Observer based semi-active suspension control applied to a light commercial vehicle

Sankaranarayanan, V.; Emekli, Mustafa Engin; Giivenc, B.A.; Giivenc, L.; Oztiirk, E.S.; Ersolmaz, S.S.; Eyol, I. Erhan; Sinai, Massoud

doi:10.1109/aim.2007.4412539

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…The full car suspension model as in [Sankaranarayanan et al, 2007], consists of a sprung mass (actual car body), three unsprung masses in which two are front tires and a single rear axle which connects the two rear tires. The whole system has 7 degrees of freedom and those are vertical motion of the sprung mass z, roll motion of the sprung mass φ, pitch motion of the sprung mass θ, vertical motion of the two front unsprung masses z 11 , z 12 , vertical motion of the rear unsprung mass z ur and the roll motion of the rear unsprung mass φ ur .…”

Section: Full Car Suspension Modelmentioning

confidence: 99%

Vehicle Chassis Control Using Adaptive Semi-Active Suspension

Sankaranarayanan

Oncu

Özcan

et al. 2008

IFAC Proceedings Volumes

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Section: Full Car Suspension Modelmentioning

confidence: 99%

Vehicle Chassis Control Using Adaptive Semi-Active Suspension

Sankaranarayanan

Oncu

Özcan

et al. 2008

IFAC Proceedings Volumes

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“…The Kalman filtering observer utilizes the recursive calculation in time domain, it estimates the current states based on the current measured inputs and the states estimated at the last moment, it is necessary and important to find out the covariance matrix of both the system process noise and the measurement noise during the recursive calculation process, to make sure the estimation accuracy could be guaranteed. [6][7][8] The Luenberger observer is designed based on the modern control theory, through the pole assignment of the error dynamic equation, the estimation error could converge to zero, so that the unknown states could be estimated. Its estimation accuracy depends on the correct error dynamic equation and a proper pole assignment.…”

Section: Introductionmentioning

confidence: 99%

State estimation and damping control for unmanned ground vehicles with semi-active suspension system

Wang

Chen

Ren

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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The new type of transportation based on intelligent driverless vehicles will bring great changes to people’s travel modes and put forward higher requirements for the ride comfortability of vehicles. This paper presents a new observing algorithm to estimate the suspension states in real time and cooperate with sliding mode controller to improve the ride comfortability. First, the nonlinear model of an air suspension system equipped with a continuously controllable damper is described in detail. Then, this nonlinear suspension model is linearized precisely based on the differential geometry theory; a linear Kalman filter observer is implemented for this linearized model; through the coordinate reverse transformation, the designed linear observer is transformed into a nonlinear one, which will be suitable for the original nonlinear system, so that, the proposed state observer can estimate all the states of the nonlinear quarter car suspension system. Then, in this nonlinear suspension system, a model reference sliding mode controller is designed to continuously control the damping force to improve the ride comfortability. Finally, the effectiveness and advantage of the proposed feedback linearization Kalman observer is illustrated by comparing with a traditional extended Kalman filter observer. The simulation research shows that the proposed feedback linearization observer enjoys a better estimation accuracy, higher operation efficiency, and greater control performance while cooperating with the sliding mode controller in ride comfortability control.

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“…Sankaranarayanan et al . designed an observer to estimate the acceleration of a vehicle, and the sprung mass and non‐sprung mass velocities were calculated by the measured acceleration values from this observer . However, the structure of this observer is too simple to meet the increasing demand of automobile active suspension.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Sliding Mode Control for Active Suspension System with Proportional Differential Sliding Mode Observer

Lin

2018

Asian Journal of Control

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In this paper, a generalized augmented transformation is considered for the quarter active suspension system with uncertainties. Specifically, the model uncertainties are converted to the augmented states and a new proportion differential sliding mode observer is used to estimate state variables and model uncertainties. A differential geometric method is applied to linearize the nonlinear suspension model. In order to weaken the vibration effect of sliding mode control force and reduce energy consumption, a fuzzy sliding mode controller is designed for the active suspension system and the fuzzy controller is applied to adjust switching control gain according to the reaching condition of sliding mode surface. The simulations are conducted to illustrate the effectiveness and advantages of this proposed observer and control strategy.

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Observer based semi-active suspension control applied to a light commercial vehicle

Cited by 6 publications

References 11 publications

Vehicle Chassis Control Using Adaptive Semi-Active Suspension

Vehicle Chassis Control Using Adaptive Semi-Active Suspension

State estimation and damping control for unmanned ground vehicles with semi-active suspension system

Fuzzy Sliding Mode Control for Active Suspension System with Proportional Differential Sliding Mode Observer

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