Mechatronics & Automotive
Mechatronic systems consist of both mechanical and electrical components. Typical challenges are the high integration of these systems along with the limited computational resources of embedded hardware such as electronic control units (ECUs) in automotive applications. We have a successful history of bridging the gap between theory and practice in close cooperation with industrial partners from the mechatronics and automotive domain.
Contact
Prof. Dr.-Ing. Knut Graichen
Tel.: +49 9131 85-27127
E-Mail | Homepage
Dr.-Ing. Andreas Michalka
Tel.: +49 9131 85-28592
E-Mail | Homepage
Highly integrated mechatronic systems can, for instance, be found in electric drives, power trains, and sensors. Future mechatronic applications will not only involve classical control loops, but will also be equipped with further intelligent and autonomous functionalities such as automatic calibration, fault-tolerant operation and predictive maintenance.
Automotive-related applications such as passenger cars, trucks or agricultural machines involve a multitude of control systems. This ranges from active suspension, traction control, assisted driving up to fully autonomous driving. On the lower level, efficient embedded control implementations are predominant, whereas higher level control loops involve complex decision making in combination with environmental perception.
State-of-the-art modeling approaches of mechatronic systems combine physics-based and data-driven design methods to account for the uncertainties introduced, for example, by wear, aging and serial production spread. A particular expertise of the Chair is to leverage these hybrid models for sophisticated control schemes and the development of tailored algorithms for real-time capable implementations.
Videos
Truck with trailer
Related projects since 2021
AUTOtech.agil: Robust Planning and Control using Probabilistic Methods
Innovative Regelungs- und Steuerungsstrategien für Druckerhöhungsanlagen - TP Erlangen
Robust Reinforcement Learning for Thermal Management Control
ORACLE: Optimized Reinforcement Architecture for Complex Energy Management
AIM4F: AI-Supported modeling for friction estimation
Related publications
Since 2022
- Harder, K., Niemeyer, J., Remele, J., & Graichen, K. (2023). Hierarchical model predictive control for an off-highway Diesel engine with SCR catalyst. International Journal of Engine Research. https://doi.org/10.1177/14680874221143600
- Bergmann, D., Harder, K., Niemeyer, J., & Graichen, K. (2022). Nonlinear MPC of a Heavy-Duty Diesel Engine With Learning Gaussian Process Regression. IEEE Transactions on Control Systems Technology, 30(1), 113-129. https://doi.org/10.1109/TCST.2021.3054650
- Rabenstein, G., Demir, O., Trachte, A., & Graichen, K. (2022). Data-driven feed-forward control of hydraulic cylinders using Gaussian process regression for excavator assistance functions. In Proceedings of the 6th IEEE Conference on Control Technology and Applications (CCTA) (pp. 962-969). Trieste (Italy).
- Dio, M., Demir, O., Trachte, A., & Graichen, K. (2023). Safe active learning and probabilistic design of experiment for autonomous hydraulic excavators. In Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 9685-9690). Detroit, US.
- Schumann, M., Ebersberger, S., & Graichen, K. (2023). Online learning and adaptation of nonlinear thermal networks for power inverters. In Proceedings of the 49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023). Marina Bay Sands (Singapore).
- Harder, K., Niemeyer, J., Remele, J., & Graichen, K. (2023). Hierarchical model predictive control for an off-highway Diesel engine with SCR catalyst. International Journal of Engine Research. https://doi.org/10.1177/14680874221143600
- Bergmann, D., Harder, K., Niemeyer, J., & Graichen, K. (2022). Nonlinear MPC of a Heavy-Duty Diesel Engine With Learning Gaussian Process Regression. IEEE Transactions on Control Systems Technology, 30(1), 113-129. https://doi.org/10.1109/TCST.2021.3054650
- Rabenstein, G., Demir, O., Trachte, A., & Graichen, K. (2022). Data-driven feed-forward control of hydraulic cylinders using Gaussian process regression for excavator assistance functions. In Proceedings of the 6th IEEE Conference on Control Technology and Applications (CCTA) (pp. 962-969). Trieste (Italy).
- Dio, M., Demir, O., Trachte, A., & Graichen, K. (2023). Safe active learning and probabilistic design of experiment for autonomous hydraulic excavators. In Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 9685-9690). Detroit, US.
- Schumann, M., Ebersberger, S., & Graichen, K. (2023). Online learning and adaptation of nonlinear thermal networks for power inverters. In Proceedings of the 49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023). Marina Bay Sands (Singapore).
2021
2020
- Bergmann, D., & Graichen, K. (2020). Safe Bayesian Optimization under Unknown Constraints. In 59th IEEE Conference on Decision and Control (CDC 2020) (pp. 3592-3597). Institute of Electrical and Electronics Engineers Inc..
- Geiselhart, R., Bergmann, D., Niemeyer, J., Remele, J., & Graichen, K. (2020). Hierarchical Predictive Control of a Combined Engine/Selective Catalytic Reduction System with Limited Model Knowledge. SAE International Journal of Engines, 13(2). https://doi.org/10.4271/03-13-02-0015
- Geiselhart, R., Bergmann, D., Niemeyer, J., Remele, J., & Graichen, K. (2020). Hierarchical predictive control of a combined engine/SCR system with limited model knowledge. SAE International Journal of Engines, 13(2). https://doi.org/10.4271/03-13-02-0015
- Mesmer, F., Szabo, T., & Graichen, K. (2020). Learning feedforward control of a hydraulic clutch actuation path based on policy gradients. In 59th IEEE Conference on Decision and Control (CDC 2020).
- Englert, T., & Graichen, K. (2020). Nonlinear model predictive torque control and setpoint computation of induction machines for high performance applications. Control Engineering Practice, 99. https://doi.org/10.1016/j.conengprac.2020.104415
- Bergmann, D., & Graichen, K. (2020). Safe Bayesian Optimization under Unknown Constraints. In 59th IEEE Conference on Decision and Control (CDC 2020) (pp. 3592-3597). Institute of Electrical and Electronics Engineers Inc..
- Geiselhart, R., Bergmann, D., Niemeyer, J., Remele, J., & Graichen, K. (2020). Hierarchical Predictive Control of a Combined Engine/Selective Catalytic Reduction System with Limited Model Knowledge. SAE International Journal of Engines, 13(2). https://doi.org/10.4271/03-13-02-0015
- Geiselhart, R., Bergmann, D., Niemeyer, J., Remele, J., & Graichen, K. (2020). Hierarchical predictive control of a combined engine/SCR system with limited model knowledge. SAE International Journal of Engines, 13(2). https://doi.org/10.4271/03-13-02-0015
- Mesmer, F., Szabo, T., & Graichen, K. (2020). Learning feedforward control of a hydraulic clutch actuation path based on policy gradients. In 59th IEEE Conference on Decision and Control (CDC 2020).
- Englert, T., & Graichen, K. (2020). Nonlinear model predictive torque control and setpoint computation of induction machines for high performance applications. Control Engineering Practice, 99. https://doi.org/10.1016/j.conengprac.2020.104415
2019
- Bergmann, D., & Graichen, K. (2019). Gaußprozessregression zur Modellierung zeitvarianter Systeme. At-Automatisierungstechnik, 67(8), 637-647. https://doi.org/10.1515/auto-2019-0015
- Mesmer, F., Szabo, T., & Graichen, K. (2019). Learning methods for the feedforward control of a hydraulic clutch actuation path. In Proc. IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2019) (pp. 733-738). Hong Kong (China).
- Bergmann, D., Geiselhart, R., & Graichen, K. (2019). Modelling and control of a heavy-duty Diesel engine gas path with Gaussian process regression. In Proc. European Control Conference (ECC 2019) (pp. 1207-1213). Naples (Italy).
- Mesmer, F., Szabo, T., & Graichen, K. (2019). Feedforward control of a hydraulic clutch actuation path. In Proc. European Control Conference (ECC 2019) (pp. 620-626). Naples (Italy).
- Bergmann, D., & Graichen, K. (2019). Gaußprozessregression zur Modellierung zeitvarianter Systeme. At-Automatisierungstechnik, 67(8), 637-647. https://doi.org/10.1515/auto-2019-0015
- Mesmer, F., Szabo, T., & Graichen, K. (2019). Learning methods for the feedforward control of a hydraulic clutch actuation path. In Proc. IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2019) (pp. 733-738). Hong Kong (China).
- Bergmann, D., Geiselhart, R., & Graichen, K. (2019). Modelling and control of a heavy-duty Diesel engine gas path with Gaussian process regression. In Proc. European Control Conference (ECC 2019) (pp. 1207-1213). Naples (Italy).
- Mesmer, F., Szabo, T., & Graichen, K. (2019). Feedforward control of a hydraulic clutch actuation path. In Proc. European Control Conference (ECC 2019) (pp. 620-626). Naples (Italy).
2018 and earlier
- Käpernick, B., & Graichen, K. (2013). Model predictive control of an overhead crane using constraint substitution. In Proceedings 2013 American Control Conference (ACC) (pp. 3979-3984). Washington, DC (USA).
- Harder, K., Buchholz, M., Späder, T., & Graichen, K. (2018). A real-time nonlinear air path observer for off-highway diesel engines. In Proceedings 2018 European Control Conference (ECC) (pp. 237-242). Limassol (Cyprus).
- Englert, T., & Graichen, K. (2018). A fixed-point iteration scheme for model predictive torque control of PMSMs. In Proceedings 6th IFAC Conference on Nonlinear Model Predictive Control (NMPC) (pp. 668-673). Madison, WI (USA).
- Käpernick, B., & Graichen, K. (2013). Transformation of output constraints in optimal control applied to a double pendulum on a cart. In Proceedings 9th IFAC Symposium ``Nonlinear Control Systems'' (NOLCOS) (pp. 193-198). Toulouse (Italy).
- Käpernick, B., & Graichen, K. (2013). Model predictive control of an overhead crane using constraint substitution. In Proceedings 2013 American Control Conference (ACC) (pp. 3979-3984). Washington, DC (USA).
- Harder, K., Buchholz, M., Späder, T., & Graichen, K. (2018). A real-time nonlinear air path observer for off-highway diesel engines. In Proceedings 2018 European Control Conference (ECC) (pp. 237-242). Limassol (Cyprus).
- Englert, T., & Graichen, K. (2018). A fixed-point iteration scheme for model predictive torque control of PMSMs. In Proceedings 6th IFAC Conference on Nonlinear Model Predictive Control (NMPC) (pp. 668-673). Madison, WI (USA).
- Käpernick, B., & Graichen, K. (2013). Transformation of output constraints in optimal control applied to a double pendulum on a cart. In Proceedings 9th IFAC Symposium ``Nonlinear Control Systems'' (NOLCOS) (pp. 193-198). Toulouse (Italy).