@inproceedings{53643, author = {{Amer, Abdelhakim and Mehndiratta, Mohit and le Fevre Sejersen, Jonas and Pham, Huy Xuan and Kayacan, Erdal}}, booktitle = {{2023 21st International Conference on Advanced Robotics (ICAR)}}, publisher = {{IEEE}}, title = {{{Visual Tracking Nonlinear Model Predictive Control Method for Autonomous Wind Turbine Inspection}}}, doi = {{10.1109/icar58858.2023.10406329}}, year = {{2024}}, } @inproceedings{53796, author = {{Amer, Abdelhakim and Álvarez-Tuñón, Olaya and Uğurlu, Halil İbrahim and Le Fevre Sejersen, Jonas and Brodskiy, Yury and Kayacan, Erdal}}, booktitle = {{2023 21st International Conference on Advanced Robotics (ICAR)}}, publisher = {{IEEE}}, title = {{{UNav-Sim: A Visually Realistic Underwater Robotics Simulator and Synthetic Data-Generation Framework}}}, doi = {{10.1109/icar58858.2023.10406819}}, year = {{2024}}, } @article{53962, author = {{Álvarez-Tuñón, Olaya and Brodskiy, Yury and Kayacan, Erdal}}, issn = {{2691-4581}}, journal = {{IEEE Transactions on Artificial Intelligence}}, pages = {{1--21}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Monocular visual simultaneous localization and mapping: (r)evolution from geometry to deep learning-based pipelines}}}, doi = {{10.1109/tai.2023.3321032}}, year = {{2023}}, } @inproceedings{53961, author = {{Álvarez-Tuñón, Olaya and Kanner, Hemanth and Marnet, Luiza Ribeiro and Pham, Huy Xuan and le Fevre Sejersen, Jonas and Brodskiy, Yury and Kayacan, Erdal}}, booktitle = {{2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}}, publisher = {{IEEE}}, title = {{{MIMIR-UW: A Multipurpose Synthetic Dataset for Underwater Navigation and Inspection}}}, doi = {{10.1109/iros55552.2023.10341436}}, year = {{2023}}, } @inproceedings{53963, author = {{Heiß, Micha and Hansen, Jakob Grimm and Li, Dengyun and Kozłowski, Michał and Kayacan, Erdal}}, booktitle = {{2023 International Conference on Unmanned Aircraft Systems (ICUAS)}}, publisher = {{IEEE}}, title = {{{PredictiveSLAM - Robust Visual SLAM Through Trajectory-Aware Object Masking}}}, doi = {{10.1109/icuas57906.2023.10155806}}, year = {{2023}}, } @inproceedings{53960, author = {{le Fevre Sejersen, Jonas and Kayacan, Erdal}}, booktitle = {{2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}}, publisher = {{IEEE}}, title = {{{CAMETA: Conflict-Aware Multi-Agent Estimated Time of Arrival Prediction for Mobile Robots}}}, doi = {{10.1109/iros55552.2023.10341937}}, year = {{2023}}, } @article{35586, author = {{Protte, Marius and Fahr, Rene and Quevedo, Daniel E.}}, issn = {{1066-033X}}, journal = {{IEEE Control Systems}}, keywords = {{Electrical and Electronic Engineering, Modeling and Simulation, Control and Systems Engineering, Electrical and Electronic Engineering, Modeling and Simulation, Control and Systems Engineering}}, number = {{6}}, pages = {{57--76}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Behavioral Economics for Human-in-the-Loop Control Systems Design: Overconfidence and the Hot Hand Fallacy}}}, doi = {{10.1109/mcs.2020.3019723}}, volume = {{40}}, year = {{2022}}, } @article{29653, author = {{Weber, Daniel and Heid, Stefan and Bode, Henrik and Lange, Jarren and Hüllermeier, Eyke and Wallscheid, Oliver}}, journal = {{IEEE Access}}, pages = {{35654–35669}}, publisher = {{IEEE}}, title = {{{Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments}}}, doi = {{10.1109/ACCESS.2021.3062144}}, volume = {{9}}, year = {{2021}}, } @article{29664, author = {{Wallscheid, Oliver}}, journal = {{IEEE Open Journal of Industry Applications}}, publisher = {{IEEE}}, title = {{{Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges}}}, year = {{2021}}, } @article{29662, author = {{Schenke, Maximilian and Wallscheid, Oliver}}, journal = {{arXiv preprint arXiv:2105.08990}}, title = {{{Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning}}}, year = {{2021}}, } @article{35589, author = {{Dragicevic, Tomislav and Parisio, Alessandra and Rodriguez, Jose and Jones, Colin and Quevedo, Daniel and Ferrarini, Luca and Preindl, Matthias and Shafiee, Qobad and Morstyn, Thomas}}, issn = {{0885-8969}}, journal = {{IEEE Transactions on Energy Conversion}}, keywords = {{Electrical and Electronic Engineering, Energy Engineering and Power Technology}}, number = {{2}}, pages = {{1311--1312}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Guest Editorial Model Predictive Control in Energy Conversion Systems}}}, doi = {{10.1109/tec.2021.3076279}}, volume = {{36}}, year = {{2021}}, } @unpublished{35588, abstract = {{We consider the joint design of control and scheduling under stochastic Denial-of-Service (DoS) attacks in the context of networked control systems. A sensor takes measurements of the system output and forwards its dynamic state estimates to a remote controller over a packet-dropping link. The controller determines the optimal control law for the process using the estimates it receives. An attacker aims at degrading the control performance by increasing the packet-dropout rate with a DoS attack towards the sensor-controller channel. We assume both the controller and the attacker are rational in a game-theoretic sense and establish a partially observable stochastic game to derive the optimal joint design of scheduling and control. Using dynamic programming we prove that the control and scheduling policies can be designed separately without sacrificing optimality, making the problem equivalent to a complete information game. We employ Nash Q-learning to solve the problem and prove that the solution is guaranteed to constitute an $\epsilon$-Nash equilibrium. Numerical examples are provided to illustrate the tradeoffs between control performance and communication cost.}}, author = {{Lu, Jingyi and Quevedo, Daniel E.}}, booktitle = {{arXiv:2103.05893}}, title = {{{A Jointly Optimal Design of Control and Scheduling in Networked Systems under Denial-of-Service Attacks}}}, year = {{2021}}, } @article{29649, author = {{Heid, Stefan and Weber, Daniel and Bode, Henrik and Hüllermeier, Eyke and Wallscheid, Oliver}}, journal = {{Journal of Open Source Software}}, number = {{54}}, pages = {{2435}}, title = {{{OMG: A scalable and flexible simulation and testing environment toolbox for intelligent microgrid control}}}, volume = {{5}}, year = {{2020}}, } @article{35585, author = {{Lu, Jingyi and Leong, Alex S. and Quevedo, Daniel E.}}, issn = {{1049-8923}}, journal = {{International Journal of Robust and Nonlinear Control}}, keywords = {{Electrical and Electronic Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering, Aerospace Engineering, Biomedical Engineering, General Chemical Engineering, Control and Systems Engineering}}, number = {{11}}, pages = {{4205--4224}}, publisher = {{Wiley}}, title = {{{Optimal event‐triggered transmission scheduling for privacy‐preserving wireless state estimation}}}, doi = {{10.1002/rnc.4910}}, volume = {{30}}, year = {{2020}}, } @inproceedings{42073, author = {{Noroozi, Navid and Jackson, Roxanne and Quevedo, Daniel E. and Wirth, Fabian R. and Findeisen, Rolf}}, booktitle = {{2019 IEEE 58th Conference on Decision and Control (CDC)}}, publisher = {{IEEE}}, title = {{{On noise-to-state stability of stochastic discrete-time systems via finite-step Lyapunov functions}}}, doi = {{10.1109/cdc40024.2019.9030178}}, year = {{2020}}, } @article{42074, author = {{Ding, K. and Ren, X. and Quevedo, D. E. and Dey, S. and Shi, L.}}, journal = {{Automatica}}, title = {{{Defensive deception against reactive jamming attacks in remote state estimation}}}, volume = {{113}}, year = {{2020}}, } @article{15741, abstract = {{ In many cyber–physical systems, we encounter the problem of remote state estimation of geo- graphically distributed and remote physical processes. This paper studies the scheduling of sensor transmissions to estimate the states of multiple remote, dynamic processes. Information from the different sensors has to be transmitted to a central gateway over a wireless network for monitoring purposes, where typically fewer wireless channels are available than there are processes to be monitored. For effective estimation at the gateway, the sensors need to be scheduled appropriately, i.e., at each time instant one needs to decide which sensors have network access and which ones do not. To address this scheduling problem, we formulate an associated Markov decision process (MDP). This MDP is then solved using a Deep Q-Network, a recent deep reinforcement learning algorithm that is at once scalable and model-free. We compare our scheduling algorithm to popular scheduling algorithms such as round-robin and reduced-waiting-time, among others. Our algorithm is shown to significantly outperform these algorithms for many example scenario}}, author = {{Leong, Alex S. and Ramaswamy, Arunselvan and Quevedo, Daniel E. and Karl, Holger and Shi, Ling}}, issn = {{0005-1098}}, journal = {{Automatica}}, title = {{{Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems}}}, doi = {{10.1016/j.automatica.2019.108759}}, year = {{2019}}, } @article{35583, author = {{Leong, Alex S. and Ramaswamy, Arunselvan and Quevedo, Daniel E. and Karl, Holger and Shi, Ling}}, issn = {{0005-1098}}, journal = {{Automatica}}, keywords = {{Electrical and Electronic Engineering, Control and Systems Engineering}}, publisher = {{Elsevier BV}}, title = {{{Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems}}}, doi = {{10.1016/j.automatica.2019.108759}}, volume = {{113}}, year = {{2019}}, } @article{35584, author = {{Ding, Kemi and Ren, Xiaoqiang and Quevedo, Daniel E. and Dey, Subhrakanti and Shi, Ling}}, issn = {{0005-1098}}, journal = {{Automatica}}, keywords = {{Electrical and Electronic Engineering, Control and Systems Engineering}}, publisher = {{Elsevier BV}}, title = {{{Defensive deception against reactive jamming attacks in remote state estimation}}}, doi = {{10.1016/j.automatica.2019.108680}}, volume = {{113}}, year = {{2019}}, } @article{42220, author = {{Guo, Z. and Shi, D. and Quevedo, D. E. and Shi, L.}}, journal = {{Trans. Signal Processing}}, number = {{1}}, pages = {{194–207}}, title = {{{Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach}}}, volume = {{67}}, year = {{2019}}, }