| Design and Experimental Evaluation of a Novel Multi-axle Electric Truck with Broad-Deep Learning-Based Collaborative Controllers for Active Steering and Direct Yaw Moment Control |
| Pak Kin Wong, Xiaozheng Wang, Shiron Thalagala |
| Department of Electromechanical Engineering, University of Macau, Macau, 999078, China |
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Received: June 25, 2025; Revised: October 26, 2025 Accepted: October 26, 2025. Published online: December 5, 2025. |
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| ABSTRACT |
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This study introduces an electric multi-axle truck (EMAT) featuring a new power system, along with collaborative active steering control (ASC) and direct yaw moment control (DYC) to tackle steering problems. The EMAT design includes a drive motor and differential on the middle axle, as well as two independent motors on the rear axle. This rear-wheel distributed drive system, supported by the drive motor, meets heavy-load demands, reduces rear motor stress and facilitates collaborative ASC and DYC. The EMAT also utilizes the broad critic deep actor (BCDA) reinforcement learning (RL) algorithm to train ASC and DYC, which demands fewer computational resources and training time than traditional RL controllers due to its optimized training process. Prototype tests validate the effectiveness of the EMAT and RL-based BCDA controller in terms of significant improvement on steering performance (including over 80% reduction in training time compared to the deep deterministic policy gradient (DDPG) algorithm). The proposed EMAT and RL-based BCDA controller show promise in truck design and dynamic control. |
| Key Words:
Multi-axle electric truck · Independent motor drive · Active steering control · Direct yaw moment control · Reinforcement learning control · Broad critic deep actor |
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Corresponding Author.
Xiaozheng Wang , Email. 
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