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.
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.
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Related projects since 2021
Funding source: Industrie
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Funding source: DFG / Schwerpunktprogramm (SPP)
Project leader: , ,
Prof. Dr.-Ing. Knut Graichen
Chair holder
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.
Funding source: Industrie
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Funding source: Industrie
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Project leader:
Dr.-Ing. Andreas Völz
Senior Lecturer
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…
Funding source: Industrie
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Funding source: Deutsche Forschungsgemeinschaft (DFG)
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Funding source: Industrie
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Funding source: Bayerische Forschungsstiftung
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Funding source: Industrie
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Funding source: Industrie
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Funding source: Bundesministerium für Wirtschaft und Energie (BMWE)
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
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.
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
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.
Funding source: Industrie
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Funding source: Industrie
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Funding source: Industrie
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Funding source: Industrie
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Funding source: Industrie
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Funding source: Industrie
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Funding source: Industrie
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
Model Predictive Control (MPC) is a widely used strategy for controlling linear and nonlinear systems. It is based on the iterative solution of a dynamic optimization problem over a receding horizon. For networked and coupled systems, distributed MPC (DMPC) is an attractive extension of MPC, where the central MPC controller is replaced by local MPC agents for the individual subsystems of the global system. The probably most popular DMPC method is ADMM (Alternating Direction Method of Multipliers) that is based on the dual decomposition of the distributed problem.
A promising alternative approach, which has not yet been fully explored in the literature, is sensitivity-based primal decomposition. In this approach, the individual agents explicitly consider the costs of their actions on the neighbors’ performance. These sensitivities can be locally computed in an efficient mannery. Compared to ADMM, sensitivity-based DMPC shows an improved convergence behavior, reduced communication overhead, and lower algorithmic complexity. The efficient local computation of sensitivities and a simpler convergence analysis are further advantages of this method. Despite these advantages, the sensitivity-based approach currently has several shortcomings compared to ADMM. In particular, convergence and stability can only be guaranteed for a maximum prediction horizon and general state couplings are more difficult to consider with primal decomposition.
Therefore, this project aims to conduct an in-depth investigation of the sensitivity-based approach for distributed model predictive control. In particular, the aforementioned shortcomings compared to ADMM shall be addressed, and the overarching topic of sensitivities can be used to increase efficiency and flexibility in numerical solutions, to simplify the methodological analysis, and to enable practical implementation. The findings will be published in the DMPC toolbox GRAMPC-D.
Funding source: Industrie
Project leader:
Prof. Dr.-Ing. Knut Graichen
Chair holder
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Local predictive optimization of globally planned motions for truck-trailer systems
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Time-Optimal Path Parameterization for Cooperative Multi-Arm Robotic Systems with Third-Order Constraints
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2024 (Abu Dhabi, 14. October 2024 - 18. October 2024)
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Sensitivity-Based Distributed Model Predictive Control: Synchronous and Asynchronous Execution Compared to ADMM
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Dynamic compensation of the threading speed drop in rolling processes
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GP-based modeling for PSD control of emulsification processes
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22nd IFAC World Congress (Yokohama, Japan, 9. July 2023 - 14. July 2023)
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A Numerical Approach for Solving the Inversion Problem for n-Trailer Systems
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DOI: 10.23919/ACC53348.2022.9867593 - , , , :
Trajectory optimization for truck-trailer systems based on predictive path-following control
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Catching objects with a robot arm using model predictive control
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Comparison of sensitivity-based and ADMM-based DMPC applied to building automation
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Semi-infinite programming using Gaussian process regression for robust design optimization
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Towards asynchronous ADMM for distributed model predictive control of nonlinear systems
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Model predictive interaction control for force closure grasping
2021 IEEE Conference on Decision and Control (CDC) (Austin, TX, USA, 14. December 2021 - 17. December 2021)
DOI: 10.1109/cdc45484.2021.9683405 - , :
A sensitivity-based distributed model predictive control algorithm for nonlinear continuous-time systems
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Learning-based driver prediction for MPC-based motion cueing algorithms
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Model predictive path-following control for general n-trailer systems with an arbitrary guidance point
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DOI: 10.23919/ecc54610.2021.9654870 - , :
Gradient-based nonlinear model predictive control for systems with state-dependent mass matrix
2021 IEEE Conference on Decision and Control (CDC), accepted
DOI: 10.1109/cdc45484.2021.9683175
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Distributed optimization with ALADIN for non-convex optimal control problems
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Neighbor approximations for distributed optimal control of nonlinear networked systems
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Nonlinear model predictive torque control and setpoint computation of induction machines for high performance applications
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DOI: 10.1016/j.conengprac.2020.104415 - , , , , :
Towards a Generic Manipulation Framework for Robots based on Model Predictive Interaction Control
IEEE International Conference on Mechatronics and Automation (ICMA) (Beijing, 13. October 2020 - 16. October 2020)
DOI: 10.1109/icma49215.2020.9233628 - , , :
Model Predictive Interaction Control for Industrial Robots
21st IFAC World Congress (Berlin, 12. July 2020 - 17. July 2020)
DOI: 10.1016/j.ifacol.2020.12.2696 - , , :
Model Predictive Position and Force Trajectory Tracking Control for Robot-Environment Interaction
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (Las Vegas, NV, USA, 25. October 2020 - 29. October 2020)
DOI: 10.1109/iros45743.2020.9341168 - , :
Moving horizon estimation for continuous glucose monitoring
7th International Conference on Biomedical Engineering and Systems (ICBES 20)
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Optimization of direct winding processes based on a holistic control approach
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DOI: 10.1109/edpc51184.2020.9388184 - , , , :
Model predictive control for agricultural machines with implements
In: Proceedings 28th Mediterranean Conference on Control and Automation (MED) 2020
DOI: 10.1109/med48518.2020.9183272
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Distributed model predictive control for continuous-time nonlinear systems based on suboptimal ADMM
In: Optimal Control Applications & Methods 40 (2019), p. 1-23
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DOI: 10.1002/oca.2459 - , , :
Towards a modular framework for distributed model predictive control of nonlinear neighbor-affine systems
In: 58th IEEE Conference on Decision and Control (CDC 2019) 2019
DOI: 10.1109/cdc40024.2019.9029800 - , , , , :
A software framework for embedded nonlinear model predictive control using a gradient-based augmented Lagrangian approach (GRAMPC)
In: Optimization and Engineering 20 (2019), p. 769-809
ISSN: 1389-4420
DOI: 10.1007/s11081-018-9417-2 - , , :
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), Vienna (Austria): 2019
DOI: 10.1016/j.ifacol.2019.11.700 - , , , :
Comparison between a Filter- and an MPC-based MCA in an Offline Simulator Study
Driving Simulation Conference & Exhibition (DSC) (Strasbourg, 4. September 2019 - 6. September 2019) - , , , :
Optimal control based reference generation for model predictive motion cueing algorithms
In: 3rd IEEE Conference on Control Technology and Application (CCTA 2019), Hong Kong (China): 2019
DOI: 10.1109/ccta.2019.8920421
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A fixed-point iteration scheme for model predictive torque control of PMSMs
In: Proceedings 6th IFAC Conference on Nonlinear Model Predictive Control (NMPC), Madison, WI (USA): 2018 - , , , :
A real-time nonlinear air path observer for off-highway diesel engines
In: Proceedings 2018 European Control Conference (ECC), Limassol (Cyprus): 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, Madrid (Spain): 2018 - , :
Constrained trajectory planning and actuator design for electromagnetic heating systems
In: Control Engineering Practice 74 (2018), p. 191-203
ISSN: 0967-0661
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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), Hamburg (Germany): 2015
- , :
Experimental results for distributed model predictive control applied to a water distribution system
In: Proceedings 2014 IEEE Multi-Conference on Systems and Control, Nice (France): 2014 - , :
PLC implementation of a nonlinear model predictive controller
In: Proceedings 19th IFAC World Congress, Cape Town (South Africa): 2014 - , :
Nichtlineare modellprädiktive Regelung auf SPS
In: Automatisierungstechnische Praxis 56 (2014), p. 38-46
ISSN: 0178-2320
DOI: 10.17560/atp.v56i03.460 - , :
The gradient based nonlinear model predictive control software GRAMPC
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A synthesis strategy for nonlinear model predictive controller on FPGA
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