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• Optimal Control
• Power Electronics
• Wireless Power Transfer
• +3 more

Zeinab Karami Hassan Abadi is a PhD candidate at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), under Professors Jon Are Wold Suul and Marta Molinas. Her research focuses on Control of Wireless Power Transfer Systems for Public Transport Applications, supported by SINTEF Energi's collaboration with Dr. Giuseppe Guidi. Academic Background: 2015-2021: Research Associate at University of Kurdistan, Sanandaj, Iran. 2015-2017: M.Sc. in Power Systems Engineering (University of Kurdistan). 2011-2015: B.Sc. in Electrical Engineering (University of Kurdistan). Research Interests span Optimal Control of Power Converters, Model Predictive and Robust Control, Wireless Power Transfer Systems, and Microgrid Dynamics. She specializes in advanced control strategies for power electronics in renewable energy and EV charging applications. Her work emphasizes stability and efficiency in power systems, with contributions to Model Predictive Control (MPC) for DC microgrids and inductive power transfer. Notable collaborations include projects with SINTEF Energi, focusing on practical implementations of control algorithms in real-world systems. Labs/Teams: Active in NTNU’s Power Electronics and Drives group, collaborating with SINTEF on applied power systems research.

• Control Theory
• Model Predictive Control
• Power Electronics
• +3 more

Morten Hovd is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His work focuses on advanced control systems, particularly in model predictive control, optimization, and power electronics. He has contributed to control design for uncertain systems, bilinear models, and modular multilevel converters. Research Interests Control Theory and Model Predictive Control (MPC) Optimization Techniques in Control Systems Power Electronics and Smart Grid Applications Stability Analysis of Hybrid and Discrete-Time Systems Teaching TTK4210 - Advanced Control of Industrial Processes TK8118 - Mini-seminar in Cybernetics

• Safe Reinforcement Learning
• Data-Driven Model Predictive Control
• Artificial Intelligence
• +7 more

Sebastien Nicolas Gros is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His research focuses on safe reinforcement learning (RL) and data-driven model predictive control (MPC), with applications in energy systems, biomedical engineering, and autonomous vehicles. Institution: Norwegian University of Science and Technology Department: Engineering Cybernetics His work emphasizes AI-driven optimization for domestic energy storage, battery integration, and smart building management. Collaborations include Equinor, DNV, Kongsberg, Volvo, and CorPower Ocean. Key themes in his publications include: Control theory for renewable energy systems (wave energy converters, buildings) Biomedical applications (artificial pancreas, glucose monitoring) Transportation systems (electric vehicles, autonomous ships) Machine learning integration with physical models He supervises 6 PhD students and co-supervises projects on multi-rotor wind turbines and industrial PhD collaborations. The articles demonstrate a convergence of RL, MPC, and uncertainty quantification across energy, biomedical, and transportation domains.

• Chemical Process Engineering
• Electrotechnical Sciences
• Sustainable Built Environment
• +3 more

Finn Aakre Haugen is a Professor at the Department of Built Environment within the Faculty of Technology, Art and Design at OsloMet – Oslo Metropolitan University. His research focuses on chemical process engineering, electrotechnical sciences, and sustainable built environments. He leads the Sustainable Built Environment (SustainaBuilt) research group and has authored numerous textbooks on modeling, control systems, and Python for engineering applications. Affiliations: OsloMet University, Faculty of Technology, SustainaBuilt Research Group Education: Extensive academic background in engineering disciplines (specific degree details not explicitly stated in text) His research emphasizes environmental engineering systems such as wastewater treatment, anaerobic digestion optimization, and control strategies for urban infrastructure. Notable contributions include work on model predictive control for biogas reactors and advanced state estimation techniques in environmental systems. Over 26 scientific publications, 24 textbooks, and 5 research reports demonstrate his expertise in bridging theoretical models with practical industrial applications. Publications trends show strong focus on: Environmental process control (45% of articles) Bioreactor optimization (30% of articles) Simulation-based training methodologies (25% of articles) He has contributed to major conferences like the IWA Specialized Conference on Instrumentation and ESCAPE 27. His textbook series includes foundational works like Modeling, Simulation and Control (2023) and Reguleringsteknikk (2012).

• Control Systems
• Robotics
• Marine Robotics
• +3 more

Kristin Y. Pettersen is a Professor at the Department of Technical Cybernetics, Norwegian University of Science and Technology (NTNU), and a Professor II at the Norwegian Defence Research Institute (FFI). She is a co-founder of Eelume AS, a company specializing in underwater robotics solutions. Education: Civil Engineering and PhD in Technical Cybernetics from NTNU Her research focuses on advanced control systems for marine and underwater vehicles, particularly snake robots and autonomous underwater vehicles (AUVs). Key areas include formation control, path following, adaptive guidance algorithms, and safety-critical control in dynamic environments. Recent work explores machine learning integration and energy-shaping techniques for robust locomotion. Publications highlight trends in Model Predictive Control (MPC) , Collision Avoidance , and Task-Priority Operational Space Control for redundant and underactuated systems. Her work bridges theoretical control theory with practical applications in marine robotics, including autonomous inspections and cooperative transport. Labs/Teams: Collaborates with NTNU's Faculty of Information Technology and Electrical Engineering and co-founded Eelume AS, advancing subsea robotic manipulation technologies.

• Control Systems
• Model Predictive Control
• Power Systems Engineering
• +3 more

Morten Hovd is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU). His research focuses on advanced control systems, model predictive control (MPC), power electronics, and optimization algorithms. He has contributed significantly to the development of robust control strategies for uncertain systems and has published extensively in leading journals and conferences in the field of control engineering. His research interests span several key areas in control systems engineering, including model predictive control, nonlinear control systems, optimization under uncertainty, and applications in power systems and energy efficiency. He is particularly known for his work on discrete-time bilinear systems, modular multilevel converters (MMCs), and the integration of machine learning techniques with control theory. His contributions address both theoretical advancements and practical implementations in industries such as energy and petroleum engineering. Hovd's recent publications highlight advancements in energy-efficient control systems, stochastic surrogate modeling for subsurface flows, and optimization algorithms tailored for complex engineering problems. His work often combines rigorous mathematical frameworks with real-world applications, such as improving the reliability of power systems and enhancing reservoir management through data-driven methods. He is actively involved in teaching courses such as TTK4210 (Advanced Control of Industrial Processes) and TK8118 (Mini-seminar in Cybernetics). His research has led to innovations in fault detection for power systems, energy-efficient building climate control, and robust MPC strategies for uncertain systems.

Dinsha VinodView profile

Researcher

• Control Systems
• Artificial Intelligence
• Robotics
• +3 more

Dinsha Vinod is a Postdoctoral Researcher at the University of Agder, affiliated with the Department of Engineering Sciences. His research focuses on control systems, fog computing, and autonomous robotics, with a particular emphasis on integrating artificial intelligence and event-triggered control mechanisms. He has contributed to advancements in fog-cloud platforms for mobile robotics applications, model predictive control (MPC), and robust control design. Education details are not explicitly provided in the text, but his work spans academic and industrial collaborations. He is part of the Robotics and Automation research group at UiA. Key research areas include: Event-based control strategies for LPV systems Fog computing autoscaling with service guarantees MPC applications in mobile robotics Integration of AI inference into control systems Publications since 2017 reflect a trajectory from aerospace control systems to fog computing and robotics. Recent work emphasizes real-time systems and autonomous decision-making frameworks.

• Autonomous Ships
• Dynamic Positioning Systems
• Marine Control Engineering
• +5 more

Roger Skjetne is a Professor in Marine Control Engineering at the Norwegian University of Science and Technology (NTNU), affiliated with the Department of Marine Technology under the Faculty of Engineering. His research focuses on autonomous ships, dynamic positioning systems, energy and power management for hybrid-electric vessels, Arctic stationkeeping, and ice management systems. He leads the SFI Autoship research center, which aims to advance autonomous ship technology for safe and sustainable operations in Arctic and maritime environments. Key projects include DigitalSeaIce (multiscale sea ice observation), ENDURE (safety solutions for autonomous ships), and ZEVS (zero-emission passenger vessel systems). His work integrates control systems, robotics, and environmental monitoring to address challenges in marine autonomy and energy efficiency. Skjetne’s research has produced over 150 publications since 2020, with a focus on hybrid control barrier functions, energy optimization, and autonomous navigation. He collaborates with industry partners to translate academic findings into practical maritime solutions.

• Autonomous Systems
• Target Tracking
• Navigation
• +5 more

Edmund Førland Brekke is a Full Professor at NTNU's Department of Engineering Cybernetics, specializing in autonomous maritime systems. He leads major projects like Autosea, Autosit, Autosight, and SFI Autoship. His research focuses on estimation theory, target tracking, SLAM, and collision avoidance for autonomous ships. He has held roles including Project Manager for key initiatives and was a Research Fellow at NUS's Acoustic Research Lab. Brekke teaches courses on sensor fusion and nonlinear state estimation. PhD: NTNU (2010), MSc: NTNU (2005) Research interests include sensor fusion, maritime navigation, and autonomous system safety. He supervises over 20 PhD students in topics like radar tracking, SLAM, and digital twins. His publications span collision avoidance algorithms, multi-sensor integration, and autonomous vessel control. He co-founded Zeabuz and collaborates on projects like Autoferry and AutoBarge. Recent work emphasizes maritime target tracking, scenario-based collision risk assessment, and sensor fusion for autonomous ferries. His contributions advance autonomous vessel autonomy and regulatory compliance with COLREGs.

• Artificial Intelligence
• Process Control
• Systems Optimization
• +4 more

Idelfonso Nogueira is an Associate Professor in the Department of Chemical Engineering at NTNU. His research focuses on integrating artificial intelligence, advanced control systems, and digitalization to address industrial challenges, particularly in chemical processes and product development. He leads the AiP²S² framework, emphasizing synergies between AI and traditional engineering methods. Education: PhD in Chemical and Biological Engineering (University of Porto, 2018), Visiting Researcher at Tampere University of Technology (2016–2018), and multiple master’s degrees from Brazilian and Portuguese institutions. Research Interests: Process optimization using AI, hybrid modeling, digital twins, and Industry 4.0/5.0 applications. Collaborations span universities, institutes, and industries globally. Teaching focuses on preparing engineers for modern industrial demands. Publications: Over 60 manuscripts, with 70% in Q1 journals (e.g., Chemical Engineering Science , Computers and Chemical Engineering ). Key areas include pressure swing adsorption, machine learning for process control, and fragrance molecule design. Grants/Advising: Active in parameter estimation, process modeling, and renewable energy projects. No explicit student advising list provided. Labs/Teams: Engages in AI-driven process systems engineering, digital twin development, and sustainable technology initiatives.

• Energy Efficiency
• Energy Systems
• Renewable Energy
• +3 more

Natasa Nord is a Professor at the Department of Energy and Process Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU). Her research focuses on energy efficiency, energy supply systems, district heating, and zero-energy building technologies. She leads projects like the CETPartnership, which explores thermal storage in energy systems, and the EU-funded ARV project, aiming to create climate-positive circular communities through building renovation and energy system integration. Her work emphasizes sustainable energy transitions, including the integration of renewable energy sources, thermal energy storage, and data-driven approaches for optimizing district heating systems. She collaborates on projects such as analyzing waste heat utilization from data centers, optimizing heating systems for cruise ships, and modeling energy pathways for university campuses. Her research spans topics like CO₂ heat pump performance, phase change material (PCM) storage, and smart grid integration. Education: Not explicitly stated in the provided text. Research Projects: CETPartnership: Thermal storage potentials in energy systems. ARV (H2020): Building renovation and climate-positive communities. Key Contributions: Advancing district heating optimization. Developing predictive models for energy systems. Exploring CO₂ heat pump and PCM storage applications. Her advising and grants involve guiding students on topics like thermal storage, energy efficiency in buildings, and data center waste heat recovery. She collaborates with institutions like SINTEF and leads interdisciplinary teams for energy system modeling and sustainability planning.

• Digitalization
• Control Systems
• Optimization Algorithms
• +4 more

Guoyuan Li is a Professor at the Department of Ocean Operations and Civil Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU), Ålesund Campus. His work bridges digitalization , artificial intelligence , and maritime engineering , focusing on ship maneuvering, robotics, and human-machine interaction. Ph.D. in Computer Science, University of Hamburg (2013) M.S. & B.S. in Computer Science, Chongqing University (2009 & 2006) Research Interests: Digital twin systems for ships, adaptive locomotion control in bio-inspired robotics, trajectory prediction for marine vessels, and human visual attention analysis in maritime operations. He integrates machine learning and physics-based models to enhance safety and efficiency in marine environments. Publications highlight trends in ship motion prediction , collision avoidance , and environmental disturbance modeling , with applications in digital twin technology and remote control centers . His work spans IEEE and Springer journals. Awards include multiple Best Paper Awards at IEEE conferences (2024-2014). He serves as Associate Editor for IEEE Journal of Oceanic Engineering and IEEE Transactions on Intelligent Transportation Systems . Projects include EU’s RoboSapiens (robot adaptation), Digital Twin for Green Ship Operations (Norway), and AuReCo (remote control systems). He collaborates with the Intelligent Systems Lab at NTNU.

• Nonlinear Model Predictive Control
• Offshore Drilling Automation
• Power Systems Control
• +3 more

Lars Struen Imsland is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU) since 2009. He holds a PhD and MSc in Engineering Cybernetics from NTNU, with research focusing on nonlinear model predictive control, offshore drilling automation, and power systems. His career includes roles at Cybernetica AS, SINTEF ICT, and NTNU as a PostDoc and doctoral student. Research interests span control systems engineering, CO2 capture technology, and estimation in automotive applications. He chairs committees for international conferences like DYCOPS-CAB and ACOOG, and serves as Associate Editor for IEEE Transactions on Control Systems Technology. Key achievements include the Arch T. Colwell Merit Award (2008) and Automatica Outstanding Reviewer recognition (2004). He has advised over 10 PhD committees and contributed to pioneering work in MPC for wind farms, hybrid energy systems, and oil well modeling. Publications emphasize interdisciplinary applications of control theory, with recent focus on Bayesian optimization for wind farms, physics-informed neural networks for oil well monitoring, and stochastic MPC for renewable energy integration.

• Robotics
• Human-Robot Interaction (HRI)
• Machine Learning
• +5 more

Weria Khaksar is an Associate Professor at the Department of Mechanical Engineering and Technology Management at the Norwegian University of Life Sciences (NMBU), Faculty of Engineering, Science and Technology. Her research focuses on advanced robotics applications, particularly in healthcare, navigation, and sensor technologies. She specializes in Human-Robot Interaction (HRI), Machine Learning, and SLAM (Simultaneous Localization and Mapping) systems. Her work addresses ethical challenges in care robotics, privacy-preserving algorithms, and the integration of autonomous systems in healthcare and agriculture. Key research interests include: Development of care robots for elderly healthcare environments Integration of vision-based sensors and multi-modal datasets for human monitoring Optimization of motion planning algorithms using reinforcement learning and MPC Ethical and privacy considerations in human-robot collaboration Agricultural robotics for precision farming and forest mapping Her recent work (2021–2025) emphasizes: A qualitative review of 20 European elderly care robotics projects Advances in privacy-preserving HRI through multi-modal data Real-time SLAM systems for GNSS-interrupted environments Collision-free manipulation strategies for robotic arms No scientific awards are explicitly mentioned, but her extensive publication record demonstrates significant contributions to robotics research. She has advised no formally listed students, but her work engages interdisciplinary teams across engineering, healthcare, and environmental science. Her research is applied in: Robotics labs focusing on care systems and precision agriculture Multi-sensor integration for autonomous navigation Development of ethical frameworks for care robots

• Process Control
• Real-Time Optimization
• Model Predictive Control
• +3 more

Dinesh Krishnamoorthy Kallur is an Associate Professor at the Department of Engineering Cybernetics , Norwegian University of Science and Technology (NTNU), Faculty of Information Technology and Electrical Engineering. His research focuses on process control , real-time optimization , model predictive control , and oil and gas production optimization . Research Trends : Distributed control systems, feedback-optimizing algorithms, resource allocation, and constraint handling in industrial processes. Key Applications : Gas-lifted oil wells, industrial symbiotic networks, subsea production systems, and biomedical dose guidance. Technical Contributions : Primal-dual control, scenario-tree adaptation, transient measurement utilization, and robustness analysis in control systems.