An adaptive approach to real-time estimation of vehicle sideslip, road bank angles and sensor bias
For automotive stability control, this work provides a practical solution to estimate sideslip angle without expensive sensors, but it is incremental as it combines existing Kalman filter techniques with adaptive tire stiffness updates.
The paper presents a sideslip estimation algorithm using only production-vehicle sensors, achieving smooth and accurate estimation along with reliable estimates of bank angles and sensor bias, validated through multiple experimental tests.
Robust estimation of vehicle sideslip angle is essential for stability control applications. However, the direct measurement of sideslip angle is expensive for production vehicles. This paper presents a novel sideslip estimation algorithm which relies only on sensors available on passenger and commercial vehicles. The proposed method uses both kinematics and dynamics vehicle models to construct extended Kalman filter observers. The estimate relies on the results provided from the dynamics model observer where the tire cornering stiffness parameters are updated using the information provided from the kinematics model observer. The stability property of the proposed algorithm is discussed and proven. Finally, multiple experimental tests are conducted to verify its performance in practice. The results show that the proposed approach provides smooth and accurate sideslip angle estimation. In addition, our novel algorithm provides reliable estimates of bank angles and lateral acceleration sensor bias.