Index

Energy Solutions

The efficient and sustainable use of energy is one of the grand challenges today. Control engineering is a key technology to accomplish this goal by providing tailored automation solutions. For instance, heating, ventilation and air conditioning (HVAC) systems in residential and non-residential buildings belong to the largest energy consumers. Advanced control schemes can help to efficiently control HVAC systems while learning the building characteristics like thermal dynamics or room occupancy patterns and considering stochastic weather forecasts to reduce the energy consumption in a predictive manner.

Another example are electrical grids that connect energy sources (power plants, wind turbines, etc.) and energy sinks (e.g. factories, households). On the lower level, each involved component, e.g. drives and inverters, can be designed to reduce undesired power dissipation. On the higher level, the energy distribution itself can be optimized using, for instance, distributed model predictive control for smart grids.

Despite the current prevalence of batteries as mobile energy storages, hydrogen-based fuel cells are a promising alternative for applications with high energy demand as, for example, trains or ships. One option to solve the problem of save hydrogen storage is the use of liquid organic hydrogen carriers (LOHC), either as storage/transport medium or for direct fuel cell usage. The de-/hydrogenation of LOHC is a complex process that offers high potential for improvement by modern control methods.

Related projects since 2021

Related publications

Learning in Control

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

Control & Optimization

The development of control and optimization methods for dynamical systems is the natural research focus of the Chair of Automatic Control. In particular, we focus on nonlinear and predictive control concepts as well as path/trajectory planning for dynamical systems closely related to optimization-based methods, always having an eye on the real-time and embedded realization for practical applications.

Contact

Prof. Dr.-Ing. Knut Graichen
Tel.: +49 9131 85-27127
E-Mail | Homepage

---

Research on nonlinear systems and control is at the heart of the Chair of Automatic Control. Modern control concepts such as model predictive control (MPC) often rely on optimization problems that have to be solved online. In particular mechatronic systems often require sampling times in the (sub-)millisecond range and therefore highly efficient control algorithms and warm-start strategies. The Chair of Automatic Control has long standing experience with the modeling of control problems of different physical domains and the development of nonlinear and predictive control concepts, always with the intention to bring these methods into practice. We also develop and maintain the open source MPC toolbox GRAMPC that was successfully used in many research and industrial projects and by other research groups. Current research concerns the extension to stochastic nonlinear systems to account for uncertainties in a consistent probabilistic setting.

Beyond the “classical” centralized view on control applications, networked systems are of increasing importance, not only in terms of autonomous and mobile robots, distributed energy networks (smart grids), but also in connection with industry 4.0 and flexible production. The control of networked systems is challenging, because centralized approaches do not scale well with the number of subsystems and do not provide the flexibility for plug-and-play or reconfiguration scenarios. We focus on both the design of distributed (model predictive) control schemes for networked systems as well as the agent-based distributed implementation of these concepts along with suitable communication concepts to enhance the overall efficiency of networked systems. An outcome of this research is the open-source framework GRAMPC-D that implements a real-time efficient ADMM algorithm for distributed model predictive control of nonlinear networked systems including plug-and-play functionality.

 

Videos

Model predictive control

• Real-time NMPC of a magnetic levitation system
• NMPC of an overhead crane (lab scale)

Distributed MPC

• Fully actuated spring-mass system
• Partially actuated spring-mass system
• Control of mobile robots at the Robotarium
• Leader-follower control of the XPlanar system
• Cooperative transport with the XPlanar system

Related projects since 2021

Anomaly detection and intelligent recalibration of sensorized systems

Term: 1. November 2021 - 31. October 2024
Funding source: Industrie
Project leader: Knut Graichen

→ More information

Robust control of modular multi-level converters

Term: 16. June 2021 - 31. December 2024
Funding source: Industrie
Project leader: Knut Graichen

→ More information

Innovative Regelungs- und Steuerungsstrategien für Druckerhöhungsanlagen - TP Erlangen

Term: 1. July 2021 - 30. June 2024
Funding source: Bayerische Forschungsstiftung
Project leader: Knut Graichen

→ More information

Motion planning for driving simulators

Term: 1. January 2021 - 30. June 2021
Funding source: Industrie
Project leader: Knut Graichen

→ More information

Feasibility of stochastic model predictive control for autonomous driving

Term: 1. August 2022 - 31. December 2022
Funding source: Industrie
Project leader: Knut Graichen

→ More information

Control of ring resonator modulators in optical communication

Term: 1. February 2022 - 31. January 2025
Funding source: Industrie
Project leader: Knut Graichen

→ More information

Cooperative manipulation with dual-arm robots at the payload limit

Term: 1. September 2022 - 31. August 2025
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Project leader: Andreas Völz

Dual-armrobots offer a high potential for automation technology, as they canbe used to implement tasks that are not possible with one arm alone.This includes in particular the manipulation of large or heavyobjects that exceed the payload of a single arm. Illustrativeexamples are the movement of beverage crates, long boards or pipes,which are also preferably grasped by humans with both hands.

However,cooperative manipulation is particularly challenging, because botharms…

→ More information

Distributed model predictive control of nonlinear systems with asynchronous communication

Term: 1. January 2022 - 31. December 2024
Funding source: Deutsche Forschungsgemeinschaft (DFG)
Project leader: Knut Graichen

→ More information

SPP 2364: Formulation of dispersed systems via (melt) emulsification: Process design, in situ diagnostics and regulation

Term: 1. January 2023 - 31. December 2025
Funding source: DFG / Schwerpunktprogramm (SPP)
Project leader: Jochen Schmidt, Franz Huber, Knut Graichen

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.

→ More information

Robust energy-based control of MMC/HVDC systems

Term: 15. June 2023 - 31. December 2026
Funding source: Industrie
Project leader: Knut Graichen

→ More information

Model predictive flight control

Term: 1. August 2023 - 31. July 2026
Funding source: Industrie
Project leader: Knut Graichen

→ More information

Receding horizon time-optimal path parameterization for robotic manipulators

Term: 1. July 2024 - 31. December 2024
Funding source: Industrie
Project leader: Knut Graichen

→ More information

Realizability of advanced control concepts on FPGA hardware

Term: 1. September 2024 - 31. May 2025
Funding source: Industrie
Project leader: Knut Graichen

→ More information

Related publications

Since 2023

• Hartmann P, Graichen K (2025). Learning-based uncertainty-aware predictive control of truck-trailer systems in rough terrain. In 19th IEEE International Conference on Control & Automation (ICCA) (accepted).
• Kruse T, Griebel T, Graichen K (2025). Adaptive Kalman filtering: Measurement and process noise covariance estimation using Kalman smoothing. IEEE Access, 13, 11863-11875. [DOI].
• Landgraf D, Wietzke T, Graichen K (2025). Stochastic model predictive control with switched latent force models. In European Control Conference.
• 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. [DOI].
• 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. [DOI].
• Pierer von Esch M, Völz A, Graichen K (2025). Asynchronous sensitivity-based distributed optimal control for nonlinear systems. In 2025 American Control Conference (ACC).
• 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, (accepted).
• Cherian AJ, Michalka A, Murray K, Roell G, Graichen K (2024). Control approaches for operating point stabilization of microring resonator modulators under fast perturbations. In Silicon Photonics XIX (pp. 128910D). [DOI].
• Conrad P, Graichen K (2024). A sensitivity-based approach to self-triggered nonlinear model predictive control. IEEE Access, 12, 153243-153252. [DOI].
• Dahlmann J, Graichen K, Völz A (2024). Ein Konzept zum automatisierten Rangieren von Fahrzeugen mit Anhängern. At-Automatisierungstechnik, 72(4), 354-368. [DOI].
• 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. [DOI].
• Goppelt-Schneider F, Schmidt-Vollus R, Graichen K (2024). Trajectory tracking control for multilevel pressure boosting systems. In 28th International Conference on System Theory, Control and Computing (ICSTCC).
• 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].
• 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. [DOI].
• 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 Iron and Steel Technology Conference (pp. 1212-1223). Warrendale, PA. [DOI].
• Pierer von Esch M, Landgraf D, Steffel M, Völz A, Graichen K (2024). Distributed stochastic optimal control of nonlinear systems based on ADMM. In 63th IEEE Conference on Decision and Control (CDC 2024).
• 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. [DOI].
• 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. [DOI].
• 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. [DOI].
• Reinhard J, Löhe K, Graichen K (2024). Optimal dynamic current control for externally excited synchronous machines. In 2024 IEEE Conference on Control Technology and Applications (CCTA) (pp. 146-152). [DOI].
• 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. [DOI].
• Südhoff T, Ebner L, Schmidt J, Graichen K (2024). GP-based modeling for PSD control of emulsification processes. In 28th International Conference on System Theory, Control and Computing (ICSTCC).
• Dio M, Völz A, Graichen K (2023). Cooperative dual-arm control for heavy object manipulation based on hierarchical quadratic programming. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 643-648).
• Gold T, Völz A, Graichen K (2023). Model predictive interaction control for robotic manipulation tasks. IEEE Transactions on Robotics, 39(1), 76-89. [DOI].
• 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. [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].
• Spenger P, Graichen K (2023). Performance prediction of NMPC algorithms with incomplete optimization. In 22nd IFAC World Congress (pp. 7456-7461).

2022

• 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. [DOI].
• Burk D, Völz A, Graichen K (2022). Improving the performance of distributed model predictive control by applying graph partitioning methods. In 26th International Conference on System Theory, Control and Computing (ICSTCC). [DOI].
• Dahlmann J, Volz A, Szabo T, Graichen K (2022). A Numerical Approach for Solving the Inversion Problem for n-Trailer Systems. In 2022 American Control Conference, ACC 2022 (pp. 2018-2024). Proceedings of the American Control Conference, 2022-June, 2018-2024. [DOI].
• Dahlmann J, Völz A, Szabo T, Graichen K (2022). A numerical approach for solving the inversion problem for general n-trailer systems.. In Proceedings 2022 American Control Conference (ACC) (pp. 2018-2024). [DOI].
• Dahlmann J, Völz A, Szabo T, Graichen K (2022). Trajectory optimization for truck-trailer systems based on predictive path-following control. In 6th IEEE Conference on Control Technology and Applications (CCTA). [DOI].
• 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). [DOI].
• Huber H, Burk D, Graichen K (2022). Comparison of sensitivity-based and ADMM-based DMPC applied to building automation. In 6th IEEE Conference on Control Technology and Applications (CCTA) (pp. 546-553). [DOI].
• 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 48th Annual Conference of the IEEE Industrial Electronics Society (IECON 2022). [DOI].
• 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. [DOI].
• 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). [DOI].
• Landgraf D, Völz A, Kontes G, Graichen K, Mutschler C (2022). Hierarchical learning for model predictive collision avoidance. In 10th Vienna International Conference on Mathematical Modelling (MATHMOD 22) (pp. 355-360). [DOI].
• Reinhard J, Löhe K, Graichen K (2022). Optimal current setpoint computation for externally excited synchronous machines. In 6th IEEE Conference on Control Technology and Applications (CCTA) (pp. 1319-1326). [DOI].
• Snobar F, Reinhard J, Huber H, Hoffmann M, Stelzig M, Vossiek M, Graichen K (2022). FOV-based model predictive object tracking for quadcopters. In 9th IFAC Symposium on Mechatronic Systems (Mechatronics 2022) (pp. 13 – 18). [DOI].
• 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). [DOI].

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 IEEE International Conference on Mechatronics and Automation (ICMA). [DOI].
• 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). [DOI].
• Gold T, Rohrmüller M, Völz A, Graichen K (2021). Model predictive interaction control for force closure grasping. In 2021 IEEE Conference on Decision and Control (CDC) (pp. 1018-1023). [DOI].
• 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)). [DOI].
• 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. [DOI].
• 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). [DOI].
• Völz A, Graichen K (2021). Gradient-based nonlinear model predictive control for systems with state-dependent mass matrix. In 2021 IEEE Conference on Decision and Control (CDC), accepted. [DOI].

2020

• 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). [DOI].
• Burk D, Völz A, Graichen K (2020). Neighbor approximations for distributed optimal control of nonlinear networked systems. In European Control Conference (ECC 2020) (pp. 1238-1243). [DOI].
• Englert T, Graichen K (2020). Nonlinear model predictive torque control and setpoint computation of induction machines for high performance applications. Control Engineering Practice, 99. [DOI].
• 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 IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 401 – 407). [DOI].
• Gold T, Völz A, Graichen K (2020). Model Predictive Interaction Control for Industrial Robots. In 21st IFAC World Congress (pp. 10026 – 10033). [DOI].
• Gold T, Völz A, Graichen K (2020). Model Predictive Position and Force Trajectory Tracking Control for Robot-Environment Interaction. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 7397-7402). [DOI].
• Kruse T, Graichen K (2020). Moving horizon estimation for continuous glucose monitoring. In 7th International Conference on Biomedical Engineering and Systems (ICBES 20). [DOI].
• Lomakin A, Mayr A, Graichen K, Franke J (2020). Optimization of direct winding processes based on a holistic control approach. In Electric Drives Production Conference (E-DPC). [DOI].
• 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). [DOI].

2019

• Bestler A, Graichen K (2019). Distributed model predictive control for continuous-time nonlinear systems based on suboptimal ADMM. Optimal Control Applications & Methods, 40(1), 1-23. [DOI].
• Burk D, Völz A, Graichen K (2019). Towards a modular framework for distributed model predictive control of nonlinear neighbor-affine systems. In 58th IEEE Conference on Decision and Control (CDC 2019) (pp. 5279-5284). [DOI].
• Englert T, Völz A, Mesmer F, Rhein S, Graichen K (2019). A software framework for embedded nonlinear model predictive control using a gradient-based augmented Lagrangian approach (GRAMPC). Optimization and Engineering, 20(3), 769-809. [DOI].
• Gold T, Völz A, Graichen K (2019). External torque estimation for an industrial robot arm using joint torsion and motor current measurements. In Joint Conference 8th IFAC Symposium on Mechatronic Systems (MECHATRONICS) and 11th IFAC Symposium on Nonlinear Control Systems (NOLCOS) (pp. 879-884). Vienna (Austria). [DOI].
• Lamprecht A, Steffen D, Häcker J, Graichen K (2019). Comparison between a Filter- and an MPC-based MCA in an Offline Simulator Study. In Driving Simulation Conference & Exhibition (DSC) (pp. 101-107).
• Lamprecht A, Steffen D, Häcker J, Graichen K (2019). Optimal control based reference generation for model predictive motion cueing algorithms. In 3rd IEEE Conference on Control Technology and Application (CCTA 2019) (pp. 203-208). Hong Kong (China). [DOI].

2018 and earlier

• 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).
• 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).
• Lamprecht A, Häcker J, Graichen K (2018). Constrained motion cueing for driving simulators using a real-time nonlinear MPC scheme. In Proceedings 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 7466-7471). Madrid (Spain).
• Rhein S, Graichen K (2018). Constrained trajectory planning and actuator design for electromagnetic heating systems. Control Engineering Practice, 74, 191-203.
• Graichen K, Hentzelt S (2015). A bi-level nonlinear predictive control scheme for hopping robots with hip and tail actuation. In Proceedings 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015) (pp. 4480-4485). Hamburg (Germany).
• Hentzelt S, Graichen K (2014). Experimental results for distributed model predictive control applied to a water distribution system. In Proceedings 2014 IEEE Multi-Conference on Systems and Control (pp. 1100-1106). Nice (France).
• Kaepernick B, Graichen K (2014). PLC implementation of a nonlinear model predictive controller. In Proceedings 19th IFAC World Congress (pp. 1892-1897). Cape Town (South Africa).
• Käpernick B, Graichen K (2014). Nichtlineare modellprädiktive Regelung auf SPS. Automatisierungstechnische Praxis, 56, 38-46. [DOI].
• Käpernick B, Graichen K (2014). The gradient based nonlinear model predictive control software GRAMPC. In Proceedings 13th European Control Conference (ECC) (pp. 1170-1175). Strasbourg (France).
• Käpernick B, Süß S, Schubert E, Graichen K (2014). A synthesis strategy for nonlinear model predictive controller on FPGA. In Proceedings 2014 UKACC 10th International Conference on Control (pp. 662-667). Loughborough (UK).
• Rhein S, Utz T, Graichen K (2014). Efficient state constraint handling for MPC of the heat equation. In Proceedings 2014 UKACC 10th International Conference on Control (pp. 663-668). Loughborough (UK).
• Utz T, Rhein S, Graichen K (2014). Transformation approach to constraint handling in optimal control of the heat equation. In Proceedings 19th IFAC World Congress (pp. 9135-9140). Cape Town (South Africa).
• Hentzelt S, Graichen K (2013). An augmented Lagrangian method in distributed dynamic optimization based on approximate neighbor dynamics. In Proceedings IEEE Intern. Conf. Systems, Man, and Cybernetics (SMC 2013) (pp. 571-576). Manchester (UK).
• 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).
• Käpernick B, Hentzelt S, Graichen K (2013). A parallelizable decomposition approach for constrained optimal control problems. In Proceedings 52th IEEE Conference on Decision and Control (CDC) (pp. 5783-5788). Florence (Italy).
• Rhein S, Utz T, Graichen K (2013). Model predictive control and moving horizon estimation of a large-scale chemical reactor model. In Proceedings 1st {IFAC} Symposium on Control of Systems Governed by Partial Differential Equations (CPDE) (pp. 121-126). Paris (France).
• Graichen K, Käpernick B (2012). A real-time gradient method for nonlinear model predictive control. In Zheng T (Eds.), Frontiers of Model Predictive Control, pp. 9-28. [DOI].
• Graichen K, Käpernick B (2012). Nonlinear MPC with systematic handling of a class of constraints. In Oberwolfach Report No. 12/2012 (Workshop “Control Theory: Mathematical Perspectives on Complex Networked Systems”) (pp. 17-18). Oberwolfach (Germany). [DOI].
• Hentzelt S, Graichen K (2012). Dual decomposition of optimal control problems with coupled nonlinear dynamics. In CD-ROM Proceedings European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2012). Vienna (Austria).

Mechatronics & Automotive

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

Related projects since 2021

Related publications

Robotics

Robots should move and interact with humans as efficiently as possible. If this includes high velocities or heavy payloads, the nonlinear rigid body dynamics as well as the input constraints need to be taken into account, which leads to a computationally demanding optimization problem. On the other hand, when the robot is in contact with its environment, the rigid body dynamics are often less relevant, but the forces and torques that arise must be considered by the control system. This is especially important for safe human-robot interaction. Here, the challenges include the contact modelling (e.g. stiffness and friction), the safe handling of contact loss, or the selection of controller parameters for different applications. Current research considers model predictive interaction control (MPIC), which refers to MPC with explicit prediction of contact forces and torques, as well as the development of specialized algorithms for solving optimization problems with rigid body dynamics.

Besides controlling motions, also the planning of paths (geometric description) and trajectories (time information) is a relevant problem for many types of robots. In particular for mobile and collaborative robots, motions should be planned in such a way that they do not cause self-collisions or collisions with obstacles in the environment. Global planners iteratively build a search structure that explores the space of possible motions, whereas local planners only search in the neighborhood of an initial solution. In order to efficiently find high-quality solutions, it is necessary to combine the advantages of both global and local planning methods. Further difficulties arise in dynamic environments, where the future motion of obstacles needs to be predicted or for car-like robots, where the non-holonomic kinematics need to be considered.

The Chair is equipped with a mobile dual-arm robot as well as a motion capturing system that have been funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the major instrumentation proposal INST 90/1167-1 FUGG. The system enables the experimental validation of planning and control methods for mobile manipulation in workspaces shared with humans. For more information contact Prof. Dr.-Ing. Knut Graichen or Dr.-Ing. Andreas Völz.

Videos

Related projects since 2021

Related publications

Publications

Projects

• Energy-Efficient Electro-Photonic Integrated Circuits for High-Performance Computing

  
  (Third Party Funds Group – Overall project)
  Term: 1. April 2025 - 31. March 2028
  Funding source: Bayerische Forschungsstiftung
  
  →More information

• Advanced monitoring and optimization for robotic strain wave gears

  
  (Third Party Funds Single)
  Term: 1. November 2024 - 30. April 2026
  Funding source: Industrie
  
  →More information

• Realizability of advanced control concepts on FPGA hardware

  
  (Third Party Funds Single)
  Term: 1. September 2024 - 31. May 2025
  Funding source: Industrie
  
  →More information

• AI-Supported modeling for friction estimation

  
  (Third Party Funds Single)
  Term: 1. September 2024 - 31. August 2025
  Funding source: Industrie
  
  →More information

• Receding horizon time-optimal path parameterization for robotic manipulators

  
  (Third Party Funds Single)
  Term: 1. July 2024 - 31. December 2024
  Funding source: Industrie
  
  →More information

• Optimized Reinforcement Architecture for Complex Energy Management

  
  (Third Party Funds Single)
  Term: 1. July 2024 - 30. June 2027
  Funding source: Industrie
  
  →More information

• Development of an innovative camera-based framework for collision-free human-machine movement

  
  (Third Party Funds Single)
  Term: 1. February 2024 - 31. January 2026
  Funding source: Bundesministerium für Wirtschaft und Klimaschutz (BMWK)
  
  →More information

• OXO-LOHC: Autotherme und ultratiefe Wasserstoff-Freisetzung aus LOHC

  
  (Third Party Funds Single)
  Term: 1. November 2023 - 31. October 2028
  Funding source: Helmholtz-Gemeinschaft
  
  →More information

• Robust Reinforcement Learning for Thermal Management Control

  
  (Third Party Funds Single)
  Term: 1. August 2023 - 31. July 2024
  Funding source: Industrie
  
  →More information

• Model predictive flight control

  
  (Third Party Funds Single)
  Term: 1. August 2023 - 31. July 2026
  Funding source: Industrie
  
  →More information

• Robust energy-based control of MMC/HVDC systems

  
  (Third Party Funds Single)
  Term: 15. June 2023 - 31. December 2026
  Funding source: Industrie
  
  →More information

• Hardware architecture, automatic control, autonomy functionality, and developer community: Modular learning control and planning for mobile professional operation vehicles

  
  (Third Party Funds Group – Sub project)
  Overall project: POV.OS - Hardware and software platform for mobile professional operation vehicles
  Term: 1. January 2023 - 31. December 2025
  Funding source: Bundesministerium für Wirtschaft und Klimaschutz (BMWK)
  
  →More information

• 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
  Term: 1. January 2023 - 31. December 2025
  Funding source: DFG / Schwerpunktprogramm (SPP)
  
  Abstract

  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.

  →More information

• Formulierung von dispersen Systemen durch (Schmelz-)Emulgierung: Prozessgestaltung, In-situ-Diagnostik und Regelung

  
  (Third Party Funds Group – Sub project)
  Overall project: Autonome Prozesse in der Partikeltechnik - Erforschung und Erprobung von Konzepten zur modellbasierten Führung partikeltechnischer Prozesse
  Term: 1. January 2023 - 31. December 2025
  Funding source: DFG / Schwerpunktprogramm (SPP)
  
  Abstract

  Ziel dieses Projekts ist die automatisierte Herstellung von flüssig-flüssig-dispersen Systemen über Schmelzeemulgieren, wobei bei diesem Prozess das Emulgieren bei erhöhter Temperatur erfolgt. Als Produkte werden nach der Abkühlung Dispersionen von sphärischen Nano- oder Mikropartikeln erhalten. In Rahmen dieses Projekts wird ein Schmelzeemulgierprozess für die automatisierte Herstellung von Produktpartikeln mit wohldefinierter Partikelgrößenverteilung (PGV) betrachtet. Diese beeinflusst dabei maßgeblich die späteren Produkteigenschaften, wie zum Beispiel das Fließverhalten oder wie die Wirkstofffreisetzungskinetik. Die PGV der Produkte wird dabei durch das komplexe Zusammenspiel konkurrierender Mechanismen bestimmt. Dies sind insbesondere der Tropfenaufbruch in einem Rotor-Stator infolge von Scher- und Dehnbeanspruchung sowie die Koaleszenz und weitere Reifung, die ihrerseits von der Systemzusammensetzung, d.h. dem genutzten Emulgator (Art, Konzentration) und der Dispersphase (Viskosität, Volumenanteil) abhängig sind. Für ein besseres Prozessverständnis und eine aktive Prozessregelung sind daher Möglichkeiten zur in situ Bestimmung der PGV dringend erforderlich. In diesem Projekt wird zur in situ Messung der PGV ein neuartiges, auf breitbandiger elastischer Lichtstreuung basierendes fasergekoppeltes Messsystem entwickelt. Dieses wird an Referenzpartikelsystemen validiert und am Emulgierprozess eingesetzt. Weiterhin wird ein hybrides Prozessmodell entwickelt, das die Basis für das Design einer modellprädiktiven Regelung des Prozesses darstellt. Die modellprädiktive Regelung wird in Kombination mit der in situ Messung die Möglichkeit für eine aktive Prozesssteuerung und die Herstellung von Emulsionen mit vorher definierten Eigenschaften bei gleichzeitiger Optimierung der Prozesszeit ermöglichen.
  

  →More information

• Predictive and learning control methods

  
  (Third Party Funds Group – Sub project)
  Overall project: AGENT-2: Agent-based data-driven modeling for stochastic and self-adjusting control of building energy systems
  Term: 1. November 2022 - 31. October 2025
  Funding source: Bundesministerium für Wirtschaft und Klimaschutz (BMWK)
  
  Abstract

  To achieve climate targets, CO2 emissions in the building sector have to be significantly reduced. However, the integration of renewable energy sources increases the complexity of building energy systems and thus the requirements for the operation strategy. Model-based and predictive controllers are necessary for efficient operation. However, due to the high complexity of the energy systems, the development, implementation, and commissioning are very complex leading to high costs, which is why model predictive and optimization-based control strategies are rarely used in practice so far. The goal of the AGENT-2 project is to develop a self-adjusting and self-learning model-predictive control concept that reduces the implementation and commissioning effort and thus increases the applicability of efficient operating strategies in practice. The control concept to be developed is based on distributed agents, each of which learns the system behavior of a subsystem and controls the subsystem. This is based on the findings and the framework developed in the previous project AGENT. The operation of the overall system is achieved by the interaction oft h e self-learning agents with each other. Thus, a self-adjusting and scalable control strategy for building energy systems is created. The self-learning control strategy is compared with state-of-the-art concepts in simulations and tested in practical operation in two demonstration buildings. The findings will be generalized and possibilities for the transfer into practice will be investigated. The project thus contributes to increasing the efficiency of building operation and to reducing the costs of controller implementation and commissioning.

  →More information

• Robust Planning and Control using Probabilistic Methods

  
  (Third Party Funds Group – Sub project)
  Overall project: Verbundprojekt MANNHEIM-AUTOtech.agil: Architektur und Technologien zur Orchestrierung automobiltechnischer Agilität
  Term: 1. October 2022 - 30. September 2025
  Funding source: Bundesministerium für Bildung und Forschung (BMBF)
  
  →More information

• Cooperative manipulation with dual-arm robots at the payload limit

  
  (Third Party Funds Single)
  Term: 1. September 2022 - 31. August 2025
  Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
  
  Abstract

  Dual-armrobots offer a high potential for automation technology, as they canbe used to implement tasks that are not possible with one arm alone.This includes in particular the manipulation of large or heavyobjects that exceed the payload of a single arm. Illustrativeexamples are the movement of beverage crates, long boards or pipes,which are also preferably grasped by humans with both hands.

  However,cooperative manipulation is particularly challenging, because botharms and the grasped object form a closed kinematic chain. Thecorresponding constraints reduce the number of degrees of freedom andmust be taken into account on the levels of control, trajectory andpath planning. Conversely, the system has the advantage that the loadcan be flexibly distributed on both arms due to the redundantactuators. This is especially crucial for heavy objects, since it isthe only way to comply with actuator torque constraints. The firstgoal of the research project is therefore the development of adynamic load distribution that explicitly takes actuator constraintsinto account and is thus suited for high payloads. To this end, anoptimization-based approach is pursued with a focus on efficiency andreal-time capability.

  Moreover,this load distribution must be taken into account on all systemlevels, since otherwise large payloads can lead to the situation thatno admissible trajectory can be computed for a path or that atrajectory is not executable by the controller. Consequently, thesecond goal is the consistent consideration of the dynamic loaddistribution. On the control level, this includes not only theisolated solution of the optimal load distribution in each samplingstep, but also the approach of a forward-looking model predictivecontroller. For trajectory planning, on the one hand, a time-optimaltrajectory generation with subordinate solution of the dynamic loaddistribution and, on the other hand, the extension of the modelpredictive controller to a predictive path-following controller shallbe investigated. Furthermore, a path planner for dual-arm robots willbe developed for the first time, which explicitly considers thepayload and can be extended in a modular manner to take collisions aswell as the additional degrees of freedom of a mobile base intoaccount.

  Thethird goal is the extensive experimental validation of the control,trajectory and path planning methods in order to practicallydemonstrate the potential of cooperative manipulation with dual-armrobots. For this purpose, a mobile dual-arm robot with additionalmotion capturing system from the DFG major instrumentation proposal438833210 is available at the Chair of Automatic Control. Inparticular, movements with large and heavy objects shall beperformed, whose mass is in the order of magnitude of the combinedmaximum payload of both arms.

  →More information

• Feasibility of stochastic model predictive control for autonomous driving

  
  (Third Party Funds Single)
  Term: 1. August 2022 - 31. December 2022
  Funding source: Industrie
  
  →More information

• Control of ring resonator modulators in optical communication

  
  (Third Party Funds Single)
  Term: 1. February 2022 - 31. January 2025
  Funding source: Industrie
  
  →More information

• Distributed model predictive control of nonlinear systems with asynchronous communication

  
  (Third Party Funds Single)
  Term: 1. January 2022 - 31. December 2024
  Funding source: Deutsche Forschungsgemeinschaft (DFG)
  
  →More information

• Anomaly detection and intelligent recalibration of sensorized systems

  
  (Third Party Funds Single)
  Term: 1. November 2021 - 31. October 2024
  Funding source: Industrie
  
  →More information

• KI-unterstützte Modellierung zur Steigerung der Regelgüte

  
  (Third Party Funds Single)
  Term: 1. July 2021 - 30. June 2024
  Funding source: Industrie
  
  →More information

• Kinesthetic teaching and predictive control of interaction tasks in robotics

  
  (Third Party Funds Single)
  Term: 1. July 2021 - 30. June 2024
  Funding source: Deutsche Forschungsgemeinschaft (DFG)
  
  Abstract

  Precise interactions as part of industrial manufacturing tasks are typically very complex to characterize and implement. One reason for this is the heterogeneity of the task-specific requirements for the motion and control behavior. A direct implementation of the task into a robot program therefore requires highly qualified specialists and is only profitable for large lot sizes. For a flexible applicability and easy (re-)configuration of the robot system, an approach to programming by kinesthetic demonstration is developed in this project. The robot is guided by the user through the entire manipulation task, while the robot motion as well as the interaction forces are simultaneously recorded. Typically, several repetitions of the demonstration are necessary in order to compensate for the suboptimality and imprecision of the human demonstration.  This is particularly important for complex motion sequences or interaction situations, such as periodic movements or the assembly of components, that are difficult to demonstrate but at the same time are crucial for a successful task execution. 

  The basis for this project is a previously developed general framework for model predictive interaction control (MPIC). The manipulation task is split into a sequence of elementary tasks, so-called manipulation primitives (MPs) with individual motion and control characteristics, which are treated in a holistic manner by a model predictive control approach. The MPIC approach is elaborated in this project regarding the kinesthetic demonstration of manipulation tasks, e.g. by considering the switching between MPs over the prediction horizon of the MPC. A further focus lies on the automatic generation of the MP sequence from the repeated demonstration of the manipulation task without requiring additional expert knowledge. Based on the demonstration, the manipulation task will be iteratively refined by learning the setpoints and the transition conditions of the MPs and finally by optimizing the overall manipulation task.

  →More information

• Innovative Regelungs- und Steuerungsstrategien für Druckerhöhungsanlagen - TP Erlangen

  
  (Third Party Funds Group – Sub project)
  Overall project: Innovative Regelungs- und Steuerungsstrategien für Druckerhöhungsanlagen
  Term: 1. July 2021 - 30. June 2024
  Funding source: Bayerische Forschungsstiftung
  
  →More information

• Robust control of modular multi-level converters

  
  (Third Party Funds Single)
  Term: 16. June 2021 - 31. December 2024
  Funding source: Industrie
  
  →More information

• Motion planning for driving simulators

  
  (Third Party Funds Single)
  Term: 1. January 2021 - 30. June 2021
  Funding source: Industrie
  
  →More information

• Thermische Umrichtermodellierung für elektrische Antriebssysteme

  
  (Third Party Funds Single)
  Term: 1. August 2020 - 31. July 2023
  Funding source: Industrie
  
  →More information

• Regelung des Antriebsstrangs beim Kaltwalzen

  
  (Third Party Funds Single)
  Term: 1. July 2020 - 31. August 2023
  Funding source: Industrie
  
  →More information

• Automated path planning for truck-trailer configurations

  
  (Third Party Funds Single)
  Term: 1. September 2019 - 31. August 2022
  Funding source: Industrie
  
  →More information

• Modulare und hierarchische Ansätze für die Regelung nebenläufiger zeitbewerteter ereignisdiskreter Systeme

  
  (Third Party Funds Single)
  Term: 15. July 2019 - 14. July 2022
  Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
  
  Abstract

  Neue Technologien haben zur Entwicklung von Systemen geführt, die weitgehend autonom agieren und typischerweise aus einer Vielzahl vernetzter Komponenten bestehen. Die Komplexität solcher Systeme erfordert neuartige Ansätze zur Modellierung und Reglersynthese, um die gewünschte Funktionalität zu garantieren. Ereignisdiskrete Systeme (discrete-event systems, DES) sind Modelle, deren Dynamik durch das Auftreten asynchroner Ereignisse charakterisiert wird. Solche Modelle eignen sich für viele "man made Systems", wie beispielsweise automatisierte Transportvorrichtungen und flexible Fertigungsanlagen. Die Modellierung ereignisdiskreter Systeme erfolgt mit aus der Informatik bekannten Beschreibungsmitteln, wie etwa endlichen Automaten, formalen Sprachen oder Petri-Netzen. Zur Reglersyn these hat sich die sog. "supervisory control theory" etabliert, bei der der Regler bzw. supervisor aus vergangenen Ereignissen ableitet, welche Ereignisse aktuell unterbunden werden müssen, um einen wunschgemäßen Ablauf des geregelten Systems zu gewährleisten. Eine zentrale Herausforderung ist hierbei die in der Komponentenzahl exponentiell wachsende Zahl von Zuständen des Gesamtsystems. Dieser begegnet man durch modulare oder hierarchische Ansätze, die das explizite Erstellen eines Gesamtmodells umgehen. In ihrer Grundform beschreiben ereignisdiskrete Systeme nur die Reihenfolge der Abfolge von Ereignissen. Dies reicht aus, um Regler zu entwerfen, die einen sicheren und zielführenden Betrieb des geregelten Systems garantieren. In vielen Anwendungen spielt aber neben Sicherheit auch Performanz eine Rolle. Letztere bezieht sich i.A. nicht nur auf die Reihenfolge sondern auch auf die Zeitpunkte, zu denen Ereignisse auftreten. Dazu bietet die Literatur eine Auswahl von Modellformen, die sich hinsichtlich ihrer Ausdrucksstärke deutlich unterscheiden. Am unteren Ende rangieren Ansätze nach Brandin/Wonham, bei denen das Verstreichen von Zeit durch das globale Ereignis "tick" abgebildet wird, sowie sog. zeitbewertete Ereignisgraphen (timed event graphs, TEGs), deren Verhalten sich als Lösungen linearer (max,+)- Gleichungen darstellen lässt. Für beide Ansätze ist die Reglersynthese gut erforscht. Allerdings wird die hier verfügbare Ausdrucksstärke vielen Anwendungen nicht gerecht: Modelliert man nach Brandin-Wonham, so lassen sich nebenläufige Prozesse, die mehrere unabhängige Echtzeituhren erfordern, nicht darstellen; verwendet man zeitbewertete Ereignisgraphen, so können logische Verzweigungen nicht dargestellt werden. In diesem Projekt wollen wir effiziente Methoden zur ereignisdiskreten Regelung für Modellformen untersuchen, die in ihrer Ausdrucksstärke über die beiden genannten Ansätze deutlich hinaus gehen. Wir streben insbesondere an, mit Hilfe modularer und hierarchischer Methoden für sog. (max,+)-Automaten und ausgewählt strukturierte Petri-Netze Regler zu entwerfen, die gegebene Anforderungen hinsichtlich Korrektheit und Performanz garantieren.

  →More information

• Agent-based systems for intelligent and robust control of complex energy systems in non-residential buildings as part of a superordinate energy system

  
  (Third Party Funds Group – Sub project)
  Overall project: Agentensysteme zur intelligenten und robusten Steuerung komplexer Energiesysteme in Nichtwohngebäuden als Bestandteil des übergeordneten Energiesystems
  Term: 1. May 2019 - 31. December 2022
  Funding source: Bundesministerium für Wirtschaft und Technologie (BMWi)
  
  Abstract
  →More information

• Formale Verifikation in der Fertigungsautomatisierung (Projektabschnitt B1)

  
  (Third Party Funds Single)
  Term: 15. April 2019 - 14. April 2020
  Funding source: Siemens AG
  
  →More information

• Trajectory planning for off-road applications

  
  (Third Party Funds Single)
  Term: 1. April 2019 - 31. October 2021
  Funding source: Industrie
  
  →More information

• Modular distributed model predictive control of nonlinear systems with neighborhood models

  
  (Third Party Funds Single)
  Term: 1. April 2019 - 31. December 2020
  Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
  
  Abstract

  The steadily growing demands on efficiency and flexibility of modern automation and control systems requires a broader design approach for the overall system that goes beyond the isolated look at and control of single subsystems. Decentral and distributed control schemes follow this holistic design approach by including the interdependencies between the subsystems in the control design.

  Model predictive control (MPC) appears to be a suitable control approach to tackle these kind of systems. In essence, MPC relies on the numerical solution of a finite-horizon dynamic optimization problem that is repetitively solved according to the sampling rate of the system. An extension of MPC to coupled systems is distributed MPC (DMPC), which assigns a single communicating MPC agent to each subsystem. 

  The goal of the project is to develop a DMPC scheme for nonlinear coupled systems, where each MPC agent contains a neighborhood model that anticipates the dynamical behavior of its neighbors in order to enhance the convergence and robustness of the distributed algorithm. Besides the development and mathematical investigation of the methodology, a further goal of the project is the numerical and experimental realization of the control approach. A particular intention of the project is to develop a modular framework that allows for an easy configuration and adaptation of the coupling structure for suitable system classes. 

  →More information

• Compliance for a robotic assistance system

  
  (Third Party Funds Single)
  Term: 1. April 2019 - 30. September 2022
  Funding source: Industrie
  
  →More information

• Formale Verifikation in der Fertigungsautomatisierung (Projektabschnitt A)

  
  (Third Party Funds Single)
  Term: 15. April 2018 - 14. April 2019
  Funding source: Siemens AG
  
  →More information

• Fehlerdiagnose verteilt-parametrischer Systeme mittels Modulationsfunktionen

  
  (Third Party Funds Single)
  Term: 1. April 2018 - 31. March 2021
  Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
  
  →More information

• Systematischer Entwurf hierarchisch-hybrider Regler

  
  (Third Party Funds Group – Sub project)
  Overall project: FOR 468: Methods from discrete mathematics for the synthesis and control of chemical processes
  Term: 1. January 2006 - 30. January 2011
  Funding source: DFG / Forschungsgruppe (FOR)
  
  Abstract

  Ziel des beschriebenen Forschungsvorhabens ist die Weiterentwicklung eines in der ersten Antragsphase untersuchten Verfahrens zum hierarchischen Entwurf hybrider Regelsysteme. Das genannte Verfahren ermöglicht es, sowohl ingenieurwissenschaftliche Intuition und regelungstechnische Standardverfahren als auch moderne Methoden der diskreten Optimierung auf sichere Weise in einen formalen Entwurfsvorgang einzubinden. Es trägt so zur Beherrschbarkeit der hybriden Regelproblemen innewohnenden Komplexität bei und wurde in der ersten Antragsphase erfolgreich auf eine Mehrprodukt-Batchanlage angewandt. Um das Verfahren zu einer praxistauglichen Entwurfsmethode zu machen, sind eine Reihe von methodischen Weiterentwicklungen notwendig. Diese betreffen strukturelle Fragen hinsichtlich der hierarchischen Reglerarchitektur, Fragen der effizienten Berechnung diskreter Abstraktionen durch sichere Abschätzung erreichbarer Zustandsmengen sowie algorithmische Fragen beim Entwurf ereignisdiskreter Reglerebenen. Die praktische Anwendbarkeit des resultierenden hierarchischen Entwurfsverfahrens soll anhand eines strukturvariablen chromatographischen Trennprozesses nachgewiesen werden.

  →More information

Research

Control & Optimization

The development of control and optimization methods for dynamical systems is the natural research focus of the Chair of Automatic Control. In particular, we focus on nonlinear and predictive control concepts as well as path/trajectory planning for dynamical systems closely related to optimization-based methods, always having an eye on the real-time and embedded realization for practical applications.

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.

Discrete Event Systems

Discrete event systems (DES) are dynamical systems with a finite-range state variable. Prototypical application domains are so called “men made systems”, e.g., for automated manufacturing or logistics, which by construction can be adequately represented by discrete-event models. Our DES research group develops methods for the analysis and synthesis of discrete-event systems with a particular focus on modular and/or hierarchical control architectures.

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.

Robotics

Robotics deals in general with machines that can assist or perform the execution of tasks such as assembly or machining tasks by industrial robots. Research projects in this area concern, for example, the control of motions and forces in human-robot interaction as well as the planning of paths and trajectories for mobile and collaborative robots.

Energy Solutions

The energy-efficient operation of technical systems is of increasing importance in view of global warming and the transition to renewable energies. Control systems engineering has a high potential to contribute to the efficient use of energy, for example, regarding energy consumption of buildings, energy conversion and storage, as well as power grids. Our research in terms of optimization and learning-based control methods forms the ideal basis to develop sustainable solutions in energy-related applications.

Projects & Publications

Discrete Event Systems

How to generate a PLC-program that operates the plant? The laboratory model below represents a flexible manufacturing system. It consists of 29 interacting electro-mechanical components (conveyor belts, pusher, stack-feeder etc.), equipped with 25 actuators (DC-motors) and 57 sensors (key-switches, inductive sensors). The traditional engineering solution to operate the manufacturing system is to program a logic controller (PLC) such that it activates the appropriate motors in reaction on sensor events. This approach crucially relies on the programmer, who must consider any possible configuration of the system. While methods from software engineering assist the programmer and increase productivity, the process by principle remains error prone and unsafe.

Control Theory for Discrete Event Systems! The manufacturing system can be formally modelled as a discrete-event system (DES). In contrast to continuous states and continuous time used in physically motivated models, discrete-event systems are characterized by discrete and qualitative changes of (symbolic) state values caused by the occurrence of asynchronous discrete events. In the context of our example, the control theoretic perspective on this system class is of a particular interest: given the formal model of the manufacturing system (plant dynamics) and the desired behaviour (formal specification), how can one systematically derive the required PLC program (controller dynamics) that makes ends meet?

Supervisory control theory (SCT) is a framework that provides an answer to the above question, first proposed by P.J. Ramadge and W.M. Wonham in the late 1980s. Since then, many researchers have contributed, including our group, with a particular focus on hierarchical, decentralized and/or modular control system architectures. At the time of writing, the required controller dynamics for systems of the complexity as our laboratory model can be synthesised easily by methods from supervisory control theory. Ongoing projects address the integration of synthesis algorithms with the work-flow of PLC programming, open questions related to fault detection and diagnosis, as well as network implementations of distributed supervision.

Further Directions: The  research group FGDES provides the software tools  libFAUDES, DESTool, CompileDES and FlexFact via the FGDES homepage.

Selected publications

A more comprehensive list is given on the FGDES homepage.