Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle Estimation
This study is targeted at the key state parameters of vehicle stability controllers, the controlled vehicle model, and the nonlinearity and uncertainty of external disturbance. An adaptive double-layer unscented Kalman filter (ADUKF) is used to compute the sideslip angle, and a vehicle stability con...
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doaj-b282568b5c8d4faebaf8fba1c562e4af2021-01-30T00:01:27ZengMDPI AGApplied Sciences2076-34172021-01-01111231123110.3390/app11031231Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle EstimationZhenzhao Zhang0Liang Chu1Jiaxu Zhang2Chong Guo3Jing Li4College of Automotive Engineering, Jilin University, Changchun 130022, ChinaCollege of Automotive Engineering, Jilin University, Changchun 130022, ChinaCollege of Automotive Engineering, Jilin University, Changchun 130022, ChinaCollege of Automotive Engineering, Jilin University, Changchun 130022, ChinaCollege of Automotive Engineering, Jilin University, Changchun 130022, ChinaThis study is targeted at the key state parameters of vehicle stability controllers, the controlled vehicle model, and the nonlinearity and uncertainty of external disturbance. An adaptive double-layer unscented Kalman filter (ADUKF) is used to compute the sideslip angle, and a vehicle stability control algorithm adaptive fuzzy radial basis function neural network sliding mode control (AFRBF-SMC) is proposed. Since the sideslip angle cannot be directly determined, a 7-degrees-of-freedom (DOF) nonlinear vehicle dynamic model is established and combined with ADUKF to estimate the sideslip angle. After that, a vehicle stability sliding mode controller is designed and used to trace the ideal values of the vehicle stability parameters. To handle the severe system vibration due to the large robustness coefficient in the sliding mode controller, we use a fuzzy radial basis function neural network (FRBFNN) algorithm to approximate the uncertain disturbance of the system. Then the adaptive rate of the system is solved using the Lyapunov algorithm, and the systemic stability and convergence of this algorithm are validated. Finally, the controlling algorithm is verified through joint simulation on MATLAB/Simulink-Carsim. ADUKF can estimate the sideslip angle with high precision. The AFRBF-SMC vehicle stability controller performs well with high precision and low vibration and can ensure the driving stability of vehicles.https://www.mdpi.com/2076-3417/11/3/1231vehicle stability controlsideslip angle estimationadaptive double-layer unscented Kalman filtersliding mode controladaptive fuzzy radial basis function neural network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhenzhao Zhang Liang Chu Jiaxu Zhang Chong Guo Jing Li |
spellingShingle |
Zhenzhao Zhang Liang Chu Jiaxu Zhang Chong Guo Jing Li Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle Estimation Applied Sciences vehicle stability control sideslip angle estimation adaptive double-layer unscented Kalman filter sliding mode control adaptive fuzzy radial basis function neural network |
author_facet |
Zhenzhao Zhang Liang Chu Jiaxu Zhang Chong Guo Jing Li |
author_sort |
Zhenzhao Zhang |
title |
Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle Estimation |
title_short |
Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle Estimation |
title_full |
Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle Estimation |
title_fullStr |
Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle Estimation |
title_full_unstemmed |
Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle Estimation |
title_sort |
design of vehicle stability controller based on fuzzy radial basis neural network sliding mode theory with sideslip angle estimation |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-01-01 |
description |
This study is targeted at the key state parameters of vehicle stability controllers, the controlled vehicle model, and the nonlinearity and uncertainty of external disturbance. An adaptive double-layer unscented Kalman filter (ADUKF) is used to compute the sideslip angle, and a vehicle stability control algorithm adaptive fuzzy radial basis function neural network sliding mode control (AFRBF-SMC) is proposed. Since the sideslip angle cannot be directly determined, a 7-degrees-of-freedom (DOF) nonlinear vehicle dynamic model is established and combined with ADUKF to estimate the sideslip angle. After that, a vehicle stability sliding mode controller is designed and used to trace the ideal values of the vehicle stability parameters. To handle the severe system vibration due to the large robustness coefficient in the sliding mode controller, we use a fuzzy radial basis function neural network (FRBFNN) algorithm to approximate the uncertain disturbance of the system. Then the adaptive rate of the system is solved using the Lyapunov algorithm, and the systemic stability and convergence of this algorithm are validated. Finally, the controlling algorithm is verified through joint simulation on MATLAB/Simulink-Carsim. ADUKF can estimate the sideslip angle with high precision. The AFRBF-SMC vehicle stability controller performs well with high precision and low vibration and can ensure the driving stability of vehicles. |
topic |
vehicle stability control sideslip angle estimation adaptive double-layer unscented Kalman filter sliding mode control adaptive fuzzy radial basis function neural network |
url |
https://www.mdpi.com/2076-3417/11/3/1231 |
work_keys_str_mv |
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