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Neural Approximation-based Model Predictive Tracking Control of Non-holonomic Wheel-legged Robots

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Abstract

This paper proposes a neural approximation based model predictive control approach for tracking control of a nonholonomic wheel-legged robot in complex environments, which features mechanical model uncertainty and unknown disturbances. In order to guarantee the tracking performance of wheel-legged robots in an uncertain environment, effective approaches for reliable tracking control should be investigated with the consideration of the disturbances, including internal-robot friction and external physical interactions in the robot’s dynamical system. In this paper, a radial basis function neural network (RBFNN) approximation based model predictive controller (NMPC) is designed and employed to improve the tracking performance for nonholonomic wheel-legged robots. Some demonstrations using a BIT-NAZA robot are performed to illustrate the performance of the proposed hybrid control strategy. The results indicate that the proposed methodology can achieve promising tracking performance in terms of accuracy and stability.

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Authors and Affiliations

  1. State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, 100081, China

    Jiehao Li, Junzheng Wang & Shoukun Wang

  2. Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, Italy

    Wen Qi & Hang Su

  3. BioMEx Center and KTH Mechanics, KTH Royal Institute of Technology, SE-100 44, Stockholm, Sweden

    Longbin Zhang

  4. Department of Informatics, Technical University of Munich, Munich, 85748, Germany

    Yingbai Hu

Authors
  1. Jiehao Li
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  2. Junzheng Wang
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  3. Shoukun Wang
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  4. Wen Qi
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  5. Longbin Zhang
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  6. Yingbai Hu
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  7. Hang Su
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Corresponding author

Correspondence to Hang Su.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Editor Fumitoshi Matsuno.

This study was supported by the Nation Natural Science Foundation of China under Grant 61773060, and China Scholarship Council under Grant 201906030066.

Jiehao Li received his M.Sc. degree in control engineering from South China University of Technology, Guangzhou, China, in 2017. He is pursuing a Ph.D. degree as a member of State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, China, and as a visiting research fellow of the Medical and Robotic Surgery Group (NEARLab) and the Department of Electronics, Information and Bioengineering (DEIB) in Politecnico di Milano, Milan, Italy, where he is working on the intelligent control and practical application for mobile robots.

Junzheng Wang received his Ph.D. degree in control science and engineering from Beijing Institute of Technology, Beijing, China, in 1994. He is the Deputy Director with the Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, where he is a Professor and a Ph.D. Supervisor. His current research interests include motion control, static and dynamic performance testing of electric and electric hydraulic servo system, and dynamic target detection and tracking based on image technology. Prof. Wang is a senior member of the Chinese Mechanical Engineering Society and the Chinese Society for Measurement. He received the Second Award from the National Scientific and Technological Progress (No.1) in 2011.

Shoukun Wang received his B.S., M.S., and Ph.D. degree in the department of automation, from Beijing Institute of Technology, Beijing, China, in 1999, 2002, and 2004, respectively. He has entered in the Department of Electronics and Computer Engineering, Purdue University, West Lafayette, USA, as a visiting scholar. He has been teaching at the School of Automation, Beijing Institute of Technology, since 2004. His research interests include sensor, measurement, and electrohydraulic control. He has participated in over 30 scientific research projects since 2001, which mainly belong to measurement and servo control. He has also served as the leader in some of these works. His main work focused on electrical-hydraulic control algorithm, robot locomotion control, and visual systems.

Wen Qi received her M.Sc. degree in control engineering from the South China University of Technology, Guangzhou, China, in 2015. Her first-authored paper was awarded the finalist of T J Tarn’s Best Paper Award on Control Applications on IEEE WCICA 2014. Now she is pursuing a Ph.D. degree as a member of the Laboratory of Biomedical Technologies (TBMLab) in Politecnico di Milano, Milano, Italy. Her main research interests include machine learning, deep learning and signal processing algorithms in wearable medical devices.

Longbin Zhang received her M.S degree in control engineering from South China University of Technology, Guangzhou, China, in 2017. She is currently pursuing a Ph.D. degree in Department of Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden, working on robotic exoskeletons for patients with motor disorders. Her main research interests include human movement simulation, strategies, consequences, and assistance in patients with motor disorders, and adaptive control.

Yingbai Hu received his M.Sc. degree in control theory and control engineering from South China University of Technology, Guangzhou, China, in 2017. He is currently working toward a Ph.D. degree in computer science as a member of the Informatics 6-Chair of Robotics, Artificial Intelligence and Real-time Systems in Technische Universität München, München, Germany. His main research interests include control and instrumentation in mobile robot, human-robot interaction, surgical robotics, reinforcement learning, etc..

Hang Su received his M.Sc. degree in control theory and control engineering from South China University of Technology, Guangzhou, China, in 2015. He obtained his Ph.D. degree in 2019 as a member of the Medical and Robotic Surgery group (NEARLab) in Politecnico di Milano, Milano, Italy. He participated in the EU funded project (SMARTsurg) in the field of Surgical Robotics. Dr. Hang Su is currently a Research Fellow in the Department of Electronics, Information and Bioengineering (DEIB) of Politecnico Di Milano. He is fostering an international research team constituting three PhD students and a few Masters students in the field of Medical Robotics. He serves as an Associate Editor for IEEE International Conference on Robotics and Automation (ICRA) and IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). He also has served as a reviewer for over 30 scientific journals, such as IEEE Transaction on Biomedical Engineering, IEEE/ASME Transactions on Mechatronics, IEEE Transaction on Automation and Engineering, IEEE Transaction on Cybernetics, IEEE Transactions on Systems, Man, and Cybernetics: Systems, etc.. He is currently Special Session Chair of IEEE International Conference on Advanced Robotics and Mechatronics (ICARM 2020). He has published several papers in international conferences and journals and has been awarded ICRA 2019 travel grant. His main research interests include control and instrumentation in medical robotics, human-robot interaction, surgical robotics, deep learning, bilateral teleoperation, etc..

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Li, J., Wang, J., Wang, S. et al. Neural Approximation-based Model Predictive Tracking Control of Non-holonomic Wheel-legged Robots. Int. J. Control Autom. Syst. 19, 372–381 (2021). https://doi.org/10.1007/s12555-019-0927-2

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