Accurate information on tire road friction coefficient (TRFC) is essential to autonomous driving systems. In this paper, a fault-tolerant estimation scheme is proposed to estimate TRFC in the case of missing measurements. First, a fault-tolerant unscented Kalman filter (FTUKF) is developed for estimating longitudinal and lateral tire forces in the condition of sensor signal loss. Then, longitudinal and lateral TRFCs are estimated separately with FTUKF based on tire forces information. Next, an event-driven multi-model fusion method based on the degree of data loss is designed to perform a weighted fusion of longitudinal and lateral TRFCs to further improve the estimation accuracy. Experiments with different working conditions are performed to demonstrate the validity of the fault-tolerant estimation framework. The results illustrate that the designed approach has higher estimation accuracy and strong adaptability under various roads.

The accurate estimation of the mass and center of gravity (CG) position is key to vehicle dynamics modeling. The perturbation of key parameters in vehicle dynamics models can result in a reduction of accurate vehicle control and may even cause serious traffic accidents. A dual robust embedded cubature Kalman filter (RECKF) algorithm, which takes into account unknown measurement noise, is proposed for the joint estimation of mass and CG position. First, the mass parameters are identified based on directly obtained longitudinal forces in the distributed drive electric vehicle tires using the whole vehicle longitudinal dynamics model and the RECKF. Then, the CG is estimated with the RECKF using the mass estimation results and the vertical vehicle model. Finally, different virtual tests show that, compared with the cubature Kalman algorithm, the RECKF reduces the root mean square error of mass and CG by at least 7.4%, and 2.9%, respectively.

Accurate information about the vehicle state such as sideslip angle is critical for both advanced assisted driving systems and driverless driving. These vehicle states are used for active safety control and motion planning of the vehicle. Since these state parameters cannot be directly measured by onboard sensors, this paper proposes an adaptive estimation scheme in case of unknown measurement noise. Firstly, an estimation method based on the bicycle model is established using a square-root cubature Kalman filter (SQCKF), and secondly, the expectation maximization (EM) approach is used to dynamically update the statistic parameters of measurement noise and integrate it into SQCKF to form a new expectation maximization square-root cubature Kalman filter (EMSQCKF) algorithm. Simulations and experiments show that EMSQCKF has higher estimation accuracy under different driving conditions compared to the unscented Kalman filter.

This study is primarily focused on the issue of low-complexity prescribed performance fault compensation for multi-input and multi-output uncertain nonlinear systems with actuator failures, coupling states, and unknown control directions. First of all, proper logarithm-type error conversion functions and smooth orientation functions are linked to design the continuous control signals in the state feedback controller. Then, based on the idea of proof by contradiction, it is shown that the state errors converge to a predictable compact aggregate at the definite rate. Meanwhile, the boundedness of any closed-loop signals might be guaranteed. The numerical and actual simulation examples are delivered to demonstrate the effectiveness of the developed control strategy at last.