Estimation of Sideslip Angle and Tire Cornering Stiffness Using Fuzzy Adaptive Robust Cubature Kalman Filter

The accurate information of sideslip angle (SA) and tire cornering stiffness (TCS) is essential for advanced chassis control systems. However, SA and TCS cannot be directly measured by in-vehicle sensors. Thus, it is a hot topic to estimate SA and TCS with only in-vehicle sensors by an effective estimation method. In this article, we propose a novel fuzzy adaptive robust cubature Kalman filter (FARCKF) to accurately estimate SA and TCS. The model parameters of the FARCKF are dynamically updated using recursive least squares. A Takagi-Sugeno fuzzy system is developed to dynamically adjust the process noise parameter in the FARCKF. Finally, the performance of FARCKF is demonstrated via both simulation and experimental tests. The test results indicate that the estimation accuracy of SA and TCS is higher than that of the existing methods. Specifically, the estimation accuracy of SA is at least improved by more than 48%, while the estimators of TCS are closer to the reference values.

Automated summary

This article proposes a novel method, the fuzzy adaptive robust cubature Kalman filter (FARCKF), to accurately estimate the sideslip angle and tire cornering stiffness. The model parameters are dynamically updated and a fuzzy system is used to improve estimation accuracy.