Model predictive control allocation based on adaptive sliding mode control strategy for enhancing the lateral stability of four-wheel-drive electric vehicles

Abstract

A novel hierarchical direct yaw moment controller is designed to enhance the lateral stability of the four-wheel-drive electric vehicle. The adaptive sliding mode control (ASMC) technique in the upper-layer controller is employed to compute an additional yaw moment. The lower-layer controller distributes this yaw moment into each independent wheel by utilizing model predictive control allocation (MPCA). The proposed MPCA aims to mitigate the performance deterioration induced by in-wheel motor dynamics and optimize the power consumption stemming from the additional yaw moment. Co-simulation and hardware-in-the-loop (HIL) test is conducted to verify the performance of the proposed controller. Validation results show that the proposed hierarchical ASMC-MPCA controller outperforms the sliding mode control MPCA (SMC-MPCA) and the integrated nonlinear model predictive control (NMPC) with the lowest root-mean-square errors [Formula: see text] of yaw rate, sideslip angle, lateral deviation, and lowest power consumption. Additionally, the chattering phenomenon in SMC-MPCA can be suppressed effectively by adaptively estimating the parameter uncertainties. The proposed ASMC-MPCA controller also consumes less computational resources than the NMPC and SMC-MPCA, which indicates that the ASMC-MPCA is more suitable for an automotive onboard controller. The comparison between hierarchical and integrated controller frameworks also shows that the hierarchical framework is more suitable for production vehicles under non-powerful vehicle control units.

Coordinated control of longitudinal and lateral movements considering dynamics for distributed drive electric vehicle platoon on curved roads

“…There have been many research achievements in the lateral stability control of DDEVs, among which direct yaw moment control (DYC) can significantly improve the stability and handling performance of the vehicle (Liang et al, 2022). The main methods of DYC include PID control (Novellis et al, 2014), SMC (Mousavinejad et al, 2017), nonlinear three-step control (Zhao et al, 2015), and MPC (Ao et al, 2023; Jalali et al, 2018; Reza et al, 2022; Xie et al, 2022). Jalali et al (2018) designed an MPC controller to limit the side slip angle to a small range through soft constraints to prevent vehicle instability.…”

“…However, there are apparent limitations to ensuring vehicle stability through constraints. Ao et al (2023) proposed a layered controller based on adaptive SMC-model predictive control allocation (ASMC-MPCA) to improve the lateral stability of four-wheel-drive electric vehicles. Among them, the upper controller uses ASMC to handle model uncertainty and parameter changes and calculate the required additional yaw moment.…”

Robust Torque Vectoring With Desired Cornering Stiffness for In-Wheel Motor Vehicles

“…An event-triggered torque vectoring controller reducing communication burden was proposed in [22]. Attempts to effectively combine optimal control and robust control in torque vectoring control can be found in [11] and [23]. In [11], a rear-wheel torque vectoring controller was introduced, employing a combination of MPC and SMC.…”