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

Gudarzi, Mohammad; Oveisi, Atta

doi:10.1260/0263-0923.33.3.317

Cited by 27 publications

(19 citation statements)

References 28 publications

(52 reference statements)

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“…1. The detailed equations of motion for this half-car model, which are bounce, pitch and each unsprung motions are as given in [21]. The schematic model of the driver's biodynamic consisting of the lumped human linear seat model is illustrated in Fig.…”

Section: Dynamic Modelingmentioning

confidence: 99%

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Reliable Robust Controller for Half-Car Active Suspension Systems Based on Human-Body Dynamics

Gudarzi

2016

FU Mech Eng

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The paper investigates a non-fragile robust control strategy for a half-car active suspension system considering human-body dynamics. A 4-DoF uncertain vibration model of the driver’s body is combined with the car’s model in order to make the controller design procedure more accurate. The desired controller is obtained by solving a linear matrix inequality formulation. Then the performance of the active suspension system with the designed controller is compared to the passive one in both frequency and time domain simulations. Finally, the effect of the controller gain variations on the closed-loop system performance is investigated numerically.

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Section: Dynamic Modelingmentioning

confidence: 99%

“…2. Considered model for the driver is based on a 4-DOF human body presented in [21]; the related equations could be found there as well. By assuming that the passenger is sitting in the front seat, the obtained dynamic equations by adding structured uncertainties can be rearranged as: where 9 is the displacement and rotation vector,…”

Section: Dynamic Modelingmentioning

confidence: 99%

Reliable Robust Controller for Half-Car Active Suspension Systems Based on Human-Body Dynamics

Gudarzi

2016

FU Mech Eng

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“…In addition, W dist , W Rdist , W n , and W Ri (i = 1, 2, 3, 4) are the frequency dependent norm-bounded weighting function for external input disturbance signal, the multiplicative uncertainty of disturbance, a high pass filter representing the activation frequency of the noise signal, and weighting function of the actuation uncertainty, respectively. Also, as a standard multiplicative representation, it is assumed that || dist ||   1 and || act i ||   1, i = 1,...,4 [36]. For the sake of applying the structured singular value theory to the plant under study, the signal configuration in Fig.…”

Section: µ-Synthesismentioning

confidence: 99%

Mu-Synthesis Based Active Robust Vibration Control of an Mri Inlet

Oveisi

Nestorović

2016

FU Mech Eng

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In this paper, a robust control technique based on μ-synthesis is employed in order to investigate the vibration control of a funnel-shaped structure that is used as the inlet of a magnetic resonance imaging (MRI) device. MRI devices are widely subjected to the vibration of the magnetic gradient coil which then propagates to acoustic noise and leads to a series of clinical and mechanical problems. In order to address this issue and as a part of noise cancellation study in MRI devices, distributed piezo-transducers are bounded on the top surface of the funnel as functional sensor/actuator modules. Then, a reduced order linear time-invariant (LTI) model of the piezolaminated structure in the state-space representation is estimated by means of a predictive error minimization (PEM) algorithm as a subspace identification method based on the trust-region-reflective technique. The reduced order model is expanded by the introduction of appropriate frequency-dependent weighting functions that address the unmodeled dynamics and the augmented multiplicative modeling uncertainties of the system. Then, the standard D-K iteration algorithm as an output-feedback control method is used based on the nominal model with the subordinate uncertainty elements from the previous step. Finally, the proposed control system implemented experimentally on the real structure is to evaluate the robust vibration attenuation performance of the closed-loop system.

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“…rr rf (20) where υ is the vehicle speed. Now, the state space equations are obtained by substituting the state variables from eqns.…”

Section: Suspension Modelmentioning

confidence: 99%

Non-Linear Predictive Control of Multi-Input Multi-Output Vehicle Suspension System

Malekshahi

Mirzaei

Aghasizade

2015

Journal of Low Frequency Noise, Vibration and Active Control

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A new predictive control approach is employed to control a non-linear 4 degrees of freedom vehicle suspension model including pitch and bounce motions. The front and rear suspension actuator forces are considered as control inputs. These are determined by minimizing a performance index defined as a weighted combination of conflicting objectives, namely ride comfort and road holding. In this way, the suspension deflection and the control forces should be restricted to admissible practical ranges. In this paper, an optimization process is used to develop a multi-input multi-output (MIMO) non-linear control law in a closed-form which is mathematically tractable and suitable for implementation. To investigate special features of the derived control law, the closed-loop system dynamics are first evaluated analytically and then the corresponding simulation results are presented. The results show that a compromise between mentioned objectives and control energy, even in the presence of uncertainties, can be easily made by regulating the control weighting factors.

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

Cited by 27 publications

References 28 publications

Reliable Robust Controller for Half-Car Active Suspension Systems Based on Human-Body Dynamics

Reliable Robust Controller for Half-Car Active Suspension Systems Based on Human-Body Dynamics

Mu-Synthesis Based Active Robust Vibration Control of an Mri Inlet

Non-Linear Predictive Control of Multi-Input Multi-Output Vehicle Suspension System

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