Prediction of Clearance Vibration for Intelligent Vehicles Motion Control

Yunhe Zhang, Faping Zhang, Wuhong Wang, Fanjun Meng, Dashun Zhang and Haixun Wang
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Yunhe Zhang: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Faping Zhang: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Wuhong Wang: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Fanjun Meng: School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130013, China
Dashun Zhang: The 55 Research Institute of China North Industries Group Corporation Limited, Changchun 130012, China
Haixun Wang: The 55 Research Institute of China North Industries Group Corporation Limited, Changchun 130012, China

Sustainability, 2022, vol. 14, issue 11, 1-17

Abstract: Motion control analysis should consider the system’s uncertainty to ensure the intelligent vehicle’s autonomy. The clearance structure of the transmission shaft is modeled as a cantilever beam with double clearance to predict the clearance vibration for mitigating the nonlinearity. Based on the Kelvin–Voigt collision model, a clearance model was developed using time-varying parameters identified by the wavelet transform. Comparing the frequency response functions (FRF) of the initial model with constant parameters and the updated model with time-varying parameters, the experimental results from the updated model indicate that the modal assurance criterion (MAC) is increased by 42.92%, 31.08%, 38.97%, and 50.74% in the first-four order. Cross-signature assurance criteria (CSAC) and cross-signature scale factor (CSF) have been increased by 6.55% and 12.37%. The control method based on the clearance model has been verified. In the case of 120 km/h, compared with model-predictive control (MPC) and sliding mode control (SMC), the peak of the lateral position error was reduced by 35.7% and 14.3%, and the peak of the heading error was reduced by 50% and 15.6%.

Keywords: intelligent vehicle; motion control; clearance nonlinearity; parameter identification; frequency response function (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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