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Advanced Model Predictive Control for Autonomous Marine Vehicles - Shi, Yang

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        Présentation Advanced Model Predictive Control For Autonomous Marine Vehicles Format Broché

         - Livre Littérature Générale

        Livre Littérature Générale - Shi, Yang - 01/02/2024 - Broché - Langue : Anglais

        . .

      • Auteur(s) : Shi, Yang - Zhang, Kunwu - Wei, Henglai - Shen, Chao
      • Editeur : Springer International Publishing Ag
      • Langue : Anglais
      • Parution : 01/02/2024
      • Format : Moyen, de 350g à 1kg
      • Nombre de pages : 216
      • Expédition : 335
      • Dimensions : 23.5 x 15.5 x 1.2
      • ISBN : 3031193563



      • Biographie:
        the recipient of the Natural Science and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship in 2020, and he is currently holding a postdoc position with the Real-time Adaptive Control Engineering Lab at University of Michigan. He served as an Associate Editor of the IEEE ISIE 2019 and the IEEE ICCA 2020. He is a member of IEEE. Henglai Wei received his B.Sc. and M.Sc. degrees in mechanical engineering and automatic control from Northwestern Polytechnical University, Xi'an, China, in 2014 and 2017, respectively. He is currently working toward his Ph.D. degree in mechanical engineering with the University of Victoria, Canada. His current research interests include distributed model predictive control, multi-agent systems, and cooperative marine robots. He is an active reviewer for more than ten international journalsand conferences. Kunwu Zhang received his M.A.Sc. and Ph.D. degrees in Mechanical Engineering from the University of Victoria, BC, Canada, in 2016 and 2021, respectively. Currently, he is a Postdoctoral Research Fellow and Lecturer with the Department of Mechanical Engineering, University of Victoria, BC, Canada. His current research interests include adaptive control, model predictive control, data-driven control, optimization, and mechatronics. He is an active reviewer for more than 15 international journals and conferences....

        Sommaire:
        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 vision. DrShen was the winner of the 2018 IEEE SMCS Thesis Grant Initiative for his Ph.D. thesis on model predictive control for underwater robotics...

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