Abstract
The distributed drive arrangement form has better potential for cooperative control of dynamics, but this drive arrangement form increases the parameter acquisition workload of the control system and increases the difficulty of vehicle control accordingly. In order to observe the vehicle motion state accurately and in real-time, while reducing the effect of uncertainty in noise statistical information, the vehicle state observer is designed based on interacting multiple model theory with square root cubature Kalman filter (IMM-SCKF). The IMM-SCKF algorithm sub-model considers different state noise and measurement noise, and the introduction of the square root filter reduces the complexity of the algorithm while ensuring accuracy and real-time performance. To estimate the vehicle longitudinal, lateral, and yaw motion states, the algorithm uses a three degree of freedom (3-DOF) vehicle dynamics model and a nonlinear brush tire model, which is then validated in a Carsim-Simulink co-simulation platform for multiple operating conditions. The results show that the IMM-SCKF algorithm’s fusion output results can effectively follow the sub-model with smaller output errors, and that the IMM-SCKF algorithm’s results are superior to the traditional SCKF algorithm’s results.
Highlights
Artificial intelligence technology has been promoted and applied to a variety of fields in recent years, and vehicles are moving closer to intelligence and electrification, with more advanced active safety control systems and intelligent driver assistance systems being installed in vehicles
Rui Song et al designed square root cubature Kalman filter (SCKF) with different error covariance matrices based on an interacting multiple model framework in order to solve the uncertainty of the error covariance of the navigation system in vehicle dynamics state estimation, and the results show that the IMM-SCKF algorithm has better accuracy compared with the traditional method [21]
An interacting multiple model based on square root cubature Kalman filter algorithm for vehicle state estimation is proposed
Summary
Artificial intelligence technology has been promoted and applied to a variety of fields in recent years, and vehicles are moving closer to intelligence and electrification, with more advanced active safety control systems and intelligent driver assistance systems being installed in vehicles. Proposed an integrated control method for lateral dynamics, combining nonlinear vehicle state and parameter estimation with a second-order differential Kalman filter to build a nonlinear vehicle model capable of accurately estimating the peak pavement adhesion coefficient as well as the sideslip angle under extreme operating conditions [12]. Rui Song et al designed SCKF with different error covariance matrices based on an interacting multiple model framework in order to solve the uncertainty of the error covariance of the navigation system in vehicle dynamics state estimation, and the results show that the IMM-SCKF algorithm has better accuracy compared with the traditional method [21]. In previous research work, interacting multiple model methods are often combined with nonlinear Kalman filter and have shown good performance in areas such as target tracking, navigation, etc., but there have been no applications to extend the IMM-SCKF algorithm to the field of vehicle state estimation. The SCKF algorithm is used to estimate the yaw rate, sideslip angle, and longitudinal and lateral vehicle speeds for each sub-model, ensuring that the fusion estimation results always maintain the output of the sub-model with the least tracking error
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