Data-Driven Dynamics, Modeling and Control (DYNAMO)

DYNAMO develops physics-informed and data-driven methods for robust modeling, control and decision support in mechatronics, robotics and offshore energy systems.

DYNAMO studies the analysis, modeling and control of complex engineering systems operating in uncertain and dynamic environments. The group combines physics-based understanding, data and experimental insight to develop robust, interpretable and deployable solutions.

Mechatronic and robotic systems: design, analysis, modeling and control.
System identification, nonlinear dynamics and stochastic systems.
Control architectures for autonomous and semi-autonomous systems.
System-level modeling, digital twins, discrete-event simulation and decision support.
Machine learning and artificial intelligence for monitoring, optimization and predictive maintenance.
Applications in offshore wind, marine and autonomous systems, robotics and reliability.

Research Projects

Status: On-going

Robotics and data science are being explored as means to transform fruit and berry production into a smarter and more sustainable system. Robot- and data-driven solutions are being developed and tested in real agricultural environments, with the aim that labor challenges may be reduced, productivity increased, and sustainability improved. Practical tools that can be trusted by farmers are intended to be created. Through collaboration among growers, researchers, and technology partners, measurable, replicable, and future-ready production systems for crops such as raspberries are being designed.

Collaborators: HVL, Vestlandsforsking, nLink, Sognabær, Sogn Frukt og Grønt

Status: On-going

Operation and Maintenance (O&M) of offshore wind farms are analyzed using a Discrete Event Simulation–based optimization framework. Offshore wind systems are modeled as complex, uncertain environments where failures, weather, logistics, and resources interact dynamically. By combining simulation with optimization, different maintenance and logistics strategies are evaluated to identify cost-effective solutions. The approach is intended to reduce O&M costs, improve turbine availability, and support better decision-making for offshore wind farm planning and operations.

Collaborators: HVL, Safetec As, Deep Ocean, Deep Wind Offshore

Extension of iSeaPower for offshore operation and maintenance through the integration of optimization and discrete-event simulation, in collaboration with Helmholtz-Zentrum Hereon

Adaptive backstepping control for underwater robots in complex environments — Jalalnezhad, Mostafa; Keymasi-Khalaji, Ali; Ghane, Mahdi (2025).
Pitch actuator fault-tolerant control of wind turbines via an L1 adaptive sliding mode control (SMC) scheme — Fayazi, A.; Ghayoumi Zadeh, H.; Ahmadian, H.; Ghane, M.; Seryasat, O. R. (2024).
Control of a group of uncertain wheeled robots with global inputs in the presence of obstacles — Mehrabani, M. B. M.; Khalaji, A. K.; Ghane, M. (2023).
A physically motivated control algorithm for an autonomous underwater vehicle — Khalaji, A. K.; Ghane, M. (2022).
Condition monitoring of spar-type floating wind turbine drivetrain using statistical fault diagnosis — Ghane, M.; Rasekhi Nejad, A.; Blanke, M.; Gao, Z.; Moan, T. (2018). Relevant publications

Head of Research Group

Mahdi Ghane

Associate Professor

Erik Kyrkjebø

Modified 17.09.26