Institute for Automation and Applied Informatics

Research

 

 

Our research ranges from tailored method design for specific applications to fundamental research on systems and control. We focus on the following topics:

 

Optimization-based and predictive control of energy systems

The reliable integration of renewable energy is a major challenge of the 21st century with cost, resource and energy efficiency being of upmost importance. Since energy systems are inherently dynamic and renewable generation is often volatile, the development of tailored reliable control methods is key to success. It is in this context where the Optimization and Control Group pursues research on optimization-based and predictive control of energy systems under explicit consideration of uncertainties and distributed optimization.

 

 Research

Recent publications

  • Appino, R.; Ordiano, A.; Mikut, R.; Faulwasser, T. & Hagenmeyer, V. On the Use of Probabilistic Forecasts in Scheduling of Renewable Energy Sources Coupled to Storages. Applied Energy, 2017. DOI: 10.1016/j.apenergy.2017.08.133.
  • Mühlpfordt, T.; Findeisen, R.; Hagenmeyer, V. & Faulwasser, T. Comments on Quantifying Truncation Errors for Polynomial Chaos Expansions. IEEE Control Systems Letters. In press.
  • Mühlpfordt, T.; Faulwasser, T.; Roald, L. & Hagenmeyer, V. Solving Optimal Power Flow with non-Gaussian Uncertainties via Polynomial Chaos Expansion. 56th IEEE Conference on Decision and Control, Melbourne, Australia, 12-15 December, 2017.
  • Appino, R.R.; Mühlpfordt, T. ; Faulwasser, T. & Hagenmeyer, V. On Solving Probabilistic Load Flow for Radial Systems using Polynomial Chaos. IEEE PES PowerTech Conference, Manchester, UK, June 18-22, 2017
  • Mühlpfordt, T.; Faulwasser, T. & Hagenmeyer, V. Solving Stochastic AC Power Flow via Polynomial Chaos Expansion. IEEE International Conference on Control Applications, Buenos Aires, Argentina, 2016 
  • Braun, P.; Faulwasser, T.; Grüne, L.; Kellett, C.; Weller, S. & Worthmann, K. Maximum grid disconnection time of a small scale neighbourhood electricity network. 22nd Int. Symposium on Mathematical Theory of Networks and Systems (MTNS), Minneapolis, USA, 2016
  • Hagenmeyer, V.; Cakmak, K.; Düpmeier, C.; Faulwasser, T.; Isele, J.; Keller, H.; Kohlhepp, P.; Kühnapfel, U.; Stucky, U. & Mikut, R. Information and Communication Technology in Energy Lab 2.0: Smart Energies System Simulation and Control Center with an Open-Street-Map-based Power Flow Simulation Example. Energy Technology, 2016, 4, 145-162
  • Wrang D.; Faulwasser, T.; Billeter, J.; Amstutz, V.; Vrubel, H.; Battistel, A.; Girault, H. & Bonvin, D. Modeling and Optimal Control of a Redox Flow Battery. Proc. Symposium for Fuel Cell and Battery Modeling and Experimental Validation (MODVAL 13), 2016

 

Turnpike and dissipativity properties and their use in economic nonlinear model predictive control

 

Model Predictive Control (MPC) “[…] is the only advanced control technique—that is, more advanced than standard PID control—to have had a significant and widespread impact on industrial process control” (Jan Maciejowski, Predictive control: with constraints Pearson Education Limited, 2002).


The Optimization and Control Group does research on economic and non-economic variants of model predictive control. In our fundamental system-theoretic research we explore turnpike and dissipativity properties of optimal control problems and their use of MPC with nonlinear process models and system constraints.

 

Turnpike

 Recent publications

  • Faulwasser, T.; Grüne, L. & Müller, M. Economic Nonlinear Model Predictive Control: Stability, Optimality and Performance. Foundations and Trends in Systems and Control. In press.
  • Faulwasser, T. & Bonvin, D. Exact Turnpike Properties and Economic NMPC. European Journal of Control , 2017, DOI: 10.1016/j.ejcon.2017.02.001
  • Faulwasser, T.; Korda, M.; Jones, C. & Bonvin, D. On the Relation between Turnpike and Dissipativity Properties in Optimal Control Problems. Automatica, 2017, 81, 297-304. DOI: 10.1016/j.automatica.2017.03.012
  • Faulwasser, T. & Bonvin, D. On the Design of Economic NMPC Based on an Exact Turnpike Property IFAC-PapersOnLine , 2015, 48, 525 - 530
  • Faulwasser, T. & Bonvin, D. On the Design of Economic NMPC based on Approximate Turnpike Properties 54th IEEE Conference on Decision and Control, 2015, 4964 - 4970
  • Faulwasser, T.; Korda, M.; Jones, C. & Bonvin, D. Turnpike and Dissipativity Properties in Dynamic Real-Time Optimization and Economic MPC Proc. of the 53rd IEEE Conference on Decision and Control, 2014, 2734-2739

 


Real-time optimization of process systems

Many process systems are subject to considerable modelling uncertainty and exogenous disturbances. Hence, common industrial practise splits the operation onto several layers, among whhich the real-time optimization (RTO) layer has the task to compute economic steady-state set-points. Together with colleagues from the Laboratoire d'Automatique of EPFL, the Optimization and Control Group is developing RTO schemes and testing them in proof-of-concept implementations.

 

Recent publications

  • Singhal, M.; Marchetti, A.; Faulwasser, T. & Bonvin, D. Improved Directional Derivatives for Modifier-Adaptation Schemes. 20th IFAC World Congress, Toulouse, France, 9-14 July, 2017.
  • Milosavljevic, P.; Schneider, R.; Faulwasser, T. & Bonvin, D. Distributed Modifier Adaptation using a Coordinator and Input-Output Data. 20th IFAC World Congress, Toulouse, France, 9-14 July, 2017.
  • Marchetti, A.; Faulwasser, T. & Bonvin, D. A Feasible-Side Globally Convergent Modifier-Adaptation Algorithm Journal of Process Control, 2017, 54, 38-46. DOI: 10.1016/j.jprocont.2017.02.01
  • Marchetti, A.; Francois, G.; Faulwasser, T. & Bonvin, D. Modifier Adaptation for Real-Time Optimization - Methods and Applications. Processes, 2016. DOI: 10.3390/pr4040055
  • Marchetti, A.; Singhal, M.; Faulwasser, T. & Bonvin, D. Gradient-based RTO Schemes with Guaranteed Feasibility in the Presence of Gradient Uncertainty. Computers & Chemical Engineering, 2016. DOi: 10.1016/j.compchemeng.2016.11.027

 

Control of mechatronic systems

Many mechatronic systems are subject to nonlinear dynamics and constraints on states and inputs. Optimization-based trajectory generation methods and tailored MPC applications allow real-time control of such systems with sampling rates in the order of a few milliseconds or below. The Optimization and Control Group has manifold experience on tailored control design and real-time feasible implementation of such methods for mechatronic and aeronautical systems. Successful application and case studies include robots, autonomous helicopters and synchrotron particle accelerators.
Some of the results obtained in close collaboration with our partners are documented in videos:

 

Recent publications

  • Faulwasser, T. & Findeisen, R. Nonlinear model predictive control for constrained output path following IEEE Trans. Automat. Contr., 2016, 61, 1-14
  • Faulwasser, T.; Weber, T.; Zometa, J. & Findeisen, R. Implementation of Constrained Nonlinear Model Predictive Path-Following Control for an Industrial Robot. IEEE Trans. Contr. Syst. Techn. 2017, 25, 1505-1511.
  • Dauer, J.; Faulwasser, T. & Lorenz, S. Run-to-Run Disturbance Rejection for Feedforward Path Following of an Adaptively Controlled Unmanned Helicopter Control Applications (CCA), IEEE Conference on, 2015, 1779-1785
  • Kopf, M.; Giesseler, H.; Varutti, P.; Faulwasser, T. & Findeisen, R. On the effect of enforcing stability in predictive control for gust load alleviation. 2015 American Control Conference (ACC), 2015
  • Faulwasser, T.; Lens, D. & Kellett, C. Predictive Control for Longitudinal Beam Dynamics in Heavy Ion Synchrotrons Proc. of 2014 IEEE Conference on Control Applications (CCA),2014