Safety-Certified Parallel Model Predictive Control of Autonomous Surface Vehicles via Neurodynamic Optimization

Guanghao Lyu; Zhouhua Peng; Jun Wang

doi:10.1109/JAS.2024.124980

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): 2056-2066

G. Lyu, Z. Peng, and J. Wang, “Safety-certified parallel model predictive control of autonomous surface vehicles via neurodynamic optimization,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 10, pp. 2056–2066, Oct. 2025. doi: 10.1109/JAS.2024.124980

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G. Lyu, Z. Peng, and J. Wang, “Safety-certified parallel model predictive control of autonomous surface vehicles via neurodynamic optimization,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 10, pp. 2056–2066, Oct. 2025. doi: 10.1109/JAS.2024.124980

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Safety-Certified Parallel Model Predictive Control of Autonomous Surface Vehicles via Neurodynamic Optimization

doi: 10.1109/JAS.2024.124980

Funds: This work was supported in part by the National Science and Technology Major Project (2022ZD0119902), the National Natural Science Foundation of China (52471372, 623B2018, 62203015, 62233001), the Liaoning Revitalization Leading Talents Program (XLYC2402054), the Key Basic Research of Dalian (2023JJ11CG008), the Fundamental Research Funds for the Central Universities (3132023508), the Collaborative Research Fund of Hong Kong Research Grants Council (C1013-24G), and the Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University (2023YBPY005)

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Author Bio:
Guanghao Lyu (Member, IEEE) received the B.E. degree in marine electrical engineering from Zhejiang Ocean University in 2017, and the Ph.D. degree in marine electrical engineering from Dalian Maritime University in 2025.He is currently a Postdoctoral Research Fellow with the School of Advanced Manufacturing and Robotics, Peking University. From 2022 to 2023, he was a Research Assistant with the Department of Computer Science, City University of Hong Kong, Hong Kong, China. His research interests include motion planning, guidance and control of single/multiple autonomous surface vehicles, and model predictive control

Zhouhua Peng (Senior Member, IEEE) received the B.E. degree in electrical engineering and automation, the M.E. degree in power electronics and power drives, and the Ph.D. degree in control theory and control engineering from Dalian Maritime University in 2005, 2008, and 2011, respectively. He currently serves as the Dean of the School of Marine Electrical Engineering at Dalian Maritime University and the Director of the Key Laboratory of Swarm Control and Electrical Technology for Intelligent Ships. From 2014 to 2016, he was a Postdoctoral Researcher at the School of Control Science and Engineering, Dalian University of Technology, and from 2016 to 2018, he was a Hong Kong Scholar in the Department of Computer Science, City University of Hong Kong. He has presided over more than 10 national-level projects, published over 170 SCI papers with more than 11 000 citations and an H-index of 56 according to Google Scholar, authored three academic monographs, and holds 76 authorized invention patents. His research interests include disturbance rejection, intelligent control, coordinated control, and optimization methods with an emphasis on unmanned marine vehicles. Prof. Peng was a recipient of the Science and Technology Award (First Class) from China Association of Oceanic Engineering in 2019, the Natural Science Awards (Second Class) from Liaoning Province in 2013 and 2017, the Hong Kong Scholar Award in 2016, and the Science and Technology Award for Youth from China Institute of Navigation in 2017. He won the Honor of the Distinguished Worker in the National Ministry of Transport of China in 2020, the Science and Innovation Leadership for Young and Middleaged Scientists of the Ministry of Transport of the People’s Republic of China in 2020, the “Bai-Qian-Wan” Talent (level Bai) from Liaoning Province in 2019, the Young Talent in Science and Technology from the Ministry of Transport of the People’s Republic of China in 2017, and the Distinguished Young Talent in Science and Technology from Dalian in 2018

Jun Wang (Life Fellow, IEEE) received the B.S. degree in electrical engineering and the M.S. degree in systems engineering from Dalian University of Technology and the Ph.D. degree in systems engineering from Case Western Reserve University Cleveland, USA. He held various academic positions at Dalian University of Technology; Case Western Reserve University, USA; the University of North Dakota, USA; and the Chinese University of Hong Kong, China. He also held various short-term or part-time visiting positions at the U.S. Air Force Armstrong Laboratory, USA; RIKEN Brain Science Institute, Japan; Chinese Academy of Sciences, Huazhong University of Science and Technology; Shanghai Jiao Tong University; Dalian University of Technology, and Swinburne University of Technology, Australia. He is currently a Chair Professor in the Department of Computer Science and Department of Data Science at City University of Hong Kong, Hong Kong, China. His research interests include AI in general and neural networks in particular. He is the Editor-in-Chief of the IEEE Transactions on Artificial Intelligence since 2025. He was the Editor-in-Chief of the IEEE Transactions on Cybernetics in 2014–2019 and an Associate Editor of the journal and its predecessor in 2003–2013. He also served in the Editorial Board or Editorial Advisory Board of the IEEE Transactions on Neural Networks, IEEE Transactions on Systems, Man, and Cybernetics-Part C, Neural Networks, and the International Journal of Neural Systems. He was a Guest Editor of special issues of the European Journal of Operational Research, International Journal of Neural Systems, Neurocomputing, Journal Franlin Institute, and IEEE Transactions on Sytems, Man and Cybernetics: Systems. He served as the President of the Asia Pacific Neural Network Assembly in 2006, the General Chair of the IEEE World Congress on Computational Intelligence in 2008, 13th and 25th International Conference on Neural Information Processing in 2006/2018, and a Program Chair of the IEEE International Conference on Systems, Man, and Cybernetics in 2012. In addition, has served on many committees such as the IEEE Fellow Committee, Fellow Evaluation Committee and Awards Committee in IEEE Computational Intelligence Society, and Fellow Evaluation Committee and Board of Governors in IEEE Systems, Man and Cybernetics Society. He was an IEEE Computational Intelligence Society Distinguished Lecturer in 2010–2012, 2014–2016, and an IEEE Systems, Man, and Cybernetics Society Distinguished Lecturer in 2017–2021. He is a Fellow of the International Association for Pattern Recognition and the Hong Kong Academy of Engineering, and a Foreign Member of Academia Europaea. He was a recipient of the Research Excellence Award from the Chinese University of Hong Kong for 2008–2009, the Outstanding Achievement Award from Asia Pacific Neural Network Assembly, IEEE Transactions on Neural Networks Outstanding Paper Award in 2011, Neural Networks Pioneer Award from the IEEE Computational Intelligence Society in 2014, Horbert Wiener Award from the IEEE Systems, Man and Cybernetics Society in 2019, among other distinctions
Corresponding author: Zhouhua Peng, e-mail: zhpeng@dlmu.edu.cn; Jun Wang, e-mail: jwang.cs@cityu.edu.hk
Received Date: 2024-07-25
Revised Date: 2024-09-13
Accepted Date: 2024-10-10

Abstract

Abstract

This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances, state constraints, and input constraints in complex ocean environments with multiple obstacles. A safety-certified parallel model predictive control scheme with collision-avoiding capability is proposed for autonomous surface vehicles in the framework of parallel control. Specifically, an extended state observer is designed by leveraging historical and real-time data for concurrent learning to map the motion of autonomous surface vehicles from its physical system to its artificial counterpart. A parallel model predictive control law is developed on the basis of the artificial system for both physical and artificial autonomous surface vehicles to realize virtual-physical tracking control of vehicles subject to state and input constraints. To ensure safety, high-order discrete control barrier functions are encoded in the parallel model predictive control law as safety constraints such that collision avoidance with obstacles can be achieved. A receding-horizon constrained optimization problem is constructed with the safety constraints encoded by control barrier functions for parallel model predictive control of autonomous surface vehicles and solved via neurodynamic optimization with projection neural networks. The effectiveness and characteristics of the proposed method are demonstrated via simulations for the safe trajectory tracking and automatic berthing of autonomous surface vehicles.

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Safety-Certified Parallel Model Predictive Control of Autonomous Surface Vehicles via Neurodynamic Optimization

doi: 10.1109/JAS.2024.124980

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