Estimation of the tire slip angle under various road conditions without tire–road information for vehicle stability control

This paper presents a tire slip angle estimator based on an Interacting Multiple Model (IMM) algorithm for vehicle stability control. The proposed algorithm is capable of estimating the tire slip angles under various road conditions without the prior knowledge of tire and road condition by only using on-board vehicle sensors. Instead of employing tire and road information, the proposed algorithm utilizes multiple numbers of model candidates to represent all aspects of vehicle motions under various road conditions. Each model candidate is a combination of lateral vehicle dynamics and transient/steady state tire dynamics. The proposed algorithm evaluates the fidelity of each model candidate to the current vehicle dynamics with probability. Moreover, in the proposed algorithm, multiple numbers of Kalman filters are embedded with these model candidates as process models. The final estimate of the proposed algorithm in each time step is a linear sum of the posteriori states from multiple embedded filters with the calculated probability as coefficients. The proposed estimation algorithm has been evaluated via vehicle tests. The tests have been conducted on dry asphalt and wet asphalt using a luxury passenger car equipped with a high-performance GPS for reference and data logging computer. The results have shown that the proposed estimator can successfully estimate tire slip angles with satisfactory accuracy under various road conditions