Advanced Model Predictive Control for Autonomous Marine Vehicles

1. Introduction.- 2. AUV Modelling.- 3. Receding Horizon Optimization for Integrated Path Planning and Tracking Control of an AUV.- 4. Lyapunov-Based Model Predictive Control for Dynamic Positioning and Trajectory Tracking Control of an AUV.- 5. Multi-Objective Model Predictive Control for Path Following Control of an AUV.- 6. Efficient Implementation Algorithms for NMPC Trajectory Tracking Control of an AUV.- 7. Distributed Lyapunov-based Model Predictive Formation Tracking Control for AUVs Subject to Disturbances.- 8. Conclusions and Future Work.

Yang Shi received his Ph.D. degree in electrical and computer engineering from the University of Alberta, Canada, in 2005. From 2005 to 2009, he was an Assistant Professor and an Associate Professor with the Department of Mechanical Engineering, University of Saskatchewan, Canada, before joining the University of Victoria, where he is currently a Professor with the Department of Mechanical Engineering. He was a Visiting Professor with the University of Tokyo, Tokyo, Japan, in 2013. His current research interests include networked and distributed systems, model predictive control, cyber-physical systems, robotics and mechatronics, navigation and control of autonomous systems (AUV and UAV), and energy system applications. Professor Shi has received several professional and academic awards, including the 2017 IEEE Transactions on Fuzzy Systems Outstanding Paper Award for his coauthored paper, and the Humboldt Research Fellowship for Experienced Researchers in 2018. He has been a member of the IEEE IES Administrative Committee since 2017, and is currently the Chair of the IEEE IES Technical Committee on Industrial Cyber-Physical Systems. He has several editorial responsibilities, including being Co-Editor-in-Chief of the IEEE Transactions on Industrial Electronics, an Associate Editor for Automatica, and an Associate Editor for IEEE Transactions on Control Systems Technology. He is a fellow of IEEE, ASME, Engineering Institute of Canada, and Canadian Society for Mechanical Engineering, and a registered Professional Engineer in British Columbia, Canada.

Chao Shen received his B.E. degree in automation engineering and M.Sc. in control science and engineering from Northwestern Polytechnical University, China in 2009 and 2012, respectively, and his Ph.D. degree in mechanical engineering from the University of Victoria, Canada, in 2018. His main research interests include model predictive control, robotics, mechatronics, deep learning and computer