Sebastien Gros
Background and activities
Current research
My main current research concerns safe Reinforcement Learning and Data-Driven MPC. I currently have 5 PhD students. This work is strongly connected to the industry, with large industrial partners such as DNV GL and Konsberg Marine.
I also co-supervise several PhD projects related to the Artificial Pancreas problem, multi-rotor wind turbine control. Finally, I supervise and cosupervise several industrial PhD projects based at Volvo (Sweden), focusing on powertrain control, 2nd life of batteries, and traffic management.
To access my publications, please consult:
- https://www.researchgate.net/profile/Sebastien-Gros-2
- https://scholar.google.no/citations?user=38fYqeYAAAAJ&hl=en
MSc thesis projects for 2020-2021
IoT Software and Algorithm for Smart buildings
In a previous MSc project, we have developped a software architecture to manage a smart house from a Raspberry Pi. The software allows the management of connected heat pumps, and the reading and logging of multiple sensors (temperature, power, etc.). This project will be a continuation of that previous project, adding control functionalities in the software. In particular, we would like to investigate the possibility of deploying MPC and estimation algorithms insise the Raspberry Pi. The student in this project will have to familiarise him/herself to the existing software infrastructure, and develop it further. The project will (if possible) include the development and testing of control algorithms.
NTNU ITK supervisor: Sebastien Gros (sebastien.gros@ntnu.no),
Low-cost smart house modelling
We have recently equipped a house with smart sensors and a connected heating system allowing to steear the heat pumps (4 indoor units), measure the house power consumption (2 smart meters) and measure the temperature in the different rooms. An early modelling of the house dynamics has been performed, using classic System Identification (SYSID) tecniques. A large amount of data has been collected on the house over the winter. The data collection is ongoing. In this project, we would like to investigate further modelling approaches to model smart houses. The end goal would be to develop a modelling technique that can be deployed on different houses with a limited amount if engineering work. Various techniques will be considered, ranging from classic SYSID to Gaussian Processes and more advance probabilistic modelling.
NTNU ITK supervisor: Sebastien Gros (sebastien.gros@ntnu.no),
Optimization of Smart Houses for the NordPool Electrical Spot Market
Norway operates under the Nord Pool open electrical power market system, whose data are publicly available online. Among other things, the hourly price of electricity (spot market) is available for the next 24h for the different regions of Norway. For regular consumers these prices are of secondary importance, as their electricity bill is managed by their provider (e.g. Trøndelagkraft), who charges a uniform price per kWh for a given month, hence smoothing the variations of the spot market. Providers perform this smoothing at a certain cost for the consumer. Increasingly, private electricity consumers can access the spot market.
In this project, we want to investigate the optimization of the electrical consumption of a smart house against the electrical spot market. We will investigate the practical aspects, the decision-making aspect and the implementation. We will investigate the benefits in terms of cost savings and power system alleviation.
NTNU ITK supervisor: Sebastien Gros (sebastien.gros@ntnu.no)
Real-time Driving Strategy Optimisation
With help from real-time driving strategy optimisation, DNV Fuel Fighter wishes to change the driving strategy while the car drives on the track and gets live telemetry data that could improve it.
Next year we will be making a new car, which means that we don't have data from the dynamics to the car. You won't be able to simulate everything right in advance. In this task the student will make a program, that with help of real-time data which comes in while driving, corrects the mathematical models and optimise during the driving and then can be more energy efficient.
NTNU ITK supervisor: Sebastien Gros (sebastien.gros@ntnu.no)
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
Journal publications
- (2022) A Semi-Distributed Interior Point Algorithm for Optimal Coordination of Automated Vehicles at Intersections. IEEE Transactions on Control Systems Technology.
- (2022) Reinforcement learning-based NMPC for tracking control of ASVs: Theory and experiments. Control Engineering Practice. vol. 120.
- (2021) Can AI Abuse Personal Information in an EV Fast-Charging Market?. IEEE transactions on intelligent transportation systems (Print).
- (2021) Verification of Dissipativity and Evaluation of Storage Function in Economic Nonlinear MPC using Q-Learning. IFAC-PapersOnLine. vol. 54 (6).
- (2021) Risk-Based Model Predictive Control for Autonomous Ship Emergency Management. IFAC-PapersOnLine.
- (2021) Reinforcement Learning of the Prediction Horizon in Model Predictive Control. IFAC-PapersOnLine.
- (2021) Optimization of the Model Predictive Control Update Interval Using Reinforcement Learning. IFAC-PapersOnLine.
- (2021) Bilevel Optimization for Bunching Mitigation and Eco-Driving of Electric Bus Lines. IEEE transactions on intelligent transportation systems (Print).
- (2021) The value of airborne wind energy to the electricity system. Wind Energy Science.
- (2021) Optimal Model-Based Trajectory Planning With Static Polygonal Constraints. IEEE Transactions on Control Systems Technology.
- (2021) Safe reinforcement learning using robust MPC. IEEE Transactions on Automatic Control.
- (2020) A game approach for charging station placement based on user preferences and crowdedness. IEEE transactions on intelligent transportation systems (Print).
- (2020) Data-driven Economic NMPC using Reinforcement Learning. IEEE Transactions on Automatic Control. vol. 65 (2).
- (2020) Experimental validation of a semi-distributed sequential quadratic programming method for optimal coordination of automated vehicles at intersections. Optimal control applications & methods. vol. 41 (4).
- (2020) Optimisation-based coordination of connected, automated vehicles at intersections. Vehicle System Dynamics. vol. 58 (5).
- (2020) Computing the power profiles for an Airborne Wind Energy system based on large-scale wind data. Renewable Energy. vol. 162.
- (2020) Optimization-Based Automatic Docking and Berthing of ASVs Using Exteroceptive Sensors: Theory and Experiments. IEEE Access. vol. 8.
- (2020) Reinforcement Learning-Based Tracking Control of USVs in Varying Operational Conditions. Frontiers in Robotics and AI. vol. 7 (32).
- (2020) Combining system identification with reinforcement learning-based MPC. IFAC-PapersOnLine. vol. 53 (2).
- (2020) Precision Deep-Stall Landing of Fixed-Wing UAVs using Nonlinear Model Predictive Control. Journal of Intelligent and Robotic Systems. vol. 101.