A Robust Adaptive Fault-Tolerant Estimator for Sideslip Angle and Tire Cornering Stiffness With Multiple Missing Data

Abstract

The accurate estimation of crucial state parameters, such as sideslip angle and tire cornering stiffness, plays a vital role in ensuring the efficacy of active safety technology and motion control for distributed drive electric vehicles. To address the challenges posed by missing measurements from multiple sensors and dynamic model mismatch during the estimation process, this article proposes a novel robust adaptive fault-tolerant estimator (RAFTE). Based on the four-wheel nonlinear vehicle model, the model parameters from the RAFTE are continuously adjusted by adaptive forgetting factor recursive least squares, which provides a more accurate dynamic model. Huber's robust M-estimation theory is integrated into the standard cubature Kalman filter, which adjusts the measurement noise covariance and innovation covariance to mitigate the effects of missing data. Then, an optimal adaptive factor is introduced to alleviate the issues of model mismatch and unmodeled dynamic characteristics that arise from the strong coupling nonlinear system. Finally, simulations and experiments are conducted to validate the estimation accuracy, convergence, and robustness of the proposed algorithm.