Analysis of dynamic behavior of vehicle shimmy system with stochastic clearance

Wei, Heng; Lu, Jianwei; Junzhao, Jiang

doi:10.3103/s0025654422010058

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Pacejka analyzed the shimmy phenomenon considering the tyre characteristics, coulomb friction in the king-pin bearings, and clearance in the wheel bearings [8]. Lu et al built a 6-DOF dynamic model of a vehicle shimmy system considering the clearance in [9] and stochastic clearance in [10]. Zhuravlev et.al.…”

Section: Introductionmentioning

confidence: 99%

Active Shimmy Control Method for Driverless Electric Vehicle Considering Unknown Sensor Measurement Error via Sampled-Data Output Feedback

Meng,

Zhao,

Liu

et al. 2024

IEEE Access

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Compared with traditional vehicles, driverless electric vehicles equipped with electric wheels are more likely to generate the front wheel shimmy phenomenon, which affects driving comfort and safety. It is necessary to study an active control approach to alleviate or even eliminate the shimmy phenomenon. The current active shimmy control methods do not consider the sensor measurement error. However, the sensor measurement error worsens the effect of an active shimmy control method in practice. For addressing this issue, this paper proposed a novel active shimmy control method considering unknown sensor measurement error via sampled-data output feedback. Firstly, a four-degree-of-freedom (4-DOF) shimmy model of the front electric wheel is built. The dynamic functions of the model are obtained via the Lagrange theorem. Based on the dynamic functions, a shimmy control system with unknown sensor measurement error and nonlinearities is proposed. Then a sampled-data output feedback control (SOFC) method is proposed for the shimmy control system. By selecting an appropriate domination gain, the designed controller can globally asymptotically stabilize the system even in the presence of unknown measurement error. Finally, the 4-DOF shimmy model and the SOFC method for the shimmy system are verified via simulation. The simulation results show that the SOFC method can address the sensor measurement error issue.

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Section: Introductionmentioning

confidence: 99%

Active Shimmy Control Method for Driverless Electric Vehicle Considering Unknown Sensor Measurement Error via Sampled-Data Output Feedback

Meng,

Zhao,

Liu

et al. 2024

IEEE Access

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From deterministic to stochastic: limits of extracting bifurcation diagrams for noisy bistable oscillators with the control-based continuation method

Sykora,

Beregi

2024

Nonlinear Dyn

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Noise limits the information that can be experimentally extracted from dynamical systems. In this study, we review the Control-based Continuation (CBC) approach, which is commonly used for experimental characterisation of nonlinear systems with coexisting stable and unstable steady states. The CBC technique, however, uses a deterministic framework, whereas in practice, almost all measurements are subject to some level of random perturbation, and the underlying dynamical system is inherently noisy. In order to discover what the CBC is capable of extracting from inherently noisy experiments, we study the Hopf normal form with quintic terms with additive noise. The bifurcation diagram of the deterministic core of this system is well-known, therefore the discrepancies introduced by noise can be easily assessed. First, we utilise the Step-Matrix Multiplication based Path Integral (SMM-PI) method to approximate the system’s steady state probability density function (PDF) for different intensity noise perturbations. We associate the local extrema of the resulting PDFs with limit cycles, and compare the resulting bifurcation diagram to those captured by CBC. We show that CBC estimates the bifurcation diagram of the noisy system well for noise intensities varying from small to moderate, and in practice, the amplitudes provided by CBC may be accepted as a ’best guess’ proxy for the vibration amplitudes characteristic to the near periodic solutions in a wide range of experiments.

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Distribution Analysis of Multiple Limit Cycles of Vehicle Shimmy System with Consideration of Joint Clearances

et al. 2022

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Analysis of dynamic behavior of vehicle shimmy system with stochastic clearance

Cited by 3 publications

References 19 publications

Active Shimmy Control Method for Driverless Electric Vehicle Considering Unknown Sensor Measurement Error via Sampled-Data Output Feedback

Active Shimmy Control Method for Driverless Electric Vehicle Considering Unknown Sensor Measurement Error via Sampled-Data Output Feedback

From deterministic to stochastic: limits of extracting bifurcation diagrams for noisy bistable oscillators with the control-based continuation method

Distribution Analysis of Multiple Limit Cycles of Vehicle Shimmy System with Consideration of Joint Clearances

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