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- Title
Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter.
- Authors
Wenkang, Wan; Jingan, Feng; Bao, Song; Xinxin, Li
- 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.
- Subjects
KALMAN filtering; SQUARE root; MULTI-degree of freedom; STATISTICS; MOTOR vehicle tires; ALGORITHMS; NOISE measurement; AIR filters
- Publication
Applied Sciences (2076-3417), 2021, Vol 11, Issue 22, p10772
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app112210772