TY - JOUR
T1 - MME-EKF-Based Path-Tracking Control of Autonomous Vehicles Considering Input Saturation
AU - Hu, Chuan
AU - Wang, Zhenfeng
AU - Taghavifar, Hamid
AU - Na, Jing
AU - Qin, Yechen
AU - Guo, Jinghua
AU - Wei, Chongfeng
PY - 2019/6/18
Y1 - 2019/6/18
N2 - This paper investigates the path-tracking control issue for autonomous ground vehicles with the integral sliding mode control (ISMC) considering the transient performance improvement. The path-tracking control is converted into the yaw stabilization problem, where the sideslip-angle compensation is adopted to reduce the steady-state errors, and then the yaw-rate reference is generated for the path-tracking purpose. The lateral velocity and roll angle are estimated with the measurement of the yaw rate and roll rate. Three contributions have been made in this paper: first, to enhance the estimation accuracy for the vehicle states in the presence of the parametric uncertainties caused by the lateral and roll dynamics, a robust extended Kalman filter is proposed based on the minimum model error algorithm; second, an improved adaptive radial basis function neural network (RBFNN) considering the approximation error adaptation is developed to compensate for the uncertainties caused by the vertical motion; third, the RBFNN and composite nonlinear feedback (CNF) based ISMC is developed to achieve the yaw stabilization and enhance the transient tracking performance considering the input saturation of the front steering angle. The overall stability is proved with Lyapunov function. Finally, the superiority of the developed control strategy is verified by comparing with the traditional CNF with high-fidelity CarSim-MATLAB simulations.
AB - This paper investigates the path-tracking control issue for autonomous ground vehicles with the integral sliding mode control (ISMC) considering the transient performance improvement. The path-tracking control is converted into the yaw stabilization problem, where the sideslip-angle compensation is adopted to reduce the steady-state errors, and then the yaw-rate reference is generated for the path-tracking purpose. The lateral velocity and roll angle are estimated with the measurement of the yaw rate and roll rate. Three contributions have been made in this paper: first, to enhance the estimation accuracy for the vehicle states in the presence of the parametric uncertainties caused by the lateral and roll dynamics, a robust extended Kalman filter is proposed based on the minimum model error algorithm; second, an improved adaptive radial basis function neural network (RBFNN) considering the approximation error adaptation is developed to compensate for the uncertainties caused by the vertical motion; third, the RBFNN and composite nonlinear feedback (CNF) based ISMC is developed to achieve the yaw stabilization and enhance the transient tracking performance considering the input saturation of the front steering angle. The overall stability is proved with Lyapunov function. Finally, the superiority of the developed control strategy is verified by comparing with the traditional CNF with high-fidelity CarSim-MATLAB simulations.
KW - Path tracking
KW - autonomous vehicles
KW - sliding-mode control
KW - extended Kalman filter
KW - neural network
U2 - 10.1109/TVT.2019.2907696
DO - 10.1109/TVT.2019.2907696
M3 - Article
VL - 68
SP - 5246
EP - 5259
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
SN - 0018-9545
IS - 6
ER -