Prof. Dr. Knut Graichen
Chair of Automatic Control
Our research focuses on the model & control design, analysis, and optimization of dynamical systems from different domains including robotics and human-machine interaction. It is also important for us to bring control and AI related research into practice by closely cooperating with industry, for instance from the automotive domain, robotics and process automation.
Research projects
- Cooperative manipulation with dual-arm robots at the payload limit (headed bei Dr. Andreas Völz)
- Kinesthetic teaching and predictive control of interaction tasks in robotics
- Distributed model predictive control of nonlinear systems with asynchronous communication
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Improving the motion control performance of a testing system
(Third Party Funds Single)
Project leader:
Term: 15. June 2026 - 14. December 2026
Funding source: Industrie -
Optimal robot motion planning
(Third Party Funds Single)
Project leader:
Term: 1. June 2026 - 31. May 2029
Funding source: Industrie -
Synergy-based model predictive interaction control for robotic hand-arm systems
(Third Party Funds Single)
Project leader: ,
Term: 1. January 2026 - 31. December 2028
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)Robotic hands are often motivated by the fact that they can be used to perform a wide range of manipulation tasks. The extension to a robotic hand-arm system (e.g., as part of a humanoid robot) makes it possible to significantly reduce the workload of people in shared workspaces. Clear examples of this can be found both in industrial manufacturing processes (pick and place, assembly, machining, etc.) and in everyday human life (opening doors, cutting food, handling objects, etc.). In addition to complicated movements, such tasks require the precise adjustment of forces. Nevertheless, they are usually performed by humans or various highly specialized machines. Alternatively, the environment could be adapted to the technology, but this is hardly practicable. In addition to safety, a particular challenge in shared workspaces is the acceptance of the robot system by the people present. An important aspect of this is that people must be able to recognize and anticipate the robot's movements intuitively. The research project aims to develop a synergy-based model predictive control (MPC) for robotic hand-arm systems that is integrated into a modular control architecture. The challenges of numerous actuated degrees of freedom are considered and the methods for null-space control are co-developed. With the help of a model-predictive approach, the aim is to achieve a modular architecture that also integrates task planning. Various approaches are being developed to efficiently integrate linear and non-linear kinematic synergies into a modular MPC formulation. Therein, the synergies are intended to reduce the complexity and thus the computation effort on the one hand and to realize human-like motions on the other. This approach also aims to increase the human acceptance of such systems.
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Distributed model predictive control using sensitivity based primal decomposition
(Third Party Funds Single)
Project leader:
Term: 1. January 2026 - 31. December 2028
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)Model Predictive Control (MPC) is a widely used strategy for controlling linear and nonlinear systems. It is based on the iterative solution of a dynamic optimization problem over a receding horizon. For networked and coupled systems, distributed MPC (DMPC) is an attractive extension of MPC, where the central MPC controller is replaced by local MPC agents for the individual subsystems of the global system. The probably most popular DMPC method is ADMM (Alternating Direction Method of Multipliers) that is based on the dual decomposition of the distributed problem. A promising alternative approach, which has not yet been fully explored in the literature, is sensitivity-based primal decomposition. In this approach, the individual agents explicitly consider the costs of their actions on the neighbors’ performance. These sensitivities can be locally computed in an efficient mannery. Compared to ADMM, sensitivity-based DMPC shows an improved convergence behavior, reduced communication overhead, and lower algorithmic complexity. The efficient local computation of sensitivities and a simpler convergence analysis are further advantages of this method. Despite these advantages, the sensitivity-based approach currently has several shortcomings compared to ADMM. In particular, convergence and stability can only be guaranteed for a maximum prediction horizon and general state couplings are more difficult to consider with primal decomposition. Therefore, this project aims to conduct an in-depth investigation of the sensitivity-based approach for distributed model predictive control. In particular, the aforementioned shortcomings compared to ADMM shall be addressed, and the overarching topic of sensitivities can be used to increase efficiency and flexibility in numerical solutions, to simplify the methodological analysis, and to enable practical implementation. The findings will be published in the DMPC toolbox GRAMPC-D.
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Control of mobile robot swarms
(Third Party Funds Single)
Project leader:
Term: 1. January 2026 - 31. December 2028
Funding source: Industrie -
EPIC4HPC: Energy-Efficient Electro-Photonic Integrated Circuits for High-Performance Computing
(Third Party Funds Group – Overall project)
Project leader:
Term: 1. April 2025 - 31. March 2028
Acronym: EPIC4HPC
Funding source: Bayerische Forschungsstiftung -
H2Season: Prototypical development of an Aceton / Isopropanol hydrogen storage system for stationary seasonal energy storage
(Third Party Funds Single)
Project leader: , ,
Term: 1. February 2025 - 31. March 2028
Acronym: H2Season
Funding source: Helmholtz-Gemeinschaft -
ORACLE: Optimized Reinforcement Architecture for Complex Energy Management
(Third Party Funds Single)
Project leader:
Term: 1. July 2024 - 30. June 2027
Acronym: ORACLE
Funding source: Industrie -
OXO-Projekt: OXO-LOHC: Autotherme und ultratiefe Wasserstoff-Freisetzung aus LOHC
(Third Party Funds Single)
Project leader: , , ,
Term: 1. November 2023 - 31. October 2028
Acronym: OXO-Projekt
Funding source: Helmholtz-Gemeinschaft -
Robust energy-based control of MMC/HVDC systems
(Third Party Funds Single)
Project leader:
Term: 15. June 2023 - 31. December 2026
Funding source: Industrie -
SPP 2364: Formulation of dispersed systems via (melt) emulsification: Process design, in situ diagnostics and regulation
(Third Party Funds Group – Sub project)
Overall project: Autonome Prozesse in der Partikeltechnik - Erforschung und Erprobung von Konzepten zur modellbasierten Führung partikeltechnischer Prozesse
Project leader: , ,
Term: 1. January 2023 - 31. December 2028
Acronym: SPP 2364
Funding source: DFG / Schwerpunktprogramm (SPP)The aim of this project is the automated production of liquid-liquid disperse systems via melt emulsification, whereby in this process emulsification takes place at elevated temperature. The products obtained after cooling are dispersions of spherical nanoparticles or microparticles. Within the scope of this project, a melt emulsification device for the automated production of product particles with a well-defined particle size distribution (PSD) will be further developed. The PSD has a significant influence on the subsequent product properties, such as flow behavior or drug release kinetics. The PSD of the products is determined by the complex interaction of competing mechanisms. These are, in particular, droplet breakup in a rotor-stator device as a result of shear and elongation stress, as well as coalescence and further ripening, which in turn depend on the system composition, i.e. the emulsifier used (type, concentration) and the dispersion phase (viscosity, volume fraction).
Therefore, for a better process understanding and an active process control, possibilities for in situ determination of the PSD are urgently required. In this project, a novel fiber-coupled measurement system based on broadband elastic light scattering is developed for in situ measurement of the PSD. The system will be validated on reference particle systems and applied to the emulsification process. Furthermore, a hybrid process model is developed, which is the basis for the design of a model predictive control of the process. The model predictive control in combination with the in situ measurement will provide the possibility for an active process control and the production of emulsions with predefined properties and a simultaneous optimization of the process time.
2026
- Kowalewski, J., Lorenz, A., & Graichen, K. (2026). Slow Converter-Driven Stability Analysis via Directional and Relative Passivity Indices. In Proceedings of the 23rd IFAC World Congress. Busan (Korea).
- Landgraf, D., Völz, A., & Graichen, K. (2026). Recursive feasibility of nonlinear stochastic model predictive control with Gaussian process dynamics. International Journal of Robust and Nonlinear Control, 36, 4957-4970. https://doi.org/10.1002/rnc.70470
- Pierer von Esch, M., Völz, A., & Graichen, K. (2026). An overview of sensitivity-based distributed optimization and model predictive control. At-Automatisierungstechnik, 74(3), 182-196. https://doi.org/10.1515/auto-2025-0105
- Pierer von Esch, M., Völz, A., & Graichen, K. (2026). Sensitivity-Based Distributed Programming for Non-Convex Optimization. IEEE Transactions on Control of Network Systems, 13(2), 1205-1217. https://doi.org/10.1109/TCNS.2026.3690575
- Snobar, F.S.N., Michalka, A., Horn, M., Schulz, T., & Graichen, K. (2026). Model-based road friction estimation from steering torque at standstill and low-speed rolling. Vehicle System Dynamics. https://doi.org/10.1080/00423114.2026.2679166
- Ullrich, L., Buchholz, M., Dietmayer, K., & Graichen, K. (2026). Toward Fully Autonomous Driving: AI, Challenges, Opportunities, and Needs. IEEE Access, 14, 17971-17997. https://doi.org/10.1109/ACCESS.2026.3659192
- Völz, A., & Graichen, K. (2026). Towards Simulation-Based Motion Planning for Deformable Linear Objects. In Proceedings of the 23rd IFAC World Congress. Busan (Korea).
2025
- Conrad, P., Michalka, A., Beck, J., & Graichen, K. (2025). Nonlinear MPC for Stabilizing the Longitudinal Dynamics of a Highly Maneuverable Aircraft. In Proc. 2025 IEEE Aerospace Conference. Big Sky, Montana (USA).
- Conrad, P., Steuter, L., Pierer von Esch, M., Beck, J., & Graichen, K. (2025). Aerodynamic neural network modeling for gradient-based model predictive flight control. In Proc. 33rd Mediterranean Conference on Control and Automation (MED 2025).
- Dahlmann, J., Völz, A., & Graichen, K. (2025). Global motion planning for multi-trailer vehicles in partially structured environments. Journal of Intelligent & Robotic Systems, 111(4). https://doi.org/10.1007/s10846-025-02330-4
- Dio, M., Wahrburg, A., Enayati, N., Graichen, K., & Völz, A. (2025). Time-Optimal Path Parameterization with Viscous Friction and Jerk Constraints based on Reachability Analysis. In Proceedings 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Hangzhou, CN.
- Findeisen, R., Rose, A., Graichen, K., & Mönnigmann, M. (2025). Embedded Optimization in Control: An Introduction, Opportunities, and Challenges. In Zhengtao Ding (ed.) (Eds.), Encyclopedia of Systems and Control Engineering. (pp. 223–244). Elsevier.
- Goller, T., Hopf, V., Völz, A., & Graichen, K. (2025). Fault Handling in Robotic Manipulation Tasks for Model Predictive Interaction Control. IEEE Robotics and Automation Letters, 10(9), 9002-9009. https://doi.org/10.1109/LRA.2025.3592069
- Hartmann, P., & Graichen, K. (2025). Learning-based uncertainty-aware predictive control of truck-trailer systems in rough terrain. In Institute of Electrical and Electronics Engineers (IEEE) (Eds.), Proceedings of the 19th IEEE International Conference on Control & Automation (ICCA) (pp. 162-167). Tallinn (Estonia), EE.
- Herrmann-Wicklmayr, M., Wietzke, T., Graichen, K., & Flaßkamp, K. (2025). Considering multiple objectives in model predictive control for building energy systems. In Proceedings 33rd Mediterranean Conference on Control and Automation (MED 2025) (pp. 502-507). Tangier, Morocco, MA.
- Kißkalt, J., Michalka, A., Strohmeyer, C., Horn, M., & Graichen, K. (2025). Estimation of input rotation speed in gauge-sensorized strain wave gears. In Proceedings of the 2025 IEEE Conference on Control Technology and Applications (CCTA) (pp. 457-463). San Diego (USA).
- Kißkalt, J., Michalka, A., Strohmeyer, C., Horn, M., & Graichen, K. (2025). Model-based fault simulation and detection for gauge-sensorized strain wave gears. In IFAC PapersOnline (pp. 271 - 276). Vienna (Austria).
- Kowalewski, J., Lorenz, A., Thomas, F., Valenzuela, R.A.A., & Graichen, K. (2025). Passivity-Based Robust Stability Analysis of the Converter–Grid Interaction and Passivity-Shaping Controller Design. IET Generation Transmission & Distribution, 19(1). https://doi.org/10.1049/gtd2.70090
- Kruse, T., Griebel, T., & Graichen, K. (2025). Adaptive Kalman filtering: Measurement and process noise covariance estimation using Kalman smoothing. IEEE Access, 13, 11863-11875. https://doi.org/10.1109/ACCESS.2025.3528348
- Landgraf, D., Völz, A., & Graichen, K. (2025). A software framework for stochastic model predictive control of nonlinear continuous-time systems (GRAMPC-S). Optimization and Engineering. https://doi.org/10.1007/s11081-025-10006-z
- Landgraf, D., Wietzke, T., & Graichen, K. (2025). Stochastic model predictive control with switched latent force models. European Journal of Control, 85, 101311. https://doi.org/10.1016/j.ejcon.2025.101366
- Pierer von Esch, M., Nistler, E., Völz, A., & Graichen, K. (2025). Sensitivity-based distributed NMPC: Experimental results for a levitating planar motion system. IEEE Transactions on Control Systems Technology, 33(3), 1110-1118. https://doi.org/10.1109/TCST.2025.3530165
- Pierer von Esch, M., Völz, A., & Graichen, K. (2025). A fixed-point iteration scheme for sensitivity-based distributed optimal control. IEEE Transactions on Automatic Control, 70(4), 2778-2785. https://doi.org/10.1109/TAC.2024.3505753
- Pierer von Esch, M., Völz, A., & Graichen, K. (2025). Asynchronous sensitivity-based distributed optimal control for nonlinear systems. In Proceedings 2025 American Control Conference (ACC) (pp. 835-842). Denver, CO (USA).
- Pierer von Esch, M., Völz, A., & Graichen, K. (2025). Sensitivity-Based Distributed Model Predictive Control for Nonlinear Systems under Inexact Optimization. Optimal Control Applications & Methods, 46, 1538-1558. https://doi.org/10.1002/oca.3277
- Rabenstein, G., Völz, A., & Graichen, K. (2025). Cable Manipulation for Contact Shaping Tasks using Elastic Rods. In Proceedings of the IEEE International Conference on Advanced Robotics and Mechatronics (ICARM). Portsmouth.
- Reinhard, J., Löhe, K., Kallabis, S., & Graichen, K. (2025). Dynamic Compensation of the Threading Speed Drop in Rolling Processes: Bayesian Optimization of the Roughing and Finishing Mill. Journal of Process Control, 156, 103579. https://doi.org/10.1016/j.jprocont.2025.103579
- Santer, P., Reinhard, J., Schindler, A., & Graichen, K. (2025). Detection of localized bearing faults in PMSMs by means of envelope analysis and wavelet packet transform using motor speed and current signals. Mechatronics, 106, 103294. https://doi.org/10.1016/j.mechatronics.2025.103294
- Santer, P., Völz, A., & Graichen, K. (2025). A Model Predictive Control Approach to Trajectory Tracking with Human-Robot Collision Avoidance. In Proceedings 9th IEEE Conference on Control Technology and Applications (CCTA). San Diego, US.
- Stecher, J., Kiltz, L., & Graichen, K. (2025). Generalized tolerance optimization for robust system design by adaptive learning of Gaussian processes. IEEE Access, 13, 68959-68983.
- Stecher, J., Kiltz, L., Ruess, N., & Graichen, K. (2025). Why engineers should care about semi-infinite programming: Nominal versus tolerance-aware geometry optimization of a proportional electromagnetic actuator. In Proceedings 10th IFAC Symposium on Mechatronic Systems & 14th Symposium on Robotics (MSROB 2025). Paris (France).
- Südhoff, T., Hsuan-Yang, S., Villwock, J., Bliatsiou, C., Topalovic, D., Graichen, K.,... Knorn, S. (2025). Closed-loop control of a liquid-liquid mixer using MPC and GPR-models. In Proceedings 33rd Mediterranean Conference on Control and Automation (MED 2025). Tangier, Morocco, MA.
- Ullrich, L., Buchholz, M., Dietmayer, K., & Graichen, K. (2025). AI safety assurance for automated vehicles: A survey on research, standardization, regulation. IEEE Transactions on Intelligent Vehicles, 10(10), 4784-4803. https://doi.org/10.1109/TIV.2024.3496797
- Ullrich, L., Buchholz, M., Dietmayer, K., & Graichen, K. (2025). Expanding the Classical V-Model for the Development of Complex Systems Incorporating AI. IEEE Transactions on Intelligent Vehicles, 10(3), 1790-1804. https://doi.org/10.1109/TIV.2024.3434515
- Ullrich, L., Buchholz, M., Petit, J., Dietmayer, K., & Graichen, K. (2025). A Concept for Efficient Scalability of Automated Driving Allowing for Technical, Legal, Cultural, and Ethical Differences. In Proc. 2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC) (pp. 2663-2670). Gold Coast (Australia).
- Ullrich, L., Mujirishvili, Z., & Graichen, K. (2025). Enhancing system self-awareness and trust of AI: A case study in trajectory prediction and planning. In Proceedings of the 36th IEEE Intelligent Vehicles Symposium (IEEE IV 2025). Cluj-Napoca (Romania).
- Ullrich, L., Zimmer, W., Greer, R., Graichen, K., Knoll, A.C., & Trivedi, M. (2025). A New Perspective On AI Safety Through Control Theory Methodologies. IEEE Open Journal of Intelligent Transportation Systems, 6, 938-966. https://doi.org/10.1109/OJITS.2025.3585274
- Wietzke, T., & Graichen, K. (2025). Physics-informed sparse Gaussian processes for model predictive control in building energy systems. In IFAC-PapersOnLine (pp. 43-48). Vienna (Austria).
- Wietzke, T., Landgraf, D., & Graichen, K. (2025). Application of stochastic model predictive control for building energy systems using latent force models. At-Automatisierungstechnik, 73(6), 441-450. https://doi.org/10.1515/auto-2024-0160
2024
- Cherian, A.J., Michalka, A., Murray, K., Roell, G., & Graichen, K. (2024). Control approaches for operating point stabilization of microring resonator modulators under fast perturbations. In Graham T. Reed, Andrew P. Knights (Eds.), Proceedings Silicon Photonics XIX (pp. 128910D). SPIE.
- Conrad, P., & Graichen, K. (2024). A sensitivity-based approach to self-triggered nonlinear model predictive control. IEEE Access, 12, 153243-153252. https://doi.org/10.1109/ACCESS.2024.3480522
- Dahlmann, J., Graichen, K., & Völz, A. (2024). Ein Konzept zum automatisierten Rangieren von Fahrzeugen mit Anhängern.
- Dahlmann, J., Völz, A., Lukassek, M., & Graichen, K. (2024). Local predictive optimization of globally planned motions for truck-trailer systems. IEEE Transactions on Control Systems Technology, 32(5), 1555-1568. https://doi.org/10.1109/TCST.2023.3345169
- Dio, M., Graichen, K., & Völz, A. (2024). Time-Optimal Path Parameterization for Cooperative Multi-Arm Robotic Systems with Third-Order Constraints. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3043-3048). Abu Dhabi, AE: Institute of Electrical and Electronics Engineers Inc..
- Goller, T., Brohm, D., Völz, A., & Graichen, K. (2024). DMP-based path planning for model predictive interaction control. In Proceedings of the European Control Conference (pp. 128-133). Stockholm (Sweden).
- Goller, T., Völz, A., & Graichen, K. (2024). A Programming by Demonstration Approach for Robotic Manipulation with Model Predictive Interaction Control. In Proceedings of the 2024 IEEE Conference on Control Technology and Applications (CCTA) (pp. 799-804). Newcastle upon Tyne, United Kingdom.
- Goppelt-Schneider, F., Schmidt-Vollus, R., & Graichen, K. (2024). Trajectory tracking control for multilevel pressure boosting systems. In Proceedings of the 28th International Conference on System Theory, Control and Computing (ICSTCC). Sinaia, Romania.
- Kißkalt, J., Michalka, A., Strohmeyer, C., Horn, M., & Graichen, K. (2024). Fault detection in gauge-sensorized strain wave gears. In Proceedings of the European Control Conference (pp. 26-33). Stockholm (Sweden).
- Kögler, P., Dahlmann, J., & Graichen, K. (2024). Side-Slip Compensation in Model Predictive Path Following Control for General-n-Trailer Systems. In Proceedings of the 28th International Conference on System Theory, Control and Computing (ICSTCC). Sinaia, Romania.
- Lukassek, M., Dahlmann, J., Völz, A., & Graichen, K. (2024). Model predictive path-following control for truck–trailer systems with specific guidance points - Design and experimental validation. Mechatronics, 100, 103190. https://doi.org/10.1016/j.mechatronics.2024.103190
- Löhe, K., Reinhard, J., Petrasch, N., Kallabis, S., Graichen, K., & Mucha, M. (2024). Work Roll Speed Drop Compensation for Hot Strip Mills Reduces Drivetrain Wear and Increases Strip Quality. In AISTech 2024 — Proceedings of the Iron & Steel Technology Conference (pp. 1212-1223). Columbus, OH, US: Warrendale, PA: Association for Iron and Steel Technology.
- Pierer von Esch, M., Landgraf, D., Steffel, M., Völz, A., & Graichen, K. (2024). Distributed Stochastic Optimal Control of Nonlinear Systems based on ADMM. IEEE Control Systems Letters, 8, 424-429. https://doi.org/10.1109/LCSYS.2024.3393411
- Pierer von Esch, M., Völz, A., & Graichen, K. (2024). Asynchronous ADMM for Nonlinear Continuous-Time Systems. Optimal Control Applications & Methods.
- Pierer von Esch, M., Völz, A., & Graichen, K. (2024). Sensitivity-Based Distributed Model Predictive Control: Synchronous and Asynchronous Execution Compared to ADMM. At-Automatisierungstechnik, 72(2), 91-106. https://doi.org/10.1515/auto-2023-0050
- Rabenstein, G., Ullrich, L., & Graichen, K. (2024). Sampling for model predictive trajectory planning in autonomous driving using normalizing flows. In Proc. 35th IEEE Intelligent Vehicles Symposium (IEEE IV 2024) (pp. 2091-2096). Jeju Island (Korea).
- Reinhard, J., Löhe, K., & Graichen, K. (2024). Optimal dynamic current control for externally excited synchronous machines. In Proceedings of the 2024 IEEE Conference on Control Technology and Applications (CCTA) (pp. 146-152). Newcastle upon Tyne, UK.
- Reinhard, J., Löhe, K., Petrasch, N., Kallabis, S., & Graichen, K. (2024). Dynamic compensation of the threading speed drop in rolling processes. Journal of Process Control, 137, 103197. https://doi.org/10.1016/j.jprocont.2024.103197
- Schumann, M., & Graichen, K. (2024). PINN-based dynamical modeling and state estimation in power inverters. In Proceedings of the 2024 IEEE Conference on Control Technology and Applications (CCTA). Newcastle upon Tyne, UK.
- Snobar, F.S.N., Michalka, A., Horn, M., Strohmeyer, C., & Graichen, K. (2024). Sensitivity-based moving horizon estimation of road friction. In Proceedings of the European Control Conference (pp. 718-724). Stockholm (Sweden).
- Südhoff, T., Ebner, L., Schmidt, J., & Graichen, K. (2024). GP-based modeling for PSD control of emulsification processes. In Proceedings of the 28th International Conference on System Theory, Control and Computing (ICSTCC). Sinaia, Romania.
- Ullrich, L., McMaster, A., & Graichen, K. (2024). Transfer learning study of motion transformer based trajectory predictions. In Proc. 35th IEEE Intelligent Vehicles Symposium (IEEE IV 2024) (pp. 110-117). Jeju Island (Korea).
- Verhoolen, A., Geißelbrecht, M., Kadar, J., Preuster, P., Wasserscheid, P., & Graichen, K. (2024). Bayesian optimization of operating points of a continuous perhydro-dibenzyltoluene dehydrogenation reactor. International Journal of Energy Research. https://doi.org/10.1155/2024/5627453
- Wietzke, T., Gall, J., & Graichen, K. (2024). Occupancy Prediction for Building Energy Systems with Latent Force Models. Energy and Buildings, 113968. https://doi.org/10.1016/j.enbuild.2024.113968
2023
- 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.
- Dio, M., Völz, A., & Graichen, K. (2023). Cooperative dual-arm control for heavy object manipulation based on hierarchical quadratic programming. In Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 643-648). Detroit, US.
- Frank, R., Wittmann, L.-M., Kleffel, T., Roth, B., Graichen, K., & Drummer, D. (2023). Investigating the Integration of Nonwoven Carbon Fibers for Mechanical Enhancement in Compression Molded Fiber-Reinforced Polymer Bipolar Plates. Polymers, 15(19). https://doi.org/10.3390/polym15193891
- Geiling, J., Wagner, L., Auer, F., Ortner, F., Nuß, A., Seyfried, R.,... Preuster, P. (2023). Operational experience with a liquid organic hydrogen carrier (LOHC) system for bidirectional storage of electrical energy over 725 h. Journal of Energy Storage, 72. https://doi.org/10.1016/j.est.2023.108478
- Gold, T., Völz, A., & Graichen, K. (2023). Model predictive interaction control for robotic manipulation tasks. IEEE Transactions on Robotics, 39(1), 76-89. https://doi.org/10.1109/TRO.2022.3196607
- 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
- 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 Proceedings of the IEEE Radar Conference. Sydney, NSW, AU: Institute of Electrical and Electronics Engineers Inc..
- Kißkalt, J., Michalka, A., Strohmeyer, C., Horn, M., & Graichen, K. (2023). Simulation chain for sensorized strain wave gears. In Proc. 27th International Conference on System Theory, Control and Computing (ICSTCC) (pp. 467 - 473). Timisoara (Romania).
- 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. https://doi.org/10.1016/j.arcontrol.2023.100905
- Rohrmüller, M., Beckerle, P., Graichen, K., Malvezzi, M., & Pozzi, M. (2023). In-Hand Manipulation with Synergistic Actuated Robotic Hands: An MPC-Based Approach. In 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids). Austin, TX, US: IEEE Computer Society.
- Schumann, M., Ebersberger, S., & Graichen, K. (2023). Improved nonlinear estimation in thermal networks using machine learning. In Proc. IEEE International Conference on Mechatronics (ICM 2023, accepted). Loughborough (UK).
- 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).
- Snobar, F.S.N., Michalka, A., Horn, M., Strohmeyer, C., & Graichen, K. (2023). Rack force estimation from standstill to high speeds by hybrid model design and blending. In Proceedings of the IEEE International Conference on Mechatronics (ICM 2023). Loughborough (UK).
- Spenger, P., & Graichen, K. (2023). Performance prediction of NMPC algorithms with incomplete optimization. In Proc. 22nd IFAC World Congress (accepted) (pp. 7456-7461). Yokohama, Japan.
- Ullrich, L., Völz, A., & Graichen, K. (2023). Robust meta-learning of vehicle yaw rate dynamics via conditional neural processes. In Proc. 62nd IEEE Conference on Decision and Control (CDC) (pp. 322-327). Marina Bay Sands (Singapore).
2022
- 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
- Burk, D., Völz, A., & Graichen, K. (2022). A modular framework for distributed model predictive control of nonlinear continuous-time systems (GRAMPC-D). Optimization and Engineering, 23, 771-795. https://doi.org/10.1007/s11081-021-09605-3
- Burk, D., Völz, A., & Graichen, K. (2022). Improving the performance of distributed model predictive control by applying graph partitioning methods. In Proceedings of the 26th International Conference on System Theory, Control and Computing (ICSTCC). Sinaia (Romania).
- Dahlmann, J., Völz, A., Szabo, T., & Graichen, K. (2022). A Numerical Approach for Solving the Inversion Problem for n-Trailer Systems. In 2022 American Control Conference (ACC) (pp. 2018-2024). Atlanta, GA, US: Institute of Electrical and Electronics Engineers Inc..
- Dahlmann, J., Völz, A., Szabo, T., & Graichen, K. (2022). Trajectory optimization for truck-trailer systems based on predictive path-following control. In Proceedings of the 6th IEEE Conference on Control Technology and Applications (CCTA). Trieste (Italy).
- Gold, T., Römer, R., Völz, A., & Graichen, K. (2022). Catching objects with a robot arm using model predictive control. In Proceedings 2022 American Control Conference (ACC) (pp. 1915-1920). Atlanta, GA (USA).
- Goller, T., Gold, T., Völz, A., & Graichen, K. (2022). Model predictive interaction control based on a path-following formulation. In Proceedings IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 551-556). Guilin (China).
- Graichen, K., & Görges, D. (2022). Ausgewählte Beiträge des GMA Fachausschusses 1.50 .,Grundlagen vernetzter Systeme". At-Automatisierungstechnik, 70(4), 315-316. https://doi.org/10.1515/auto-2022-0033
- Huber, H., Burk, D., & Graichen, K. (2022). Comparison of sensitivity-based and ADMM-based DMPC applied to building automation. In Proceedings of the 6th IEEE Conference on Control Technology and Applications (CCTA) (pp. 546-553). Trieste (Italy).
- Kowalewski, J., Lorenz, A., Lomakin, A., Alvarez, R., & Graichen, K. (2022). Circulating current control and energy balancing of a modular multilevel converter using model predictive control for HVDC applications. In Proceedings of the 48th Annual Conference of the IEEE Industrial Electronics Society (IECON 2022). Brussels (BE).
- Lamprecht, A., Steffen, D., Nagel, K., Häcker, J., & Graichen, K. (2022). Online Model Predictive Motion Cueing With Real-Time Driver Prediction. IEEE Transactions on Intelligent Transportation Systems, 23(8), 12414-12428. https://doi.org/10.1109/tits.2021.3114003
- Landgraf, D., Völz, A., & Graichen, K. (2022). Nonlinear model predictive control with latent force models. In Proceedings 2022 American Control Conference (ACC) (pp. 4979-4984). Atlanta, GA (USA).
- Landgraf, D., Völz, A., Kontes, G., Graichen, K., & Mutschler, C. (2022). Hierarchical learning for model predictive collision avoidance. In IFAC PapersOnLine (pp. 355-360). Vienna (Austria).
- Makowicki, T., Bitzer, M., & Graichen, K. (2022). Cycle-by-Cycle Combustion Optimisation: Calibration of Data-based Models and Improvements of Computational Efficiency. Mathematical and Computer Modelling of Dynamical Systems, (accepted). https://doi.org/10.1080/13873954.2022.2052111
- 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).
- Reinhard, J., Löhe, K., & Graichen, K. (2022). Optimal current setpoint computation for externally excited synchronous machines. In Proceedings of the 6th IEEE Conference on Control Technology and Applications (CCTA) (pp. 1319-1326). 6th IEEE Conference on Control Technology and Applications (CCTA).
- Schumann, M., Ebersberger, S., & Graichen, K. (2022). Dynamic and stationary state estimation of fluid cooled three-phase inverters. In Proceedings of the 26th IEEE International Symposium on Power Electronics, Electrical Drives Automation and Motion (SPEEDAM 2022). Sorrento (Italy).
- Snobar, F.S.N., Reinhard, J., Huber, H., Hoffmann, M., Stelzig, M., Vossiek, M., & Graichen, K. (2022). FOV-based model predictive object tracking for quadcopters. In Proceedings of the 9th IFAC Symposium on Mechatronic Systems (Mechatronics 2022) (pp. 13 - 18). Los Angeles, CA (USA).
- Stecher, J., Kiltz, L., & Graichen, K. (2022). Semi-infinite programming using Gaussian process regression for robust design optimization. In Proceedings European Control Conference (pp. 52-59). London (UK).
2021
- Burk, D., Völz, A., & Graichen, K. (2021). Experimental validation of the open-source DMPC framework GRAMPC-D applied to the remote-accessible robotarium. In Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA).
- Burk, D., Völz, A., & Graichen, K. (2021). Towards asynchronous ADMM for distributed model predictive control of nonlinear systems. In Proceedings European Control Conference (ECC 2021) (pp. 1950-1955).
- Gold, T., Rohrmüller, M., Völz, A., & Graichen, K. (2021). Model predictive interaction control for force closure grasping. In Proceedings of the 2021 IEEE Conference on Decision and Control (CDC) (pp. 1018-1023). Austin, TX, USA.
- Huber, H., & Graichen, K. (2021). A sensitivity-based distributed model predictive control algorithm for nonlinear continuous-time systems. In 5th IEEE Conference on Control Technology and Applications (CCTA) (pp. (accepted)).
- Lamprecht, A., Emmert, T., & Graichen, K. (2021). Learning-based driver prediction for MPC-based motion cueing algorithms. In Driving Simulation Conference Europe 2021 (DSC) (pp. 133 - 140).
- Lamprecht, A., Steffen, D., Häcker, J., & Graichen, K. (2021). Potential der modellprädiktiven Regelung für Fahrsimulatoren. At-Automatisierungstechnik, 69(2), 155-170. https://doi.org/10.1515/auto-2020-0090
- Lukassek, M., Völz, A., Szabo, T., & Graichen, K. (2021). Model predictive path-following control for general n-trailer systems with an arbitrary guidance point. In Proceedings European Control Conference (ECC 2021) (pp. 1329-1334).
- Völz, A., & Graichen, K. (2021). Gradient-based nonlinear model predictive control for systems with state-dependent mass matrix. In Proceedings of the 2021 IEEE Conference on Decision and Control (CDC), accepted.
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..
- Burk, D., Völz, A., & Graichen, K. (2020). Distributed optimization with ALADIN for non-convex optimal control problems. In 59th IEEE Conference on Decision and Control (CDC 2020).
- Burk, D., Völz, A., & Graichen, K. (2020). Neighbor approximations for distributed optimal control of nonlinear networked systems. In Proceedings of the European Control Conference (ECC 2020) (pp. 1238-1243).
- 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
- 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), 211-222. https://doi.org/10.4271/03-13-02-0015
- Gold, T., Lomakin, A., Goller, T., Völz, A., & Graichen, K. (2020). Towards a Generic Manipulation Framework for Robots based on Model Predictive Interaction Control. In Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 401 - 407). Beijing, CN.
- Gold, T., Völz, A., & Graichen, K. (2020). Model Predictive Interaction Control for Industrial Robots. In Proceedings of the 21st IFAC World Congress (pp. 10026 - 10033). Berlin, DE.
- Gold, T., Völz, A., & Graichen, K. (2020). Model Predictive Position and Force Trajectory Tracking Control for Robot-Environment Interaction. In Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 7397-7402). Las Vegas, NV, USA, US.
- Jemaa, K., Kotman, P., Reimann, S., & Graichen, K. (2020). Tube-based internal model control of minimum-phase input-affine MIMO systems under input constraints. In Proceedings of the 21st IFAC World Congress.
- Joos, S., Trachte, A., Bitzer, M., & Graichen, K. (2020). Constrained real-time control of hydromechanical powertrains - methodology and practical application. Mechatronics, 71. https://doi.org/10.1016/j.mechatronics.2020.102397
- Kruse, T., & Graichen, K. (2020). Moving horizon estimation for continuous glucose monitoring. In Proceedings of the 7th International Conference on Biomedical Engineering and Systems (ICBES 20).
- Lomakin, A., Mayr, A., Graichen, K., & Franke, J. (2020). Optimization of direct winding processes based on a holistic control approach. In Proceedings of the Electric Drives Production Conference (E-DPC). Ludwigsburg (D).
- Lukassek, M., Völz, A., Szabo, T., & Graichen, K. (2020). Model predictive control for agricultural machines with implements. In Proceedings 28th Mediterranean Conference on Control and Automation (MED) (pp. 387-392).
- Mayr, A., Kißkalt, D., Lomakin, A., Graichen, K., & Franke, J. (2020). Towards an intelligent linear winding process through sensor integration and machine learning techniques. In Proceedings of the 8th CIRP Global Web Conference – Flexible Mass Customisation (CIRPe 2020).
- 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).
- Völz, A., & Graichen, K. (2020). Prädiktive Pfadfolgeregelung für die kollisionsfreie Bewegungsplanung von Robotern. At-Automatisierungstechnik, 68(7), 557-570. https://doi.org/10.1515/auto-2020-0048