Prof. Gregory Provan

Cork Complex Systems LabComputer Science Department, University College Cork,

Cork, Ireland.

Optimised Embedded Distributed Optimised Embedded Distributed

Controller forController for

Automated Lighting SystemsAutomated Lighting SystemsAlie El-Din Mady, Menouer Boubekeur and Gregory Provan

Outline…

� System of Systems (SoS)

� Integrated Modelling for Building Management System s

(BMS)

� Lighting Scenario

� Parameterizable/Predictable Distributed (PPD)

Controller

� Simulation Results

� Design Improvements

� Summary and Future Trends

page 2page 2

Outline…

� System of Systems (SoS)

� Integrated Modelling for Building Management System s

(BMS)

� Lighting Scenario

� Parameterizable/Predictable Distributed (PPD)

Controller

� Simulation Results

� Design Improvements

� Summary and Future Trends

page 3page 3

System of Systems (SoS)

page 4page 4

System

System System

Emergent PropertiesEmergent Properties

Emergent Properties

System of System Interactions

SoS Environment

Emergent Properties

SystemElement

SystemElement

SystemElement

System Elements and

Interactions

The need to maintain autonomy while at the same time operating within the SoS context greatly increases the complexity of an SoS and is at the heart of the SoS architecture challenge

Outline…

� System of Systems (SoS)

� Integrated Modelling for Building Management System s

(BMS)

� Lighting Scenario

� Parameterizable/Predictable Distributed (PPD)

Controller

� Simulation Results

� Design Improvements

� Summary and Future Trends

page 5page 5

Modelling Methodology

page 6page 6

Code Generation,

Model Transformation

Charon Toolset,ModelicaSysML

Charon Simulator, PHAVer, UPPAAL, Lydia, …

ACCESS 24/7 Monitoring

FIRE

ENERGY HVAC

LIFTSSECURITY

LIGHTING

Integrated Control Environment

Middleware

Interfaces

� Common System–Level Information Model

� Data sharing

� Integration Mechanism

� Middleware� Benefits

� Sub-systems are integrated, yet

� Retain their own� standards � protocols� networks� Separate control

environments

Model Integration for BMS

No such system exists todayNo such system exists today

page 7page 7

What to Model ?

page 8page 8

Example: Building Management System (BMS)

Model-Transformation

Target Model(Embedded Java Code)

Embedded Control

Methodology for Automatic Control Code Generation

page 9page 9

Extract Control Blocks

Global building modelling

Inject Nominal BehaviourEventually Inject Faulty Behaviour

BIM / IFC

SysML Structure

SysML Behaviour

(e.g. FSM, AD, …)

DSML behaviour(Timed Hybrid System)

Modelling

TransformationTransformation

Generic Meta-model

Target Meta-model

(Embedded Java)

conforms to

conforms to conforms to

conforms to conforms to

Source Meta-model

(HS Language)

BNF Spec.

Trans. Rules

SunSPOT

Libraries

Emulation

Sun SPOT Network

Concurrent

DSML Processes

Env. Modeling

DSML behaviour(Hybrid System)

Simulation

Simulation

Engine

Parameter Estimation (e.g. Simulation)

Methodology

page 10page 10

Analysis and Optimisation

Requirement Specification

Wireless/Wired Sensor and Actuator network

Physical Plant

Integrated Simulation Platform

page 11page 11

Hybrid/discrete

Automata modelling

Preferences

Environment

Modeling(Sensors, Agents,

… )

Simulation

Results

Simulation

Engine

page 12page 12

Hierarchical Modelling Using Charon

Blinding

Light

Person

Top Level Design “Agents”

Behaviour Description

Finite State Machine “Mode”

Automatic

AutomaticWith

Margin Control

Manual Control

Default

Outline…

� System of Systems (SoS)

� Integrated Modelling for Building Management System s

(BMS)

� Lighting Scenario

� Parameterizable/Predictable Distributed (PPD)

Controller

� Simulation Results

� Design Improvements

� Summary and Future Trends

page 13page 13

UseUse--case Diagram : Requirements specificationcase Diagram : Requirements specification

System Description and Requirement Specification

page 14page 14

Model Specifications

page 15page 15

One open area, 10 controlled zones.

Outline…

� System of Systems (SoS)

� Integrated Modelling for Building Management System s

(BMS)

� Lighting Scenario

� Parameterizable/Predictable Distributed (PPD)

Controller

� Simulation Results

� Design Improvements

� Summary and Future Trends

page 16page 16

page 17page 17

Parameterizable/Predictable Distributed Controller

Parameterization for local controllers (e.g. consider blinding, the occupancy priority).

•Local Optimization •Predictions for external light, light interferences and next actuation settings.

Distributed Optimisation Techniques

� Luminance Boundaries:

In order to distribute the energy consumption over all the controlled zones, luminance boundaries have been set to limit the user's preferences of exceeding 700 Lux. This will also limit the interferences between zones.

page 18page 18

� Tuning Process:If the sensed value is more than 70 Lux different than the optimal one, the actuated light is decreased by one dimming level (70 Lux) instead of the exact Lux difference. This will diminish the interferences and then speed up the stabilization process.

� Scheduling: Identify the independent zones to be actuated concurrently.

� Expected Interference : To avoid this initial instability, an expected interference parameter is introduced using a Linear Prediction Coding (LPC) algorithm.

Before

After

Environment Modeling

ControllerPresence Daylight

Outline…

� System of Systems (SoS)

� Integrated Modelling for Building Management System s

(BMS)

� Lighting Scenario

� Parameterizable/Predictable Distributed (PPD)

Controller

� Simulation Results

� Design Improvements

� Summary and Future Trends

page 20page 20

Simulation Results (Single Zone)

page 21page 21

Preferences =560 Lux, 50% Blinding

Light Sensor Sampling Rate Internal Light/Actuation

External LightBlinding Actuation

Simulation Results (Multiple Zones)

page 22page 22

Outline…

� System of Systems (SoS)

� Integrated Modelling for Building Management System s

(BMS)

� Lighting Scenario

� Parameterizable/Predictable Distributed (PPD)

Controller

� Simulation Results

� Design Improvements

� Summary and Future Trends

page 23page 23

PPD Energy Usage Improvement

page 24page 24

PPD improves ~ 30% comparing to Presence detection strategy~ 9% comparing to PI Controller

PPD WSAN Improvement

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WSAN QoS Improvement

Packet Loss ~ 53-100%

Buffer Size ~ 98%

Controller Duty Cycle ~ 34-65%

Response Time ~ 98-99%

Channel Throughput ~ 82-91%

QoS to the WSAN for the PPD strategy versus a cent ralised strategy

Centralised Strategy PPD Strategy

Outline…

� System of Systems (SoS)

� Integrated Modelling for Building Management System s

(BMS)

� Lighting Scenario

� Parameterizable/Predictable Distributed (PPD)

Controller

� Simulation Results

� Design Improvements

� Summary and Future Trends

page 26page 26

Summary and Future Trends

� This article has described PPD-Controller for light ing.

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PPPP

Predictability is guaranteed through the prediction of light actuation (PI-Controller), interferences (LPC technique) and the external light (GASA engine).

Parameterization has been implemented through a global controller capable of parameter-reconfiguration of the local controllers.

� We intend to implement a demonstration of the developed system in the Environmental Research Institute (ERI) building, which is the ITOBO 'Living Laboratory‘.

� Next releases of the controller will integrate fau lt-tolerant control reconfiguration, allowing the controller to operate when faults occur.

Thank you