Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle Estimation

Zhenzhao Zhang,Chong Guo,Liang Chu,Jiaxu Zhang,Jing Li

doi:10.3390/app11031231

Abstract

This study is targeted at the key state parameters of vehicle stability controllers, the controlled vehicle model, and the nonlinearity and uncertainty of external disturbance. An adaptive double-layer unscented Kalman filter (ADUKF) is used to compute the sideslip angle, and a vehicle stability control algorithm adaptive fuzzy radial basis function neural network sliding mode control (AFRBF-SMC) is proposed. Since the sideslip angle cannot be directly determined, a 7-degrees-of-freedom (DOF) nonlinear vehicle dynamic model is established and combined with ADUKF to estimate the sideslip angle. After that, a vehicle stability sliding mode controller is designed and used to trace the ideal values of the vehicle stability parameters. To handle the severe system vibration due to the large robustness coefficient in the sliding mode controller, we use a fuzzy radial basis function neural network (FRBFNN) algorithm to approximate the uncertain disturbance of the system. Then the adaptive rate of the system is solved using the Lyapunov algorithm, and the systemic stability and convergence of this algorithm are validated. Finally, the controlling algorithm is verified through joint simulation on MATLAB/Simulink-Carsim. ADUKF can estimate the sideslip angle with high precision. The AFRBF-SMC vehicle stability controller performs well with high precision and low vibration and can ensure the driving stability of vehicles.

Highlights

With the intellectualizing of vehicles, the active safety control system becomes important in ensuring human-vehicle safety
By integrating the strong inferring ability of fuzzy systems and the strong learning ability of rbfnns, we proposed a fuzzy radial basis function neural network (FRBFNN) for uncertainty approximation
An AFRBF-sliding mode controller (SMC) vehicle stability control uncertainty term based on adaptive double-layer unscented Kalman filter (ADUKF) sideslip angle estimation is proposed

Summary

Introduction

With the intellectualizing of vehicles, the active safety control system becomes important in ensuring human-vehicle safety. As an important component of this system, the vehicle stability controller can effectively prevent vehicles from sharp steering, sideslip, and overturn when additional yaw moment is needed (e.g., Active steering, anti-collision upon emergency, path planning, tracking control), and is critical in guaranteeing human-vehicle safety [1,2,3,4,5]. The sideslip angle, a key parameter of vehicle stability control, cannot be directly determined by sensors given the consideration of costs and is estimated by building observers based on multi-information fusion [6,7,8,9,10]. If a vehicle model cannot well reflect the real vehicle status or if the measured sideslip angle is largely different from the real value, the vehicle stability control quality will be lowered and vehicle instability cannot be effectively avoided.

Methods

Results

Conclusion