Boiroux, Dimitri

Capolei, Andrea

Duun-Henriksen, Anne Katrine

Frison, Gianluca

Gade-Nielsen, Nicolai Fog

Halvgaard, Rasmus

Hovgaard, Tobias Gybel

Jørgensen, John Bagterp

Schmidt, Signe

Skajaa, Anders

Sokoler, Leo Emil

Standardi, Laura

Völcker, Carsten

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 20, 2017Computational Methods for Model Predictive ControlNew Opportunities for Computational ScientistsJørgensen, John Bagterp; Boiroux, Dimitri; Hovgaard, Tobias Gybel; Halvgaard, Rasmus Fogtmann;Skajaa, Anders; Gade-Nielsen, Nicolai Fog; Standardi, Laura; Sokoler, Leo Emil; Völcker, Carsten;Capolei, Andrea; Frison, Gianluca; Schmidt, Signe; Duun-Henriksen, Anne KatrinePublication date:2012Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Jørgensen, J. B., Boiroux, D., Hovgaard, T. G., Halvgaard, R., Skajaa, A., Gade-Nielsen, N. F., ... Duun-Henriksen, A. K. (2012). Computational Methods for Model Predictive Control: New Opportunities forComputational Scientists Technical University of Denmark (DTU). [Sound/Visual production (digital)]. Workshopon the State-of-the-Art in Scientific and Parallel Computing (PARA 2012), Helsinki, Finland, 10/06/2012Computational Methods for Model Predictive Control New Opportunities for Computational Scientists  John Bagterp Jørgensen Dimitri Boiroux, Tobias Gybel Hovgaard, Rasmus Halvgaard, Anders Skajaa, Nicolai Fog Gade-Nielsen, Laura Standardi,  Leo Emil Sokoler, Carsten Völcker, Andrea Capolei,  Gianluca Frison, Signe Schmidt, Anne-Katrine Dunn Henriksen Technical University of Denmark PARA 2012, Finlandia Hall, Helsinki, Finland June 10-13, 2012 Introduction • Model Predictive Control – Optimal Experimental Design – Parameter Estimation – State Estimation – Regulation • Constrained Optimization of Dynamical Systems • Case Studies – The Artificial Pancreas – Closed-Loop Oil Reservoir Management – Smart Energy Systems  2 MODEL PREDICTIVE CONTROL 3 Systematic Model Building 4 Model Predictive Controller 5 Regulator Estimator System Sensors Targets Inputs Model Predictive Controller Outputs Measure-ments State-/parameter-estimate Model Predictive Control MPC = Estimation + Regulation    Moving horizon implementation 6 Filtering and Prediction - EKF Filtering Prediction 7 Optimal Control Problem Integrator (Runge-Kutta Methods) • DOPRI54 (non-stiff systems) • ESDIRK12 / ESDIRK23 / ESDIRK34 (stiff systems) Sensitivities • Forward, Adjoint Optimization   • SQP • Single-shooting • Multiple-shooting 9    Insulin Administration Systems  for  People with Diabetes   The Artificial Pancreas 10 Blood glucose control The blood glucose must stay within certain upper and lower bounds! –   Too low: coma (immediate effect) –   Too high: blindness and other long-term effects 11 Insulin Administration Systems Pen or Pump dosing         Continuous blood glucose monitor       Normal blood glucose level (90 mg/dL) Control Algorithm + - 12 Systems Diagram ManBloodGlucoseInsulin infusionrateDiffusion ofinsulin intissueDiffusion ofglucose intissueModelPredictiveControllerSubcutaneous GlucosePhysical activity /exerciseInsulininjectionPhysiological SystemGlucoseSensorInsulinPumpMealArtificial PancreasGlucoseMeterBloodGlucoseSetpoint13 Physiological Models – The Virtual Patient 14 Physiological Models  – A Compartment Model 15 The Cobelli Model – and the Virtual Clinic • Developed by  – University of Virginia – University of Padova – Juvenile Diabetes Research Foundation • 30 virtual patients in the version that we have a license to • The full version including 300 patients approved to substitute animal tests by FDA 16 Clinical Trials • Gastric emptying trials (left) • Clinical trials collecting data for modeling (right) 17 Example of a Clinical Experiment 8:00 Insert lines 12:30 Exercise 15:00 Snack 10:00 Meal/ bolus 18 Identification Results • Representative model fit  • Very accurate model fit     • Note:   Model is fit to the CGM signal. The intravenous glucose concentration is also shown (thick line) for comparison 19 The DTU Type 1 Diabetes Model 20 Insulin Administration Strategies NMPC Pre-meal Insulin Allowed NMPC No Pre-meal Insulin MDI (Pen Based) Insulin Treatment 21 Closed-Loop Studies by NMPC Meals not announced Meals announced at meal-time 22 Overnight Stabilization – A Cohort 23 Clinical Closed Loop Studies 24 MODEL PREDICTIVE CONTROL FOR MANAGEMENT OF OIL RESERVOIRS 25 An Offshore Oil Reservoir 26 Mathematical Model 27 Closed-Loop Reservoir Management 28 t x Experimental setup Seismic data Applied Seismology: Subsurface Imaging  29 The Computational Challenge: Integrated Inversion of Seismic and Production Data Seismic inversion History matching Seismic data Oil production data 30 History Matching (State and Parameter Est) + Seismic Data       => Permeability Field 31 Optimization Problem •  Single-Shooting and SQP •  ESDIRK methods tailored for this problem •  Gradients computed by the adjoint method 32  Objective Function is the Net Present Value 33 Optimal Oil Field Development - MVs 34 Optimal Oil Field Development - CVs 35 Oil Field Development Using NMPC 36 MODEL PREDICTIVE CONTROL OF SMART ENERGY SYSTEMS 37 Smart Energy Systems 38 Wind Power 0%20%40%60%80%100%120%01-2007 03-2007 05-2007 07-2007 09-2007 11-2007 01-2008Wind power / consumption DK-WWind power share in the western part of Denmark: 24 % 39 Wind Power Production and Imbalances 0%20%40%60%80%100%120%0 5 10 15 20 25 30 35Wind speed [m/s]Production in % of total installed capacity % of installed capacity1 m/s change in wind speed changes the production by 350 MW  (DK West) 40 Power System Development – DK West Centralized system of the mid 1980s Primary  production plants Local plants Wind turbines More decentralized system of today 41 Load Balancing Controller 42 Models of Energy Components 43 Controllable Power Generators 46 MPC for Economic Power Portfolio Optimization 47 Soft Economic MPC 48 Simple Test Example 49 Optimal Production Profiles 50 Cooling Houses – A Flexible Consumer • Motivation: • Refrigeration and air-conditioning consume  substantial amounts of energy. • E.g. up to 80% of energy consumed by supermarkets goes to refrigeration.  • Methods: • Economic MPC:  Minimize the cost of cooling subject to temperature limits • Predictions of weather, energy prices and load profiles. • Implementation on industrial hardware.  EvaporatorCompressorExpansionvalveCondenserCold RoomOutdoorsurroundingsSuction Pressure controllerCondenser Pressure ControllerNEFNCFNCTcrPCPEQloadPc,ref=PdewT(Ta+DT) Stored goodsMPCCooling system used in refrigerators and cooling houses FPGA 52 Power Management  2 Power Plants and 1 Cooling House • Economic Optimizing MPC • Minimize power consumption and costs without lowering the cooling quality • Load shifting utilizing the thermal capacity of the cooling house 53 Online Computing Time  Tailored Primal-Dual Interior Point Algorithm 54 Warm Start Interior Point Methods 55 Accelerating LP Algorithms with GPUs 56 Implementation Details 57 Speed-Up with GPU 58 Dantzig-Wolfe Decomposition 59 Uncertainty 60 Uncertainty Scenarios 61 The Extended LQ Problem 62 KKT System for the  Extended LQ Problem 63 KKT System for the  Extended LQ Problem 64 Numerical Results 65 1 1.5 2 2.5-3-2.5-2-1.5-1-0.500.51C code, N=100log10(nx)log 10(wall clock time)  Riccati, nu=1Riccati, nu=2Riccati, nu=5Condensing, nu=1Condensing, nu=2Condensing, nu=5MA57, nu=1MA57, nu=2MA57, nu=5Numerical Results 66 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-3-2.5-2-1.5-1-0.5C code, nx=50, nu=5log10(N)log 10(CPU time)  RiccatiCondensingMA57Summary • There is a need for HPC software supporting MPC – QP, LP, SOCP algorithms – ODE/PDE solvers equipped with sensitivity computing abilities (adjoints) – SQP / NLP algorithms – Efficient direct sparse and iterative methods for KKT systems (even when they are ill-conditioned) • The number of potential applications is very large 67 Center for Energy Ressources Engineering (CERE) Consortium www.cere.dtu.dk  Novo Nordisk A/S, Hvidovre Hospital, Medtronic Danfoss A/S, DONG Energy A/S, Vestas A/S, GEA Process Engineering A/S The Danish Strategic Research Council The Danish Research Council for Production and Technology Sciences  68 

Computational Methods for Model Predictive Control:New Opportunities for Computational Scientists

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Computational Methods for Model Predictive Control:New Opportunities for Computational Scientists

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