Bakgrunn og aktiviteter
Project proposals for ITK 2019-2020
A novel approach to Reinforcement Learning
Reinforcement Learning (RL) is a fast-developing field of research, which deals with exploiting data to improve decision-making for dynamic systems. While most RL approaches are based on using Deep Neural Network to support the decision making, we are developing alternative approaches based on constrained optimization. Our ambition with these new approaches is to be able to impose safety constraints for AI-based decision making. A number of questions are, however, still open. One open question we will start investigating in this project is the developments of rich parametrizations of the constrained optimization problem, and their effect on the performance of the AI agent.
The project is suitable for two students working in a team. The student(s) taking part in this project will discover cutting-edge computer tools for optimization and optimal control, and learn some of the theory behind RL and optimal control.
NTNU ITK supervisor: Sebastien Gros (email@example.com)
Risk-management in predictive control
In the context of predictive control for mission planning (e.g. autonomous vehicle, drone, etc.), the risk associated to a selected plan can be of crucial importance for the operator. A very useful view of the problem is to measure the risk in terms of the probability of failing the mission, and the cost of failure. Assessing accurately the probability of failing a mission under a given plan (in addition to e.g. some low-level control systems) is, unfortunately, fairly difficult to do at a reasonable computational cost. In this project, we will explore novel ideas to perform this estimation at a lower computational cost.
The project is suitable for two students working in a team. The student(s) taking part in this project will learn concepts from advanced statistics, and Markov Processes, and learn how to handle these problems in the computer.
NTNU ITK supervisor: Sebastien Gros (Sebastien.firstname.lastname@example.org)
Vitenskapelig, faglig og kunstnerisk arbeid
- (2020) Data-driven Economic NMPC using Reinforcement Learning. IEEE Transactions on Automatic Control. vol. 65 (2).
- (2019) Enhancing the net energy of wind turbine using wind prediction and economic NMPC with high-accuracy nonlinear WT models. Renewable Energy.
- (2019) Dual-mode Batch-to-batch Optimization as a Markov Decision Process. Industrial & Engineering Chemistry Research. vol. 58 (30).
- (2019) A dual Newton strategy for tree-sparse quadratic programs and its implementation in the open-source software treeQP. International Journal of Robust and Nonlinear Control. vol. 29.
- (2019) Engineering Wake Induction Model For Axisymmetric Multi-Kite Systems. Journal of Physics: Conference Series. vol. 1256 (1).
- (2019) Wind Turbine Fatigue Reduction based on Economic-Tracking NMPC with Direct ANN Fatigue Estimation. Renewable Energy. vol. 147.
- (2019) A reference model for airborne wind energy systems for optimization and control. Renewable Energy. vol. 140.
- (2019) Drag-mode airborne wind energy vs. wind turbines: An analysis of power production, variability and geography. Energy. vol. 193.
- (2019) Autonomous docking using direct optimal control. IFAC-PapersOnLine. vol. 52 (21).
- (2019) Rare event chance-constrained optimal control using polynomial chaos and subset simulation. Processes. vol. 7 (4).
- (2019) MPC approaches for modulating air-to-water heat pumps in radiant-floor buildings. Control Engineering Practice. vol. 95.
- (2019) Ideal Benefits of Exceeding Fixed Voltage Limits on Lithium-Ion Batteries with Increasing Cycle Age. Journal of Power Sources.