Multi-Agent-Based Coordinated Control of ABS and AFS for Distributed Drive Electric Vehicles

Niaona Zhang, Jieshu Wang, Zonghao Li, Shaosong Li and Haitao Ding
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Niaona Zhang: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130000, China
Jieshu Wang: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130000, China
Zonghao Li: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130000, China
Shaosong Li: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130000, China
Haitao Ding: State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130000, China

Energies, 2022, vol. 15, issue 5, 1-17

Abstract: A vehicle with a four-channel anti-lock braking system (ABS) has poor safety and stability when braking on a low-adhesion road or off-road. In view of this situation, this paper proposes a multi-objective optimization coordinated control method for ABS and AFS based on multi-agent model predictive control (MPC). Firstly, the single-wheel control method is adopted to establish the single-wheel equation based on the slip rate and the stability equation of the centroid yaw based on AFS. The four wheels and the centroid are regarded as agents. The mathematical model of distributed drive electric vehicles based on graph theory and the coordinated control of AFS and ABS is established to reduce the dimension of the model. Secondly, on the basis of the multi-agent theory, an integrated coordinated control method for AFS and ABS based on distributed model predictive control (DMPC) is proposed to realize the ideal values of the vehicle’s slip rate, yaw rate, and sideslip angle, and improve the braking safety and handling stability of the vehicle. Then, to solve the problems of high levels of resource consumption, low real-time performance, and complex implementation in the optimization of the DMPC solution, a prediction solution method using a discrete simplified dual neural network (SDNN) is proposed to balance the computational efficiency and system dynamic performance. Finally, a hardware-in-the-loop (HIL) test bench is built to test the effectiveness of the proposed method under the conditions of a low-adhesion road and an off-road.

Keywords: distributed drive electric vehicle; active front steering (AFS); anti-lock braking system (ABS); coordinated control; heterogeneous multi-agent (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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