TY - GEN
T1 - Internal-Stably Energy-Saving Cooperative Control of Articulated Wheeled Robot with Distributed Drive Units
AU - Yang, Yi
AU - Peng, Huishuai
AU - Hu, Zhexi
AU - Li, Haoyu
AU - Xie, Shanshan
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Articulated wheeled robots play a crucial role in the logistics industry. However, conventional tractor-driven articulated wheeled robots exhibit poor internal stability and are prone to jackknifing, while also consuming a significant amount of energy. By deploying distributed drives and coordinating control among multiple drives, these issues can be effectively addressed. However, the flexible connections between the bodies of articulated vehicles pose significant challenges to the coordinated control of distributed drives. This paper proposes a multi-drive unit coordinated control algorithm based on driving force equivalence and allocation. A neural network is used to predict the driving force, and through non-linear driving force equivalence, a feedforward driving force is obtained. This is combined with a closed-loop feedback compensation controller to form a control architecture that integrates feedforward and feedback, resulting in the equivalent total driving force for the vehicle queue. Subsequently, an equivalent distribution strategy allocates the required driving force to each drive, enabling the vehicle bodies to achieve accurate and stable speed tracking while allowing each drive to operate near its efficient operating point, thereby reducing total energy consumption. Experiments demonstrate that our algorithm significantly lowers the total energy consumption of the vehicle queue under standard operating conditions while ensuring speed-tracking accuracy and improving internal stability.
AB - Articulated wheeled robots play a crucial role in the logistics industry. However, conventional tractor-driven articulated wheeled robots exhibit poor internal stability and are prone to jackknifing, while also consuming a significant amount of energy. By deploying distributed drives and coordinating control among multiple drives, these issues can be effectively addressed. However, the flexible connections between the bodies of articulated vehicles pose significant challenges to the coordinated control of distributed drives. This paper proposes a multi-drive unit coordinated control algorithm based on driving force equivalence and allocation. A neural network is used to predict the driving force, and through non-linear driving force equivalence, a feedforward driving force is obtained. This is combined with a closed-loop feedback compensation controller to form a control architecture that integrates feedforward and feedback, resulting in the equivalent total driving force for the vehicle queue. Subsequently, an equivalent distribution strategy allocates the required driving force to each drive, enabling the vehicle bodies to achieve accurate and stable speed tracking while allowing each drive to operate near its efficient operating point, thereby reducing total energy consumption. Experiments demonstrate that our algorithm significantly lowers the total energy consumption of the vehicle queue under standard operating conditions while ensuring speed-tracking accuracy and improving internal stability.
UR - https://www.scopus.com/pages/publications/105016633351
U2 - 10.1109/ICRA55743.2025.11128843
DO - 10.1109/ICRA55743.2025.11128843
M3 - Conference contribution
AN - SCOPUS:105016633351
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3459
EP - 3465
BT - 2025 IEEE International Conference on Robotics and Automation, ICRA 2025
A2 - Ott, Christian
A2 - Admoni, Henny
A2 - Behnke, Sven
A2 - Bogdan, Stjepan
A2 - Bolopion, Aude
A2 - Choi, Youngjin
A2 - Ficuciello, Fanny
A2 - Gans, Nicholas
A2 - Gosselin, Clement
A2 - Harada, Kensuke
A2 - Kayacan, Erdal
A2 - Kim, H. Jin
A2 - Leutenegger, Stefan
A2 - Liu, Zhe
A2 - Maiolino, Perla
A2 - Marques, Lino
A2 - Matsubara, Takamitsu
A2 - Mavromatti, Anastasia
A2 - Minor, Mark
A2 - O'Kane, Jason
A2 - Park, Hae Won
A2 - Park, Hae-Won
A2 - Rekleitis, Ioannis
A2 - Renda, Federico
A2 - Ricci, Elisa
A2 - Riek, Laurel D.
A2 - Sabattini, Lorenzo
A2 - Shen, Shaojie
A2 - Sun, Yu
A2 - Wieber, Pierre-Brice
A2 - Yamane, Katsu
A2 - Yu, Jingjin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE International Conference on Robotics and Automation, ICRA 2025
Y2 - 19 May 2025 through 23 May 2025
ER -