Index

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.


Contact

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



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.

Multi-zone layout of a demonstrator building for HVAC control at the Bosch Research Campus Renningen (Source: Bosch)
Modeling and control of LOHC reactors (Source: Julian Kadar, HI-ERN)
Modular multilevel converter for HVDC (© Siemens Energy, 2023)

Related projects

AGENT-2: Predictive and learning control methods

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…

More information

Robust control of modular multi-level converters

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

The consortium of the project AGENT consists of the partners RWTH Aachen, Friedrich-Alexander- Universität Erlangen-Nürnberg and Robert Bosch GmbH, who already conduct research in the field of building energy system technology and building automation technology. The practitioners’ perspective shows that increasing complexity of energy systems within non-residential buildings leads to further challenges within their operation. These are caused by complex interaction and the attempt to control such systems by means of a central instance using a supervisory control system. The goal in this project is the development of a future building automation system based on agents. A building with its energy system will be enabled to optimize not only its own operation but to serve as a part of a superordinate energy system and to behave as a grid-friendly building. The agent-based system will be self-configuring and, for instance, optimize the energy consumption of the building. Therewith, it reduces the effort of construction, commissioning and operation of energy and building energy systems. For the task of controlling energy systems, generic tasks of single components and groups of components will be defined and included into a practitioner’s guide. Practical usability will be ensured to allow for dissemination in the building energy sector.

More information

Robust energy-based control of MMC/HVDV systems

Thermische Umrichtermodellierung für elektrische Antriebssysteme


Related publications

Since 2021

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.


Contact

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



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.

Embedded learning of combustion models
Stochastic MPC considers obstacle uncertainty for collision avoidance

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.

Reinforcement learning for a hydraulic clutch
Bayesian optimization subject to an unknown equality constraint

Related projects

AGENT-2: Predictive and learning control methods

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…

More information

AUTOtech.agil: Robust Planning and Control using Probabilistic Methods

Anomaly detection and intelligent recalibration of sensorized systems

KI-unterstützte Modellierung zur Steigerung der Regelgüte

Kinesthetic teaching and predictive control of interaction tasks in robotics

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…

More information

Thermische Umrichtermodellierung für elektrische Antriebssysteme

Robust Reinforcement Learning for Thermal Management Control


Related publications

Since 2021

2020

2019 and earlier

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.

MPC: Moving horizon optimization
Embedded NMPC implementation
Augmented reality optimization sandbox

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.

Leader-follower control for mobile robots
Graph topology of networked system
XPlanar cooperative transport

 

Videos

Related projects

Feasibility of stochastic model predictive control for autonomous driving

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

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

Control of ring resonator modulators in optical communication

Cooperative manipulation with dual-arm robots at the payload limit

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

Anomaly detection and intelligent recalibration of sensorized systems

Distributed model predictive control of nonlinear systems with asynchronous communication

KI-unterstützte Modellierung zur Steigerung der Regelgüte

Robust control of modular multi-level converters

Innovative Regelungs- und Steuerungsstrategien für Druckerhöhungsanlagen - TP Erlangen

Motion planning for driving simulators

Automated path planning for truck-trailer configurations

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

The consortium of the project AGENT consists of the partners RWTH Aachen, Friedrich-Alexander- Universität Erlangen-Nürnberg and Robert Bosch GmbH, who already conduct research in the field of building energy system technology and building automation technology. The practitioners’ perspective shows that increasing complexity of energy systems within non-residential buildings leads to further challenges within their operation. These are caused by complex interaction and the attempt to control such systems by means of a central instance using a supervisory control system. The goal in this project is the development of a future building automation system based on agents. A building with its energy system will be enabled to optimize not only its own operation but to serve as a part of a superordinate energy system and to behave as a grid-friendly building. The agent-based system will be self-configuring and, for instance, optimize the energy consumption of the building. Therewith, it reduces the effort of construction, commissioning and operation of energy and building energy systems. For the task of controlling energy systems, generic tasks of single components and groups of components will be defined and included into a practitioner’s guide. Practical usability will be ensured to allow for dissemination in the building energy sector.

More information

Modular distributed model predictive control of nonlinear systems with neighborhood models

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

Trajectory planning for off-road applications

Compliance for a robotic assistance system

Regelung des Antriebsstrangs beim Kaltwalzen

Robust energy-based control of MMC/HVDV systems

Model predictive flight control


Related publications

Since 2022

2021

2020

2019

2018 and earlier

Mechatronics & Automotive

Mechatronic systems consist of both mechanical and electrical components. Typical challenges are the high integration of these systems along with the limited computational resources of embedded hardware such as electronic control units (ECUs) in automotive applications. We have a successful history of bridging the gap between theory and practice in close cooperation with industrial partners from the mechatronics and automotive domain.


Contact

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

Dr.-Ing. Andreas Michalka
Tel.: +49 9131 85-28592
E-Mail | Homepage



Highly integrated mechatronic systems can, for instance, be found in electric drives, power trains, and sensors. Future mechatronic applications will not only involve classical control loops, but will also be equipped with further intelligent and autonomous functionalities such as automatic calibration, fault-tolerant operation and predictive maintenance.

Automotive-related applications such as passenger cars, trucks or agricultural machines involve a multitude of control systems. This ranges from active suspension, traction control, assisted driving up to fully autonomous driving. On the lower level, efficient embedded control implementations are predominant, whereas higher level control loops involve complex decision making in combination with environmental perception.

State-of-the-art modeling approaches of mechatronic systems combine physics-based and data-driven design methods to account for the uncertainties introduced, for example, by wear, aging and serial production spread. A particular expertise of the Chair is to leverage these hybrid models for sophisticated control schemes and the development of tailored algorithms for real-time capable implementations.

Model-scale truck with trailer
Strain wave gear with integrated torque sensor (Source: Schaeffler)
Drive train control for steel rolling (Source: Primetals)
FEM simulation of the temperature distribution of a 3-phase inverter in an electric vehicle

Videos

Related projects

AUTOtech.agil: Robust Planning and Control using Probabilistic Methods

Innovative Regelungs- und Steuerungsstrategien für Druckerhöhungsanlagen - TP Erlangen

Thermische Umrichtermodellierung für elektrische Antriebssysteme

Automated path planning for truck-trailer configurations

Trajectory planning for off-road applications

Regelung des Antriebsstrangs beim Kaltwalzen

Robust Reinforcement Learning for Thermal Management Control


Related publications

Since 2022

2021

      2020

      2019

      2018 and earlier

      Robotics

      Robotics deals in general with machines that can assist in or perform the execution of tasks such as assembly or machining 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.


      Contact

      Dr.-Ing. Andreas Völz
      Tel.: +49 9131 85-61036
      E-Mail | Homepage

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



      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.

      Cooperative dual-arm manipulation
      Robot-environment interaction control
      XPlanar system and Allegro hand

      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.

      Boston Dynamics Spot
      Dynamic obstacle detection
      Collision-free motion planning

      Videos

      Related projects

      Compliance for a robotic assistance system

      Cooperative manipulation with dual-arm robots at the payload limit

      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

      Kinesthetic teaching and predictive control of interaction tasks in robotics

      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…

      More information

      Motion planning for driving simulators


      Related publications

      Since 2022

      2021

      2020

          2019

          2018 and earlier

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          Projects

          • Robust Reinforcement Learning for Thermal Management Control


            (Third Party Funds Single)
            Term: 1. August 2023 - 31. July 2024
            Funding source: Industrie
          • Model predictive flight control


            (Third Party Funds Single)
            Term: 1. August 2023 - 31. July 2026
            Funding source: Industrie
          • Robust energy-based control of MMC/HVDV systems


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

            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.

          • 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)

            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.

          • 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)
          • 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)

            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.

          • Feasibility of stochastic model predictive control for autonomous driving


            (Third Party Funds Single)
            Term: 1. August 2022 - 31. December 2022
            Funding source: Industrie
          • Control of ring resonator modulators in optical communication


            (Third Party Funds Single)
            Term: 1. February 2022 - 31. January 2025
            Funding source: Industrie
          • 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)
          • Anomaly detection and intelligent recalibration of sensorized systems


            (Third Party Funds Single)
            Term: 1. November 2021 - 31. October 2024
            Funding source: Industrie
          • KI-unterstützte Modellierung zur Steigerung der Regelgüte


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

            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.

          • 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
          • Robust control of modular multi-level converters


            (Third Party Funds Single)
            Term: 16. June 2021 - 31. December 2024
            Funding source: Industrie
          • Motion planning for driving simulators


            (Third Party Funds Single)
            Term: 1. January 2021 - 30. June 2021
            Funding source: Industrie
          • Thermische Umrichtermodellierung für elektrische Antriebssysteme


            (Third Party Funds Single)
            Term: 1. August 2020 - 31. July 2023
            Funding source: Industrie
          • Regelung des Antriebsstrangs beim Kaltwalzen


            (Third Party Funds Single)
            Term: 1. July 2020 - 31. August 2023
            Funding source: Industrie
          • Automated path planning for truck-trailer configurations


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

            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.

          • 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)
          • Formale Verifikation in der Fertigungsautomatisierung (Projektabschnitt B1)


            (Third Party Funds Single)
            Term: 15. April 2019 - 14. April 2020
            Funding source: Siemens AG
          • Trajectory planning for off-road applications


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

            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. 

          • Compliance for a robotic assistance system


            (Third Party Funds Single)
            Term: 1. April 2019 - 30. September 2022
            Funding source: Industrie
          • Formale Verifikation in der Fertigungsautomatisierung (Projektabschnitt A)


            (Third Party Funds Single)
            Term: 15. April 2018 - 14. April 2019
            Funding source: Siemens AG
          • Fehlerdiagnose verteilt-parametrischer Systeme mittels Modulationsfunktionen


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

            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.

          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


          The chair works on research projects in cooperation with industrial partners and public funding bodies such as the German Research Foundation (DFG). An overview of ongoing and completed projects can be found in this section.



          The Chair publishes papers in renowned international conferences and journals. A list of publications produced at the chair can be found here.


          Discrete Event Systems

          The research group FGDES develops methods for the analysis and sythnesis of discrete-event systems with a particular focus on modular and/or hierarchical architectures.  Here, discrete-event systems 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.


          Contact

          Prof. Dr.-Ing. Thomas Moor
          Tel.: +49 9131 85-27129
          E-Mail | FGDES Homepage


          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

          Since 2022

          2021

          2020

          2019

          2018 and earlier

          A more comprehensive list is given on the FGDES homepage.