Learning in Control
Algorithms of artificial intelligence and machine learning are of increasing importance for control applications. Our research and expertise in this domain ranges from the modeling of unknown or uncertain dynamics over iterative and reinforcement learning to Bayesian optimization.
One research focus at the Chair is on learning in model design and identification. Hybrid and data-driven models are attractive if physical modeling is either poor or requires high effort. Practical applications often require an online adaptation of these models in order to reflect effects of aging or wear or to increase the model accuracy in different operation regimes. Information about the reliability and trustworthiness of a learned model can directly be used within the control design. For instance, the prediction of the uncertainty allows to satisfy constraints with a given probability. A challenge with learning-based methods is to ensure real-time feasibility with possibly weak hardware resources in order to bring these advanced learning in control methods into practice.
Another field of research and expertise is learning in optimization and control, for instance, reinforcement learning and Bayesian optimization. Reinforcement learning aims at obtaining an optimal control strategy from repeated interactions with the system. Formulating this task as an optimization problem shows the conceptual similarity to model predictive control, with the difference that reinforcement learning does not require model knowledge of the system. In a similar spirit, Bayesian optimization allows to solve complex optimization problem, in particular if the cost function or constraints are not analytically known or can only be evaluated by costly numerical simulations. Many technical tasks such as the optimization of production processes, an optimal product design or the search of optimal controller setpoints can be formulated as (partially) unknown optimization problem, illustrating the generality and importance of Bayesian optimization.
Related projects since 2021
Related publications
- Kißkalt J, Michalka A, Strohmeyer C, Horn M, Graichen K (2025). Model-based fault simulation and detection for gauge-sensorized strain wave gears. In 11th Vienna International Conference on Mathematical Modelling (MATHMOD 25) (pp. 271 – 276). [DOI].
- Landgraf D, Wietzke T, Graichen K (2025). Stochastic model predictive control with switched latent force models. In European Control Conference.
- Wietzke T, Graichen K (2025). Physics-informed sparse Gaussian processes for model predictive control in building energy systems. In 11th Vienna International Conference on Mathematical Modelling (MATHMOD 25).
- Wietzke T, Landgraf D, Graichen K (2025). Application of stochastic model predictive control for building energy systems using latent force models. At-Automatisierungstechnik, (accepted).
- Conrad P, Graichen K (2024). A sensitivity-based approach to self-triggered nonlinear model predictive control. IEEE Access, 12, 153243-153252. [DOI].
- Goller T, Brohm D, Völz A, Graichen K (2024). DMP-based path planning for model predictive interaction control. In European Control Conference (pp. 128-133).
- Goller T, Völz A, Graichen K (2024). A Programming by Demonstration Approach for Robotic Manipulation with Model Predictive Interaction Control. In 2024 IEEE Conference on Control Technology and Applications (CCTA) (pp. 799-804).
- Kißkalt J, Michalka A, Strohmeyer C, Horn M, Graichen K (2024). Fault detection in gauge-sensorized strain wave gears. In European Control Conference (pp. 26-33). [DOI].
- Rabenstein G, Ullrich L, Graichen K (2024). Sampling for model predictive trajectory planning in autonomous driving using normalizing flows. In 5th IEEE Intelligent Vehicles Symposium (IEEE IV 2024) (pp. 2091-2096).
- Schumann M, Graichen K (2024). PINN-based dynamical modeling and state estimation in power inverters. In 2024 IEEE Conference on Control Technology and Applications (CCTA).
- Snobar F, Michalka A, Horn M, Strohmeyer C, Graichen K (2024). Sensitivity-based moving horizon estimation of road friction. In European Control Conference (pp. 718-724).
- Ullrich L, Buchholz M, Dietmayer K, Graichen K (2024). Expanding the Classical V-Model for the Development of Complex Systems Incorporating AI. IEEE Transactions on Intelligent Vehicles. [DOI].
- Ullrich L, Buchholz M, Dietmayer K, Graichen K (2024). AI safety assurance for automated vehicles: A survey on research, standardization, regulation. IEEE Transactions on Intelligent Vehicles. [DOI].
- Ullrich L, McMaster A, Graichen K (2024). Transfer learning study of motion transformer based trajectory predictions. In 5th IEEE Intelligent Vehicles Symposium (IEEE IV 2024) (pp. 110-117).
- Wietzke T, Gall J, Graichen K (2024). Occupancy Prediction for Building Energy Systems with Latent Force Models. Energy and Buildings, pp. 113968. [DOI].
- Dio M, Demir O, Trachte A, Graichen K (2023). Safe active learning and probabilistic design of experiment for autonomous hydraulic excavators. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 9685-9690).
- Hoffmann M, Braun S, Sura O, Stelzig M, Schüßler C, Graichen K, Vossiek M (2023). Concept for an Automatic Annotation of Automotive Radar Data Using AI-segmented Aerial Camera Images. In 2023 IEEE International Radar Conference, RADAR 2023. [DOI].
- Kißkalt J, Michalka A, Strohmeyer C, Horn M, Graichen K (2023). Simulation chain for sensorized strain wave gears. In 27th International Conference on System Theory, Control and Computing (ICSTCC) (pp. 467 – 473). [DOI].
- Landgraf D, Völz A, Berkel F, Schmidt K, Specker T, Graichen K (2023). Probabilistic prediction methods for nonlinear systems with application to stochastic model predictive control. Annual Reviews in Control, 56, 100905. [DOI].
- Schumann M, Ebersberger S, Graichen K (2023). Online learning and adaptation of nonlinear thermal networks for power inverters. In 49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023).
- Schumann M, Ebersberger S, Graichen K (2023). Improved nonlinear estimation in thermal networks using machine learning. In IEEE International Conference on Mechatronics (ICM 2023). [DOI].
- Snobar F, Michalka A, Horn M, Strohmeyer C, Graichen K (2023). Rack force estimation from standstill to high speeds by hybrid model design and blending. In IEEE International Conference on Mechatronics (ICM 2023). [DOI].
- Spenger P, Graichen K (2023). Performance prediction of NMPC algorithms with incomplete optimization. In 22nd IFAC World Congress (pp. 7456-7461).
- Ullrich L, Völz A, Graichen K (2023). Robust meta-learning of vehicle yaw rate dynamics via conditional neural processes. In 62nd IEEE Conference on Decision and Control (CDC).