An adaptive pneumatic suspension based on the estimation of the excitation frequency

Nieto, A.J.; Morales, A.L.; Trapero, Juan R.; Chicharro, J.M.; Pintado, P.

doi:10.1016/j.jsv.2010.11.009

Cited by 38 publications

(21 citation statements)

References 30 publications

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“…By assuming that feedback (measurement signal) to be vertical acceleration of the vehicle center of gravity, then one has, 8) where C and D are given in Appendix A. Before designing the output feedback controller for the half-car model, we need to specify controlled values as following [24,27], Ride comfort: Ride comfort can be generally quantified by vertical acceleration of the passenger's head, thus, it is chosen as the first control output, i.e.…”

Section: Vehicle-driver Modelmentioning

confidence: 99%

See 1 more Smart Citation

Robust Control for Ride Comfort Improvement of an Active Suspension System considering Uncertain Driver's Biodynamics

Gudarzi

Oveisi

2014

Journal of Low Frequency Noise, Vibration and Active Control

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This paper presents a robust output feedback control design for a half-car active suspension system by considering driver's biodynamics. Because of different kinds of passengers there is a wide range of variations in biodynamics' parameters, and an appropriate robust control design approach, μ-synthesis approach, is used to tackle these parametric uncertainties. The performance of active suspension system with designed controller is compared with the open loop one in both frequency and time domain simulations. The results show that μ-synthesis based controller achieves great performance in ride comfort. In addition, suspension deflections, road holding and actuators force remain in reasonable regions. Finally, analysis of robust performance indicating that the μ-synthesis controller remains stable in a wide range of frequency domain despite parametric uncertainties, which is also validated by a specific case in this paper.

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Section: Vehicle-driver Modelmentioning

confidence: 99%

“…For the state space representation of Vehicle-Driver Model which is given in Equations (7) and (8), the elements of A are as follow:…”

Section: Appendix Amentioning

confidence: 99%

Robust Control for Ride Comfort Improvement of an Active Suspension System considering Uncertain Driver's Biodynamics

Gudarzi

Oveisi

2014

Journal of Low Frequency Noise, Vibration and Active Control

View full text Add to dashboard Cite

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“…Active, semiactive, and adaptive suspensions may improve the overall performance in comparison to passive configurations. To this end, several devices are used, such as pneumatic systems, 13‐15 magnetorheological (MR) dampers, 16‐18 variable orifice dampers, 19,20 hydropneumatic spring‐dampers, 21,22 or electro‐hydraulic actuators 23 . All these devices are used in control strategies which can be divided in two groups, depending on the availability of road profile information.…”

Section: Introductionmentioning

confidence: 99%

Pointwise‐constrained optimal control of a semiactive vehicle suspension

Palomares

Bellido

Morales

et al. 2020

Optim Control Appl Methods

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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.

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“…The main advantage of EAS system is the improvement of the vehicle dynamic performance, including the driving comfort, the handling stability and the fuel economy [5,6]. As a well-established vehicle suspension system, EAS system has been researched in depth and been used in a large number of vibration isolation occasions, such as the vehicle seat suspension and the railway vehicle suspension [7][8][9][10]. However, the vehicle height and leveling adjustment control problem of an EAS system still poses difficulties for researchers, which are reflected in the previous academic reports on this theme.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Sliding Mode Control for the Vehicle Height and Leveling Adjustment System of an Electronic Air Suspension

Sun

Cai

Yuan

et al. 2018

Chin. J. Mech. Eng.

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The accurate control for the vehicle height and leveling adjustment system of an electronic air suspension (EAS) still is a challenging problem that has not been effectively solved in prior researches. This paper proposes a new adaptive controller to control the vehicle height and to adjust the roll and pitch angles of the vehicle body (leveling control) during the vehicle height adjustment procedures by an EAS system. A nonlinear mechanism model of the full-car vehicle height adjustment system is established to reflect the system dynamic behaviors and to derive the system optimal control law. To deal with the nonlinear characters in the vehicle height and leveling adjustment processes, the nonlinear system model is globally linearized through the state feedback method. On this basis, a fuzzy sliding mode controller (FSMC) is designed to improve the control accuracy of the vehicle height adjustment and to reduce the peak values of the roll and pitch angles of the vehicle body. To verify the effectiveness of the proposed control method more accurately, the full-car EAS system model programmed using AMESim is also given. Then, the co-simulation study of the FSMC performance can be conducted. Finally, actual vehicle tests are performed with a city bus, and the test results illustrate that the vehicle height adjustment performance is effectively guaranteed by the FSMC, and the peak values of the roll and pitch angles of the vehicle body during the vehicle height adjustment procedures are also reduced significantly. This research proposes an effective control methodology for the vehicle height and leveling adjustment system of an EAS, which provides a favorable control performance for the system.

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An adaptive pneumatic suspension based on the estimation of the excitation frequency

Cited by 38 publications

References 30 publications

Robust Control for Ride Comfort Improvement of an Active Suspension System considering Uncertain Driver's Biodynamics

Robust Control for Ride Comfort Improvement of an Active Suspension System considering Uncertain Driver's Biodynamics

Pointwise‐constrained optimal control of a semiactive vehicle suspension

Fuzzy Sliding Mode Control for the Vehicle Height and Leveling Adjustment System of an Electronic Air Suspension

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