolation, and a finer integration of the vehicle position. It is shown, by experimental results with a DGPS/IMU reference, that this model provides highly accurate odometry estimates, compared with existing methods. Utilising sensors that return the change in height of vehicle reference points with changing suspension configurations, a planar model of the vehicle suspension is defined, thus augmenting the odometry model. An experimental framework and evaluations criteria is presented by which the goodness of the odometry is evaluated and compared with existing methods. This odometry model has been designed to support low‐speed surround‐view camera systems that are well‐known. Thus, some application results that show a performance boost for viewing and computer vision applications using the proposed odometry are presented.

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A Novel Robust Optimal Active Control of Vehicle Suspension Systems

Cited by 8 publications

References 10 publications

Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview

Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview

Explicit model predictive control of an active suspension system

2.5D vehicle odometry estimation

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