Reliable Estimation of Automotive States Based on Optimized Neural Networks and Moving Horizon Estimator

Accurate estimation of vehicle sideslip angle and attitude angles are essential for the safety control and lateral behaviour of driving performance. In this article, the variation of wheels cornering stiffness is considered for sideslip estimation and addressed by introducing a recursive least squares approach. Based on the nonlinear vehicle dynamic model and the investigated coupling effect between lateral and longitudinal velocity, an optimized moving horizon estimator is proposed to obtain the vehicle sideslip angle, in which an iteration decent algorithm is integrated. Furthermore, a framework, consisting of inertial navigation system measurements, a dual neural network and a square-root cubature Kalman filter, is designed, such that the influence of sensor noise and varied maneuvers are alleviated when estimating the system states. Finally, extensive simulation and field experiments are carried out on different driving scenarios to verify the effectiveness of the developed method. The obtained results clearly indicate the satisfactory estimation accuracy of the designed strategy, superior to the existing estimation methods, such as sole neural networks methods and Kalman-based filters.

Automated summary

This article introduces a method for accurate estimation of vehicle sideslip angle and attitude angles. The method takes into account the variation of wheels cornering stiffness and addresses it by introducing a recursive least squares approach. An optimized moving horizon estimator is proposed to obtain the vehicle sideslip angle based on the nonlinear vehicle dynamic model and the investigated coupling effect between lateral and longitudinal velocity, integrating an iteration decent algorithm. Furthermore, a framework consisting of inertial navigation system measurements, a dual neural network, and a square-root cubature Kalman filter is designed to alleviate the influence of sensor noise and varied maneuvers when estimating the system states. Extensive simulation and field experiments are carried out to verify the effectiveness of the developed method in different driving scenarios, showing satisfactory estimation accuracy superior to existing methods.