Vehicle Sideslip Angle Estimation Based on Radial Basis Neural Network and Unscented Kalman Filter Algorithm
Most existing ESC (electronic stability control) and ADS (auto drive system) stability controls rely on the measurement of yaw rate and sideslip angle. However, the existing sensors are too expensive, which is one of the factors that makes it difficult to measure the side slip angle of vehicles dire...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
|
| Series: | Actuators |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-0825/12/10/371 |
| _version_ | 1797575086963687424 |
|---|---|
| author | Chuanwei Zhang Yansong Feng Jianlong Wang Peng Gao Peilin Qin |
| author_facet | Chuanwei Zhang Yansong Feng Jianlong Wang Peng Gao Peilin Qin |
| author_sort | Chuanwei Zhang |
| collection | DOAJ |
| description | Most existing ESC (electronic stability control) and ADS (auto drive system) stability controls rely on the measurement of yaw rate and sideslip angle. However, the existing sensors are too expensive, which is one of the factors that makes it difficult to measure the side slip angle of vehicles directly. Therefore, the estimation of sideslip angle has been extensively discussed in the relevant literature. Accurate modeling is complicated by the fact that vehicles are highly nonlinear. This article combines a radial basis function neural network with an unscented Kalman filter to propose a new sideslip angle estimation method for controlling the dynamic behavior of vehicles. Considering the influence of input data type and sensor ease of measurement factors on the results, a two-degrees-of-freedom vehicle nonlinear dynamic model was established, and a radial basis function neural network estimation algorithm was designed. In order to reduce the impact of noise and improve the reliability of the algorithm, the neural network algorithm was combined with the Kalman filter. The information collected from low-cost sensors for actual vehicle operation (longitudinal vehicle speed, steering wheel angle, yaw rate, lateral acceleration) was trained using a radial basis function neural network to obtain a “pseudo slip angle”. The “pseudo slip angle”, yaw rate, and lateral acceleration are input as observations of the Kalman filter. The sideslip angle obtained from different observation methods was compared with the values provided by the Carsim 2020. The experiment shows that the sideslip angle estimator based on the radial basis function neural network and unscented Kalman filter achieves the optimal effect. |
| first_indexed | 2024-03-10T21:32:21Z |
| format | Article |
| id | doaj.art-7bc4e71655a04bb18276d729adb6dd8f |
| institution | Directory Open Access Journal |
| issn | 2076-0825 |
| language | English |
| last_indexed | 2024-03-10T21:32:21Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj.art-7bc4e71655a04bb18276d729adb6dd8f2023-11-19T15:16:06ZengMDPI AGActuators2076-08252023-09-01121037110.3390/act12100371Vehicle Sideslip Angle Estimation Based on Radial Basis Neural Network and Unscented Kalman Filter AlgorithmChuanwei Zhang0Yansong Feng1Jianlong Wang2Peng Gao3Peilin Qin4College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaMost existing ESC (electronic stability control) and ADS (auto drive system) stability controls rely on the measurement of yaw rate and sideslip angle. However, the existing sensors are too expensive, which is one of the factors that makes it difficult to measure the side slip angle of vehicles directly. Therefore, the estimation of sideslip angle has been extensively discussed in the relevant literature. Accurate modeling is complicated by the fact that vehicles are highly nonlinear. This article combines a radial basis function neural network with an unscented Kalman filter to propose a new sideslip angle estimation method for controlling the dynamic behavior of vehicles. Considering the influence of input data type and sensor ease of measurement factors on the results, a two-degrees-of-freedom vehicle nonlinear dynamic model was established, and a radial basis function neural network estimation algorithm was designed. In order to reduce the impact of noise and improve the reliability of the algorithm, the neural network algorithm was combined with the Kalman filter. The information collected from low-cost sensors for actual vehicle operation (longitudinal vehicle speed, steering wheel angle, yaw rate, lateral acceleration) was trained using a radial basis function neural network to obtain a “pseudo slip angle”. The “pseudo slip angle”, yaw rate, and lateral acceleration are input as observations of the Kalman filter. The sideslip angle obtained from different observation methods was compared with the values provided by the Carsim 2020. The experiment shows that the sideslip angle estimator based on the radial basis function neural network and unscented Kalman filter achieves the optimal effect.https://www.mdpi.com/2076-0825/12/10/371sideslip angle estimationneural networkKalman filtersensor fusion |
| spellingShingle | Chuanwei Zhang Yansong Feng Jianlong Wang Peng Gao Peilin Qin Vehicle Sideslip Angle Estimation Based on Radial Basis Neural Network and Unscented Kalman Filter Algorithm Actuators sideslip angle estimation neural network Kalman filter sensor fusion |
| title | Vehicle Sideslip Angle Estimation Based on Radial Basis Neural Network and Unscented Kalman Filter Algorithm |
| title_full | Vehicle Sideslip Angle Estimation Based on Radial Basis Neural Network and Unscented Kalman Filter Algorithm |
| title_fullStr | Vehicle Sideslip Angle Estimation Based on Radial Basis Neural Network and Unscented Kalman Filter Algorithm |
| title_full_unstemmed | Vehicle Sideslip Angle Estimation Based on Radial Basis Neural Network and Unscented Kalman Filter Algorithm |
| title_short | Vehicle Sideslip Angle Estimation Based on Radial Basis Neural Network and Unscented Kalman Filter Algorithm |
| title_sort | vehicle sideslip angle estimation based on radial basis neural network and unscented kalman filter algorithm |
| topic | sideslip angle estimation neural network Kalman filter sensor fusion |
| url | https://www.mdpi.com/2076-0825/12/10/371 |
| work_keys_str_mv | AT chuanweizhang vehiclesideslipangleestimationbasedonradialbasisneuralnetworkandunscentedkalmanfilteralgorithm AT yansongfeng vehiclesideslipangleestimationbasedonradialbasisneuralnetworkandunscentedkalmanfilteralgorithm AT jianlongwang vehiclesideslipangleestimationbasedonradialbasisneuralnetworkandunscentedkalmanfilteralgorithm AT penggao vehiclesideslipangleestimationbasedonradialbasisneuralnetworkandunscentedkalmanfilteralgorithm AT peilinqin vehiclesideslipangleestimationbasedonradialbasisneuralnetworkandunscentedkalmanfilteralgorithm |