Model Predictive Control (MPC) Professors and Supervisors in Australia

Hoam Chungمشاهده پروفایل

مدرس

Monash UniversityAustralia

Dr. Hoam Chung is a Lecturer in the Department of Mechanical and Aerospace Engineering at Monash University, specializing in autonomous systems, model predictive control (MPC), and robotics. His research focuses on UAV navigation, human-swarm interaction, medical diagnostics through motion analysis, and optimization algorithms. He leads the Monash Unmanned Aerial Systems (MUAS) and Monash Connected Autonomous Vehicles (MCAV) teams, advancing projects like aerial perching for inspection and shared autonomy in multi-robot systems. Research interests: MPC theory, autonomous vehicles, medical motion diagnostics, semi-infinite optimization Key collaborations: BErkeley AeRobot (BEAR) project (2000–2009), projects with Soft Robotics and Multi-Robot Systems His work contributes to UN SDGs through advancements in sustainable transportation (SDG 9, 11) and health innovations (SDG 3). Recent studies include Parkinson’s disease detection via gait analysis and UAV-based agricultural monitoring.

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Fabio Ramosمشاهده پروفایل

استاد

University of SydneyAustralia

Professor Fabio Ramos is a Professor of Robotics and Machine Learning at the University of Sydney's School of Computer Science. He holds a PhD from the University of Sydney (2008) and has held postdoctoral fellowships with the Australian Research Council (2008–2010 and 2012–2014). His research focuses on statistical machine learning for data fusion in robotics, environmental monitoring, healthcare, and mining automation. He leads projects in autonomous systems, including robotic navigation, terrain modeling, and human-robot interaction. Educations: B.Sc. and M.Sc. in Mechatronics Engineering, University of Sao Paulo (2001, 2003) Ph.D. in Robotics and Machine Learning, University of Sydney (2008) Research Interests: Professor Ramos' work addresses the integration of sensor data into actionable insights for autonomous systems. Key areas include: Statistical machine learning for large-scale data fusion Occupancy mapping and environment perception Autonomous navigation and path planning Healthcare robotics for patient monitoring Robot perception in unstructured environments Simulation-to-reality transfer in robotics Publications & Trends: His recent work emphasizes real-time robotic systems, including UAV path planning, differentiable simulators, and robust policy learning. Notable contributions include probabilistic trajectory optimization and Bayesian methods for uncertainty quantification. Awards: Sydney Accelerator Fellowship (2017) Supervisor of the Year (2017) Best Paper Awards at Robotics Science and Systems (2017) and IROS (2005) Grants & Labs: He leads the Sydney Institute for Robotics and Intelligent Systems (SIRIS) and collaborates with the Centre for Translational Data Science. His current projects include: Modeling geothermal energy potentials via geophysical data fusion Mobile health (mHealth) systems for mental health monitoring Differentiable simulators for robotic cutting and manipulation Teams: His research group includes students like Ethan HIRSCHOWITZ (Intelligent Reward Design) and Kunal OSTWAL (Curriculum Learning). Collaborations span robotics, healthcare, and environmental science domains.

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Zifan Linمشاهده پروفایل

مدرس

The University of Western AustraliaAustralia

Zifan Lin is a Lecturer and PhD student in the Multi-Discipline Doctor of Philosophy program at the School of Engineering, University of Western Australia. His research focuses on power electronics, underwater imaging, and control systems, with notable contributions to inverter technology, DC microgrid systems, and machine learning applications in vision systems. He actively collaborates on projects spanning Australia and other regions, addressing challenges in renewable energy integration and advanced image processing. His academic background includes multi-disciplinary training in engineering, complementing his teaching duties. Research interests emphasize practical solutions for energy systems (e.g., DC-DC converters, NPC inverters) and underwater image enhancement networks like TANet and ReX-Net. Recent work explores semantic feature refinement for mask detection and robust control schemes for electric motors using reinforcement learning. Publications highlight innovations in space vector modulation strategies, finite-set model predictive control, and agricultural drying technologies. While no specific awards are listed, his prolific output in top journals like IEEE Transactions and LWT-FOOD SCIENCE AND TECHNOLOGY reflects academic recognition. His advising and grant activities remain unspecified in available data.

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Boyang Liمشاهده پروفایل

مدرس

University of Newcastle Australia (UON)Australia

Dr. Boyang Li is a Lecturer in Aerospace Systems Engineering at the University of Newcastle, Australia. His career includes roles as Research Assistant Professor at Hong Kong Polytechnic University (2020–2023), where he founded the Autonomous Aerial Systems Laboratory (AASL), and postdoctoral research at Nanyang Technological University (Singapore) and the University of Edinburgh (UK). He holds a PhD from Hong Kong Polytechnic University and B.Eng./M.Eng. degrees from Northwestern Polytechnical University, China. Education: Doctor of Philosophy, Hong Kong Polytechnic University (2018) M.Eng., Northwestern Polytechnical University (2015) B.Eng., Northwestern Polytechnical University (2012) Research Interests: Dr. Li's work focuses on advancing uncrewed aerial vehicle (UAV) technology, including nonconventional UAV configurations (VTOL tail-sitters), flight dynamics integration with advanced control methods (e.g., Model Predictive Control), and trajectory optimization. His empirical research involves field experiments with aerial and underwater robotic systems. Key themes include autonomous navigation, sensor integration, and real-world applications in infrastructure inspection and environmental monitoring. Grants & Funding: NSW Space Research Network Project ($14,922, 2025) RuralAI Sensor Cluster Development ($15,000, 2023) Lab2Field RuralAI Kit ($15,000, 2023) AU$22.5K Start-up Fund (2023–2024) Supervision: Dr. Li has supervised/co-supervised 6 completed and 1 ongoing HDR student, focusing on UAV control systems, underwater robotics, and reinforcement learning applications. Notable advisees include Li-yu Lo (MPhil), Yefeng Yang (PhD), and Haochen Hu (MSc). Labs & Teams: He leads the Autonomous Aerial Systems Group at the University of Newcastle and collaborates with the High-speed Thermo-fluid and MAV/UAV Lab (HK PolyU). His lab develops UAV systems for applications like infrastructure inspection and advanced air mobility.

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Jie Baoمشاهده پروفایل

استاد

University of New South WalesAustralia

Professor Jie Bao is a Process Control expert specializing in dissipativity/passivity-based control, particularly for complex industrial systems. He leads the Process Control Research Group at the School of Chemical Engineering, University of New South Wales (UNSW), and directs the ARC Research Hub for Integrated Energy Storage Systems. His research focuses on distributed control systems, energy storage technologies (e.g., flow batteries), aluminum smelting processes, and big data-driven control methodologies. Education: PhD (Process Control) from the University of Queensland, BE and ME from Zhejiang University. Research Grants: Over AUD 18 million in competitive funding, including ARC Discovery Projects, Industry Hubs, and CSIRO collaborations. Notable projects include: ARC Research Hub for Integrated Energy Storage Solutions (AUD 3.189M, 2019–2025) ARC Research Hub for Smart Process Design and Control (AUD 5.312M, 2023–2027) Long Duration Energy Storage Solutions via Vanadium Flow Batteries (AUD 1.815M, 2023–2026) Research Interests: Dissipativity theory, networked control systems, behavioral systems theory, and applications in energy storage, aluminum smelting, and membrane systems. Leadership & Service: Associate Editor for Journal of Process Control and Journal of Franklin Institute . ARC College of Experts member. Serves on the IFAC Technical Committees for Chemical Process Control and Mining/Metal Processing. Teaching: CEIC3006 Process Dynamics and Control, CEIC8102 Advanced Process Control, and mentor for design projects (CEIC4000).

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Anna Lidfors Lindqvistمشاهده پروفایل

مدرس

University of Technology SydneyAustralia

Dr. Anna Lidfors Lindqvist is a Lecturer at the School of Mechanical and Mechatronic Engineering, University of Technology Sydney (UTS), where she specializes in vehicle dynamics, control systems, and engineering education innovation. She holds a PhD from UTS (2018-2022) focused on vehicle dynamics and solar-electric vehicles, and is fluent in Swedish. Since December 2022, she has served as a full-time lecturer, previously contributing as a Curriculum Developer (2021-2023) where she redesigned engineering programs and integrated sustainable principles into curricula. Her research bridges mechanical engineering and pedagogical innovation, with dual focuses on: Net-zero technologies: Developing stability control systems for solar-electric vehicles, vibration analysis in transportation, and lightweight vehicle dynamics under variable loads Educational reform: Creating AI-integrated project frameworks (PAIIF), implementing formative sprints for feedback, and designing authentic assessment methods like threshold exams Publication analysis reveals consistent themes in vehicle control systems (SMC/MPC applications) and transformative engineering education frameworks, with recent expansion into AI-driven curriculum design. Awards & Honors: Citation for Outstanding Early Career Contribution to Engineering Education (2024) UTS Vice-Chancellor’s Early Career Teaching Award (2023) AAEE Academy Fellowship (2023) IEEE VTS NSW Research Contest prizes (2021) Higher Degree Research Excellence Award (2019) She coordinates the Mechanical Design Fundamentals Studio 1 course using Agile-inspired sprints, supervises Capstone students, and secured the 'UTS Wool Bioharvesting' grant (2024-2027). Previously managed the Australian Technology Network Solar Car team.

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Saeid Nahavandiمشاهده پروفایل

استاد

Swinburne University of TechnologyAustralia

Roles and Affiliations: Distinguished Professor Saeid Nahavandi is the inaugural Associate Deputy Vice-Chancellor (Research) and Chief of Defence Innovation at Swinburne University of Technology. He leads defence innovation and research strategy, with a focus on autonomous systems, robotics, and AI. Previously, he served as Pro Vice-Chancellor (Defence Technologies) at Deakin University and founded its Institute for Intelligent Systems Research and Innovation. Research Focus: Specializes in robotics, haptics, autonomous systems, AI, and advanced modelling/simulation. His work bridges academia and industry, with collaborations spanning Airbus, Boeing, NASA, and NATO. He has secured over $150M in funding and established three tech startups. Key research areas include motion simulation, teleoperation systems, and defence technologies. Articles Overview: Over 1,300 publications span AI, robotics, and control engineering. Recent work emphasizes autonomous navigation reviews, uncertainty-aware AI, and motion cueing algorithms. His research addresses real-world applications like driver distraction detection and robotic ultrasound. Awards and Recognition: Recipient of the 2022 Clunies Ross Entrepreneur of the Year Award, 2021 Australian Space Awards Researcher of the Year, and multiple engineering excellence accolades. A Fellow of ATSE, IEEE, and other leading institutions. Grants & Industry Impact: Led ARC Training Centres for automated vehicles and energy storage. Notable grants include a $15M ARC Training Centre for Automated Vehicles in Rural/Remote Regions (2024–2029). Collaborates globally on defence, aerospace, and smart transportation projects. Labs & Teams: Heads Swinburne’s Defence Innovation Group and collaborates with Harvard University (as an Associate) and the University of Windsor (adjunct professor). Advises governments and industries on technology strategy and innovation.

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