Optimal Coordinated Control of Active Front Steering and Direct Yaw Moment for Distributed Drive Electric Bus

Abstract

This paper suggests a hierarchical coordination control strategy to enhance the stability of distributed drive electric bus. First, an observer based on sliding mode observer (SMO) and adaptive neural fuzzy inference system (ANFIS) was designed to estimate the vehicle state parameters. Then the upper layer of the strategy primarily focuses on coordinating active front steering (AFS) and direct yaw moment control (DYC). The phase plane method is utilized in this layer to provide an assessment basis for the switching control safety of AFS and DYC. The lower layer of the strategy designs an integral terminal sliding mode controller (ITSMC) and a non-singular fast terminal sliding mode controller (NFTSMC) to obtain the optimal additional front wheel steering angle to improve handling performance. A fuzzy sliding mode controller (FSMC) is also proposed to obtain additional yaw moment to ameliorate yaw stability. Finally, the strategy proposed in this paper is subjected to simulation testing and compared with the performance of AFS and DYC systems. The proposed strategy is also evaluated for tracking errors in sideslip angle and yaw rate under two conditions. The results demonstrate that the proposed strategy can effectively adapt to various extreme environments and improve the maneuvering and yaw stability of the bus.

Direct Yaw Moment Control for Distributed Drive Electric Vehicles Based on Hierarchical Optimization Control Framework

“…However, when the state trajectory gets to the sliding mode surface, it will generate chattering and deteriorate the control effect of the vehicle's yaw stability. Cao et al [17] and Lin et al [18] designed a direct-yaw-moment controller using a LQR algorithm, which has small a steady-state error and good robustness, improving the vehicle handling stability. Nevertheless, further optimization of the weight coefficient should be conducted to improve the control performance of LQR.…”

Advanced Modeling, Analysis and Control for Electrified Vehicles

“…The primary goal of this Special Issue is to provide timely solutions to technological developments and challenges in the modeling, estimation, simulation, design, control and optimization of electrified vehicle systems. After a rigorous review process, 10 manuscripts were ultimately accepted which address adhesion coefficient estimation and the estimation of tire-road parameters [7,8], the optimization of an active steering system [9], braking energy regeneration [10], anti-rollover control [11], the coordinated control of vehicle dynamics [12], the integrated control of path tracking and stability [13], vehicle detection and ground segmentation [14], automatic emergency braking systems [15] and obstacle avoidance control [16]. A brief summary of these articles is provided below.…”

“…Furthermore, the achievement of integrated control via the coordination of aspects of a vehicle dynamics subsystem, such as active front steering (AFS), direct yaw moment control (DYC) for path following and improvements in the lateral stability the vehicle, is addressed. In [12], a hierarchical coordination control strategy comprising the coordination of active front steering and direct yaw moment is proposed to enhance the stability of a distributed-drive electric bus. An observer based on a sliding-mode observer (SMO) and an adaptive neural fuzzy inference system (ANFIS) are built to estimate the vehicle parameters; the upper-layer control focuses on a coordinated strategy, while the lower-layer control of the strategy incorporates an integral terminal sliding mode controller (ITSMC) and a non-singular fast terminal sliding mode controller (NFTSMC) to obtain the optimal additional front wheel steering angle and yaw moment, respectively.…”