Model Predictive Optimization and Control of Quadruped Whole-Body Locomotion

Chao Cun; Qunting Yang; Zhijun Li; MengChu Zhou; Jianxin Pang

doi:10.1109/JAS.2024.125073

Volume 12 Issue 10

Oct. 2025

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2025 > 12(10): 2103-2114

C. Cun, Q. Yang, Z. Li, M. C. Zhou, and J. Pang, “Model predictive optimization and control of quadruped whole-body locomotion,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 10, pp. 2103–2114, Oct. 2025. doi: 10.1109/JAS.2024.125073

Citation:

C. Cun, Q. Yang, Z. Li, M. C. Zhou, and J. Pang, “Model predictive optimization and control of quadruped whole-body locomotion,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 10, pp. 2103–2114, Oct. 2025. doi: 10.1109/JAS.2024.125073

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Model Predictive Optimization and Control of Quadruped Whole-Body Locomotion

doi: 10.1109/JAS.2024.125073

Funds: This work was supported in part by the National Natural Science Foundation of China (62133013, U22A2060) and Dreams Foundation of Jianghuai Advance Technology Center (2023-ZM01Z024)

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Author Bio:
Chao Cun received the B.S. degree in communication engineering from the Southwest University of Science and Technology in 2020. He is currectly a Ph.D. degree candidate in automation at the University of Science and Technology of China. His current research interests include the design and control of quadrupeds, biped robot control and reinforcement learning

Qunting Yang received the B.S. degree in automation from Southwest University of Science and Technology in 2018. She is currently a Ph.D. degree candidate in automation at the University of Science and Technology of China. Her current research interests include robotic systems and motion planning and control

Zhijun Li (Fellow, IEEE) received the Ph.D. degree in mechatronics from Shanghai Jiao Tong University in 2002. From 2003 to 2006, he was a Postdoctoral Fellow with The University of Electro-Communications, Japan, and the National University of Singapore, Singapore. He is currently a Chair Professor of Tongji University, where he has been the Dean of the School of Mechanical Engineering, since 2024. His current research interests include wearable robotics, bio-mechatronics systems, and intelligent control. Dr. Li has published over 400 papers, where the prestigious contributions were wearable robotics and bio-mechatronics systems. He has received the Distinguished Lecturer of IEEE RAS, the Web of Science Highly Cited Researcher from 2019 to 2024, the 2018 National Ten-Thousand Talents Program in China, and the 2016 National Distinguished Young Scholar. He is a Fellow of AAIA

MengChu Zhou (Fellow, IEEE) received the B.S. degree in control engineering from Nanjing University of Science and Technology in 1983, the M.S. degree in automatic control from Beijing Institute of Technology in 1986, and the Ph. D. degree in computer and systems engineering from Rensselaer Polytechnic Institute, USA in 1990. He joined New Jersey Institute of Technology (NJIT), USA in 1990, and has been a Distinguished Professor of electrical and computer engineering since 2013. His research interests are in intelligent automation, machine learning, Petri nets, robotics, Internet of Things, big data, cloud/edge computing, transportation and energy systems. Prof. Zhou has over 1400 publications including 17 books, over 900 journal papers (over 700 in IEEE transactions), and 32 book-chapters. He holds 31 patents and several pending ones. His recently Co-Authored books include Learning Automata and their Applications to Intelligent Systems, Device-Edge-Cloud Continuum Paradigms, Architectures and Applications, and Sustainable Manufacturing Systems: An Energy Perspective. He served as Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica, Associate Editor of IEEE Transactions on Robotics and Automation, IEEE Transactions on Automation Science and Engineering, and IEEE Transactions on Industrial Informatics, and Editor of IEEE Transactions on Automation Science and Engineering. He served as a Guest-Editor for many journals including IEEE Internet of Things Journal, IEEE Transactions on Industrial Electronics, IEEE Transactions on Evolutionary Computation, and IEEE Transactions on Semiconductor Manufacturing. He is presently Senior Editor of IEEE Transactions on Intelligent Transportation Systems, and Associate Editor of Research, IEEE Transactions on Industrial Informatics, IEEE Internet of Things Journal, and Frontiers of Information Technology & Electronic Engineering. He is founding Chair/Co-Chair of Technical Committee on AI-Based Smart Manufacturing Systems and Technical Committee on Humanized Crowd Computing of IEEE Systems, Man, and Cybernetics Society (SMC), Technical Committee on Semiconductor Manufacturing Automation of IEEE Robotics and Automation Society (RAS). He chaired IEEE North Jersey Section SMC Chapter during 2013–2023 and has been the Chair of the RAS Chapter since 2024. He is also a Member of IEEE TAB Periodicals Committee and Periodicals Review and Advisory Committee. He served as Vice-Chair of Fellow Evaluation Committee and Vice-President for Conferences and Meetings of IEEE Systems, Man, and Cybernetics Society. He was General Chair of IEEE Conference on Automation Science and Engineering, Washington DC., USA, August 23–26, 2008, General Co-Chair of 2003 IEEE International Conference on System, Man and Cybernetics (SMC), Washington DC, USA, October 5–8, 2003, 2019 IEEE International Conference on SMC, Bari, Italy, Oct. 6–9, 2019, 2022 IEEE International Conference on SMC, Prague, Czech, October 9–12, 2022, and 2024 IEEE International Conference on SMC, Kuching, Malaysia, Oct. 6–10, 2024, Founding General Co-Chair of 2004 IEEE International Conference on Networking, Sensing and Control, Taipei, China, March 21–23, 2004, and General Chair of 2006 IEEE International Conference on Networking, Sensing and Control, Ft. Lauderdale, USA. April 23–25, 2006. He was Program Chair of 2010 IEEE International Conference on Mechatronics and Automation, Xi’an, China, August 4–7, 2010, 1998 and 2001 IEEE International Conference on SMC and 1997 IEEE International Conference on Emerging Technologies and Factory Automation. Dr. Zhou has led or participated in over 60 research and education projects with total budget over $14M, funded by National Science Foundation, Department of Defense, NIST, New Jersey Science and Technology Commission, and industry. Prof. Zhou is a recipient of Excellence in Research Prize and Medal from NJIT, Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, and Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE SMC Society, Computer-Integrated Manufacturing UNIVERSITY-LEAD Award from Society of Manufacturing Engineers, Distinguished Service Award from IEEE Robotics and Automation Society, and Edison Patent Award from the Research & Development Council of New Jersey. He is a Life Member of Chinese Association for Science and Technology-USA and served as its President in 1999. He is Fellow of IEEE, International Federation of Automatic Control (IFAC), American Association for the Advancement of Science (AAAS), Chinese Association of Automation (CAA), and National Academy of Inventors (NAI)

Jianxin Pang received the B. E. and Ph.D. degrees from the University of Science and Technology of China, in 2002 and 2008, respectively. He is a Senior Engineer with UBTech Robotics, Ubtech Robotics Corporation. His research interests include image and video understanding, human-computer Interaction, and intelligent robot
Corresponding author: Qunting Yang, e-mail: qunting@mail.ustc.edu.cn; Zhijun Li, e-mail: zjli@ieee.org
Chao Cun and Qunting Yang contributed equally to this work.
Received Date: 2024-07-13
Revised Date: 2024-08-27
Accepted Date: 2024-11-23

Abstract

Abstract

In this paper, a framework of model predictive optimization and control for quadruped whole-body locomotion is presented, which enables dynamic balance and minimizes the control effort. First, we propose a hierarchical control scheme consisting of two modules. The first layer is to find an optimal ground reaction force (GRF) by employing inner model predictive control (MPC) along a full motor gait cycle, ensuring the minimal energy consumption of the system. Based on the output GRF of inner layer, the second layer is designed to prioritize tasks for motor execution sequentially using an outer model predictive control. In inner MPC, an objective function about GRF is designed by using a model with relatively long time horizons. Then a neural network solver is used to obtain the optimal GRF by minimizing the objective function. By using a two-layered MPC architecture, we design a hybrid motion/force controller to handle the impedance of leg joints and robotic uncertainties including external perturbation. Finally, we perform extensive experiments with a quadruped robot, including the crawl and trotting gaits, to verify the proposed control framework.
- Hybrid motion/force control,
- model predictive control (MPC),
- neural-dynamics,
- quadruped,
- whole-body control

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Chao Cun and Qunting Yang contributed equally to this work.

References(41)

References

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Model Predictive Optimization and Control of Quadruped Whole-Body Locomotion

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