Nonlinear and filter based estimation for vehicle suspension control

Koch, Guido; Kloiber, Tobias; Lohmann, Boris

doi:10.1109/cdc.2010.5718052

Cited by 25 publications

(16 citation statements)

References 8 publications

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“…In these works, a linear suspension representation is extended by a nonlinear damper model, which generates a fictitious force input based on the damper relative velocity estimate. In Koch et al (2010b), the estimation accuracy of state variables is further enhanced by running several Kalman filters of this kind in parallel.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear state estimation for suspension control applications: a Takagi-Sugeno Kalman filtering approach

Pletschen

Diepold

2017

Control Engineering Practice

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Section: Introductionmentioning

confidence: 99%

“…With regard to the suspension control problem, this concept has been adopted e.g. in Lindgärde (2002), Koch et al (2010b) and Delvecchio et al (2010). In these works, a linear suspension representation is extended by a nonlinear damper model, which generates a fictitious force input based on the damper relative velocity estimate.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear state estimation for suspension control applications: a Takagi-Sugeno Kalman filtering approach

Pletschen

Diepold

2017

Control Engineering Practice

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“…However, the damper relative velocityż cw =ż c − z w is usually needed for the control of the semi-active damper. Thus, it is generated from the available measurement signals by applying a filter-based velocity estimation approach described in detail in Koch et al (2010). The resulting velocity estimate has no phase delay and is of high estimation accuracy, such that the extended measurement…”

Section: Suspension Model In Ts System Representationmentioning

confidence: 99%

“…General extensions of Kalman filter theory to nonlinear systems are the Extended (EKF) or the Unscented Kalman filter (UKF) variants, see e. g. Zarchan and Musoff (2005) and Simon (2006). Suspension control applications of these concepts may be found in Koch et al (2010) and Fleps-Dezasse and Brembeck (2013). However, these approaches are computationally intensive.…”

Section: Introductionmentioning

confidence: 99%

“…However, when passive or semi-active dampers are part of the suspension configuration, their significant nonlinear force characteristics cannot be neglected without substantial deterioration in estimation quality. Thus, a frequently employed strategy is to extend a linear suspension representation by a nonlinear damper model that generates a fictitious force input based on the estimated damper relative velocity, see e. g. Lindgärde (2002), Koch et al (2010). General extensions of Kalman filter theory to nonlinear systems are the Extended (EKF) or the Unscented Kalman filter (UKF) variants, see e. g. Zarchan and Musoff (2005) and Simon (2006).…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear State Estimation in Suspension Control Based on Takagi-Sugeno Model

Pletschen

Badur

2014

IFAC Proceedings Volumes

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We present a new nonlinear state estimation approach based on Kalman filter theory and Takagi-Sugeno (TS) modeling for an active vehicle suspension application in this paper. The nonlinear state equations of a so-called hybrid suspension configuration, which result from nonlinear spring and damping characteristics, are exactly represented by means of a continuoustime TS system, i. e. a convex combination of local linear state space models. We derive observer gain matrices for each linear subsystem on the basis of standard Kalman filter theory, before we construct the global observer for the overall nonlinear system. Convergence of the global observer is ensured in terms of linear matrix inequality conditions. We then study the estimation performance of the TS Kalman filter in simulations and experiments on a hybrid quarter-car test rig using a measured road profile as disturbance input. The approach achieves a high estimation accuracy of well above 90 % in the simulation and 70 − 90 % in the experiments.

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A nonlinear parameter varying observer for real‐time damper force estimation of an automotive electro‐rheological suspension system

Pham

Sename

Dugard

2021

Intl J Robust & Nonlinear

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This paper proposes a nonlinear parameter varying (NLPV) observer to estimate, in real-time, the damper force of an electrorheological (ER) damper in a road vehicle suspension system. The objectives are to minimize the effects of bounded unknown road profile disturbances and measurement noises on the estimation errors in the  ∞ framework. Furthermore, the nonlinearity coming from the damper model (and considered in the observer formulation) is handled through a Lipschitz condition. The observer inputs are data given by two low-cost sensors (two accelerometers data from the sprung mass and the unsprung mass). For performance assessment, the observer is implemented on a 1/5-scaled real vehicle testbench of GIPSA-lab, namely INOVE (see www.gipsa-lab.fr/projet/inove). Both simulation and experimental results demonstrate the effectiveness of the proposed observer in terms of the ability to estimate the damper force in real-time and to minimize the effects of measurement noises and road disturbances.

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Nonlinear and filter based estimation for vehicle suspension control

Cited by 25 publications

References 8 publications

Nonlinear state estimation for suspension control applications: a Takagi-Sugeno Kalman filtering approach

Nonlinear state estimation for suspension control applications: a Takagi-Sugeno Kalman filtering approach

Nonlinear State Estimation in Suspension Control Based on Takagi-Sugeno Model

A nonlinear parameter varying observer for real‐time damper force estimation of an automotive electro‐rheological suspension system

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