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TRANSCRIPT
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Lecture 9
Pr inc ip les of Model ing for Cyber-Phys ica l Systems
Instructor: Madhur Behl
Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected] 1
Model Predictive Control
Slides adapted from: Mark Canon (U. of Oxford)Manfred Morari (ETH, UPenn)Alberto Bemporad (IMT Lucca)
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MPC: Mathematical formulation
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MPC: Mathematical formulation
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Receding horizon philosophy
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MPC: Mathematical formulation
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Receding horizon philosophy
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Energy Efficient Building Control
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Energy Efficient Building Control
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Energy Efficient Building Control
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Constraints
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Constrained Infinite Time Optimal Control
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Constrained Finite Time Optimal Control (CFTOC)
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On-line Receding Horizon Control
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On-line Receding Horizon Control
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Unconstrained problem
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Unconstrained problem
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Unconstrained problem
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Unconstrained problem
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Unconstrained problem
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Unconstrained problem
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Unconstrained problem
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Unconstrained problem
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Example
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Example
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Example
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Example
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Example
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MPC challenges
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Literature
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MPC for buildings
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MPC for buildings
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Disturbances
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Controlled variables
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MLE+
MLE+ Overview
2. The scientific computational capability of Matlab/Simulink:I.Matlab Toolboxes
II.Matlab Built-in Functions.
Matlab
1. High-Fidelity Physical models of the whole-building Energy Simulator EnergyPlus.
E+
BACnet
3. Control Synthesis - Building Control Deployment.
82Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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EnergyPlus Building Model 1 System Identification2
Control Deployment54 Simulation ResultsControl Design inMatlab
3
From Control/Scheduling Algorithmsto Synthesis and Deployment in Real Buildings
MLE+ Workflow
83Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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EnergyPlus Building Modelü Small office building with 3 zonesü Chicago weather file during winterü Model Predictive Control:
o Minimize the power consumption of the radiant heatero Maintain thermal comfort (22°C - 24°C)
Advanced Controls: Model Predictive Control (MPC)
84Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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Inputs to EnergyPlus
Outputs from EnergyPlus
Variable Configuration Screen1. Parses E+ File to abstract IN/OUT2. Selects Inputs/Outputs3. Writes Configuration File Automatically
Advanced Controls: Variable Configuration
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Inputs to EnergyPlus
Outputs from EnergyPlus
Configuration File1. .xml file contains Co-Simulation Exchange Variables2. Inputs to E+
• Power of radiant heating system3. Outputs from E+
• Room temperatures• Radiant heating system power
Advanced Controls: Input/Output Configuration
86Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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System Identification1. Random Signal Generator2. IDINPUT (Matlab Built-in
function)3. Package Data: IDDATA
(Matlab Built-in type)4. Import to System
Identification Toolbox (IDENT)
Advanced Controls: System Identification
87Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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System Identification (2)1. Estimate Model According
(IDENT)2. Inputs:
• Radiant Power• Outside Temp• Solar Radiation
3. Outputs:• Room Temp
Advanced Controls: System Identification (2)
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ü Use template script to specify controllerü Easily integrate with Matlab’s Model Predictive Control toolbox.ü MPC:
ü Prediction Horizon: 2ü Control Horizon: 9ü Minimize Total Power Consumption
Advanced Controls: Control Design
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Simulation Resultsü Displays and save results in Matlab
8 am 6 pm
Advanced Controls: Simulation Results
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Simulink Example: Co-Design & Controlsü 5 Zone Buildingü California Weather Fileü July 1st – 7th (Summer Time)ü VAV System
Integrated Modeling: MLE+ Simulink
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HVAC
E+ Building
Integrated Modelingü E+ Buildingü VAV model in Simulinkü Inputs to E+:
ü Heat Flowü Outputs from E+
ü Room Tempü Outdoor Temp
Integrated Modeling: Simulation Overview
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HVAC System• Temperature Setpoints according to
Schedule• Central HVAC Model• VAV Boxes
CentralHVAC
VAV BoxesOccupancy Setpoints
Integrated Modeling: HVAC System
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VAV Model1. Thermostat2. Reheat Coil
VAV Box
Integrated Modeling: VAV boxes
94Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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Thermostat
Reheat Coilü VAV Boxü Inputs:
ü Setpointsü Room Tempü Supply Air Temp
ü Outputs:ü Mass Flow Rate
ü Thermostat Room Levelü Heat/FreeCool/Mec
hanicalCoolü Reheat Coil
Integrated Modeling: VAV Box
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MLE+ is a featured third party tool recognized by DoE
96Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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Campus-Wide Simulation
Supply SideEPlus LoadProfilerobject
Demand SideEPlus TemperatureSource object
MLE+ S function block in Simulink
97Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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MLE+ Over 400+
users
98Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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How are building models obtained today ?
White Box
1/Ugw
1/Ugi
1/Ugo
Cgo
Cgi
Tgo
Tg
Tgi
Tz
Q.rad,e
1/Uei1/Uew1/Ueo
Ceo Cei
TaTeo Tei
Q.sol,eQ.solt/2
1/Uci
1/Ucw
1/UcoCci
Cci
Ta
Q.sol,c
Q.rad,c
TaQ.conv + Q.sens
1/Uii 1/Uiw 1/Uio
Cii Cio
Tci
Tco
1/Uwin Tii Tio
Q.solt/2
Q.rad,g
[ExternalWalls]
Ti
[Ceiling]
[Floor]
[InternalWalls]
[Windows]
Grey Box
Black Box
99Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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How are building models obtained today ?
White Box
Grey Box
Black Box
100Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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White-Box Modeling
Hire building modeling experts
Set parametersFloor by floorZone by zoneWall by wall
Layer by layerEquipment by
equipmentTransfer geometry
Floor PlanHVAC layout
Not always
available
Guess nominalparameter
values
First principles basedHigh fidelity
Building energy simulation
Model TuningData
Sensor retrofits
101Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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White-Box Modeling
Hire building modeling experts
Set parametersFloor by floorZone by zoneWall by wall
Layer by layerEquipment by
equipmentTransfer geometry
Floor PlanHVAC layout
Not always
available
Guess nominalparameter
values
First principles basedHigh fidelity
Building energy simulation
Model TuningData
Sensor retrofits
Cost and time prohibitive(sensor installation, commissioning & expertise)
Not suitable for model-based controlModel complexity and uncertainty, (1000’s parameters and states)
102Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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How are building models obtained today ?
White Box
1/Ugw
1/Ugi
1/Ugo
Cgo
Cgi
Tgo
Tg
Tgi
Tz
Q.rad,e
1/Uei1/Uew1/Ueo
Ceo Cei
TaTeo Tei
Q.sol,eQ.solt/2
1/Uci
1/Ucw
1/UcoCci
Cci
Ta
Q.sol,c
Q.rad,c
TaQ.conv + Q.sens
1/Uii 1/Uiw 1/Uio
Cii Cio
Tci
Tco
1/Uwin Tii Tio
Q.solt/2
Q.rad,g
[ExternalWalls]
Ti
[Ceiling]
[Floor]
[InternalWalls]
[Windows]
Grey Box
Black Box
103Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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Grey-Box [Inverse] Modeling
Hire building modeling experts
Set parametersFloor by floorZone by zoneWall by wall
Layer by layerEquipment by
equipmentTransfer geometry
Floor PlanHVAC layout
Not always
available
Guess nominalparameter
values
Parameter Estimation
DataSensor retrofits
Lumped Parameter‘RC’ model 104Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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Grey-Box Modeling
Hire building modeling experts
Set parametersFloor by floorZone by zoneWall by wall
Layer by layerEquipment by
equipmentTransfer geometry
Floor PlanHVAC layout
Not always
available
Guess nominalparameter
values
Parameter Estimation
DataSensor retrofits
Lumped Parameter‘RC’ model
Promising but still cost and time prohibitive(sensor installation, commissioning & expertise
Modeling uncertainty,Time-varying parameters)
Suitable for model-based control(10-100’s parameters and states)
105Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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Cost and Time prohibitive modeling
Project duration: May 2007 – March 2015
Phase 1: EnergyPlus model (white-box), RC model (grey box), MPC development and evaluation. [Only simulated studies]
Phase 2: Retrofitted building with sensors, commercial MPC software, demand response, peak reduction, uncertain models..
“..the biggest hurdle to mass adoption of intelligent building control is the cost and effort required to capture accurate dynamical models of
the buildings.”
Sturzenegger, D.; Gyalistras, D.; Morari, M.; Smith, R.S., "Model Predictive Climate Control of
a Swiss Office Building: Implementation, Results, and Cost-Benefit Analysis," Control Systems
Technology, IEEE Transactions on , vol.PP, no.99, pp.1,1, March 2015
106Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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How are building models obtained today ?
White Box
Grey Box
Black Box
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Black-Box Modeling
Data
Could beautomated
Feature engineering
Not well aligned with control synthesis
Coarse grained predictions
Non-physical parameters
108Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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Too many sub-systemsNon-linear interactions
Modeling using first principles is hard !
Each building design is different.Must be uniquely modeled
Long operational lifetimes~50-100 years
109Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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Energy Systems Modeling
Suitability for control
Mod
elin
g Di
fficu
lty
(cos
t)
UnsuitableSuitable
High
Lo
w
White Box
Grey Box
Black Box
110Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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Foundations of Data Predictive Control for CPS
mbCRT DPC-RT Ensemble-DPC
Single-step look ahead[with single reg. trees]
Finite receding horizon[with single reg. trees]
Finite receding horizon[with ensemble models]
111Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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Foundations of Data Predictive Control for CPS
mbCRT DPC-RT Ensemble-DPC
Single-step look ahead[with single reg. trees]
Finite receding horizon[with single reg. trees]
Finite receding horizon[with ensemble models]
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- ICCPS ‘16, BuildSys 15, CISBAT 15, Journal of Applied Energy- Best Paper Award (SRC TECHCON-IoT): ‘Sometimes, Money Does Grow on Trees’-Ph.D. Dissertation: Madhur Behl, UPenn(2016)
- ACM BuildSys 16 (Best Presentation Award )
- ACM Transactions of Cyber Physical Systems.
- American Control Conference 17 (Best Energy Systems Paper Award )
Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected]
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Energy CPS Module RecapReview of ODEs and dynamical systems.State-Space modeling and implementation
in MATLAB, LTI models.First principles – Generalized systems
theory.Heat transfer basics.HVAC systems and electricity markets
overview.Introduction to EnergyPlus.‘RC’ network based state-space thermal
modeling.
Nominal values of parameters from IDF file.Parameter estimation optimizationNon-linear least squares.Model evaluation and goodness of fit.Model sensitivity analysis and experiment
designModel predictive control basicsCodebase to learn a state-space model from
any data-set.
Principles of Modeling for CPS – Fall 2018 Madhur Behl [email protected] 113