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Cons nsen ensus sus-based based co cooperat perative ive co cont ntrol rol ap approach roach ap applied lied to to ur urban an tr traffic affic ne netwo twork rk Antonio Artuedo, Ral M. del Toro, Rodolfo Haber

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SLIDE 1

3rd International Electronic Conference on Sensors and Applications

15–30 November 2016

Cons nsen ensus sus-based based co cooperat perative ive co cont ntrol rol ap approach roach ap applied lied to to ur urban an tr traffic affic ne netwo twork rk

Antonio Artuñedo, Raúl M. del Toro, Rodolfo Haber

{antonio.artunedo, raul.deltoro, rodolfo.haber}@car.upm-csic.es Centre for Automation and Robotics (UPM-CSIC)

www.car.upm-csic.es

November 2016 1 ECSA 2016

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SLIDE 2

Outline

  • Introduction
  • Proposed solution in a simulated environment
  • Modeling
  • Consensus-based cooperative control
  • Simulation (Open & closed loop)
  • Results & conclusions

November 2016 2 3rd International Electronic Conference on Sensors and Applications

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SLIDE 3

Introduction

  • Current smart cities research aims to the integration of urban

subsystems for the anticipation and control of daily situations and unexpected events in order to succeed under complex and potentially unstable conditions.

  • Overall performance of the city is determined by the dynamic

behavior of coupled physical subsystems which have different domains or timing aspects.

  • One of the main challenges is the necessary cooperation among

different entities such as vehicles or infrastructure systems and exploit the information available through networks of sensors deployed as infrastructures for smart cities.

November 2016 3 3rd International Electronic Conference on Sensors and Applications

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SLIDE 4

Introduction

  • The increasing number of sensors, actuators, communication

systems and low cost computation already deployed in cities, enable new applications that can go beyond specific systems and cover different urban systems and scenarios.

  • In this work an algorithm for cooperative control of urban

subsystems is applied in order to provide solutions for mobility related problems in cities.

November 2016 4 3rd International Electronic Conference on Sensors and Applications

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SLIDE 5

Cyber world Adaptive TLC network Physical environmet

TLC 1 TLC 2 TLC 3 TLC 4

Information service providing pollution data Mobility domain. Road traffic control subsystem Network of units for adaptive traffic lights control cycles Pollution subsystem

Pollution monitoring Other pollution sources

Communication network

Pollution

Traffic subsystem

ξ x Δu ε

Traffic

Intersections traffic state TLC cycle adaptation Air quality monitoring stations of the city

Proposed solution in a simulated environment

November 2016 5 3rd International Electronic Conference on Sensors and Applications

Scenario based on:

  • Emission control scheme

suggested by Andó et. al. [1] Goal:

  • Improve performance of

urban traffic networks, in specific regions of the city, based on air pollution information.

[1] B. Ando, S. Baglio, S. Graziani, E. Pecora, and N. Pitrone, ʺA predictive model for urban air pollution evaluationʺ, in Instrumentation and Measurement Technology Conference, 1997. IMTC/97. Proceedings. Sensing, Processing, Networking., IEEE, 1997, pp. 1056‐1059 vol.2..

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SLIDE 6

coupled DEVS: TLC_network

Traffic

Pollution monitoring TLC1 Other pollution sources TLC2 TLC3 TLC4

ξ x1 u1 ε4 u2 u3 u4 x2 x3 x4 ε3 ε2 ε1 Ev Eo

Modeling: DEVS (Discrete Event Systems Specification)

November 2016 6 3rd International Electronic Conference on Sensors and Applications

  • It enables specification
  • f basic components

and how they are connected together:

  • atomic models, input

ports, changing states,

  • utput ports, couplings.
  • Atomic models:
  • Traffic-light control unit

(TLC),

  • Pollution-monitoring

system

  • Traffic system (i.e. road

network, vehicles, traffic lights, etc.),

  • Other pollution sources
  • Coupled models: TLC

network

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SLIDE 7

Modeling: DEVS (Discrete Event Systems Specification)

November 2016 7 3rd International Electronic Conference on Sensors and Applications

  • It enables specification
  • f basic components

and how they are connected together:

  • atomic models, input

ports, changing states,

  • utput ports, couplings.
  • Atomic models:
  • Traffic-light control unit

(TLC),

  • Pollution-monitoring

system

  • Traffic system (i.e. road

network, vehicles, traffic lights, etc.),

  • Other pollution sources
  • Coupled models: TLC

network

coupled DEVS: TLC_network

Traffic

Pollution monitoring TLC1 Other pollution sources TLC2 TLC3 TLC4

ξ x1 u1 ε4 u2 u3 u4 x2 x3 x4 ε3 ε2 ε1 Ev Eo classdef am_TLC < atomic %% Description % Adapt. Traffic Control Subsys. model %% Superclass % |atomic| %% Class Methods %% Inherited Properties %% User Defined Properties %% Ports %% States in s %% properties (Access = public) accTflow = [0 0]; accMflow = []; ... end methods function obj = am_TLC(name,inistates,elapsed) ... end ... end end

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SLIDE 8

Consensus-based decision-making

  • Consensus: fundamental problem in the study of cooperative control for

distributed multi-agent coordination.

  • This approach deals with a set of systems each pursuing its own
  • bjectives as well as their common goals, employing communications

between them.

Why consensus? sensus?

  • It’s proposed in the literature as an SoS cooperative-control paradigm to

extract greater benefits from the constituent systems of an SoS [2].

  • Applications: cooperative control of vehicles, robots and rovers, wireless-

sensor networks, traffic-optimization and control problems in urban environments

November 2016 8 3rd International Electronic Conference on Sensors and Applications

[2] T. Nanayakkara, F. Sahin, and M. Jamshidi, Intelligent control systems with an introduction to system of systems engineering: CRC Press, 2010.

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SLIDE 9

Consensus-based cooperative control

  • 1. Graph definition:
  • 2. Representing system dynamics by a consensus

state variable – estimation of pollutant concentration at each intersection 𝜁𝑗 𝑙 + 1 = 𝜁𝑗 𝑙 + 𝛽𝑗𝜊 𝑙 − 𝑜 + 𝛾𝑦𝑗 𝑙 − 𝑛 + 𝛿Δ𝑣𝑗

November 2016 9 3rd International Electronic Conference on Sensors and Applications

TLC1 TLC2 TLC4 TLC3

Current value

  • f consensus

variable Overall city pollution & intersection contribution factor Measured total number

  • f vehicles & relational

factor to intersection emission Control action in %: change of traffic light cycle & relational factor to local emissions

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SLIDE 10

Consensus-based cooperative control

  • 3. Consensus-based control law design

Δ𝑣𝑗 𝑙 = − 1 𝛿 𝛽𝑗𝜊 𝑙 − 𝑜 + 𝛾𝑦𝑗 𝑙 − 𝑛 + 𝜇 𝑏𝑗𝑘 𝜁𝑗 − 𝜁𝑘

𝑘∈𝑂𝑗

Note: control action is restricted to a variation of ±50% over the initial value, to avoid large dissimilarities with pre-defined traffic-light cycle lengths.

November 2016 10 3rd International Electronic Conference on Sensors and Applications

Consensus action based on consensus state of neighbors Feed-forward action related to local pollution data Feed-forward action related to local traffic data

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SLIDE 11

Open loop scenario simulation

November 2016 11 3rd International Electronic Conference on Sensors and Applications

Based d on an urban-li like ke road ad network work:

  • 4 signalized traffic intersections & fixed

traffic-light cycles

  • Vehicles circulate following random routes.

Tools ls:

  • SUMO microscopic traffic simulator
  • MatlabDEVS toolbox

Traffic queues at intersections (AVG. vehicle queues for 20 secs at every approach) NOx emissions (AVG. for 20 secs of the whole scenario)

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SLIDE 12

Closed loop scenario simulation

November 2016 12 3rd International Electronic Conference on Sensors and Applications

  • Same scenario than open loop

simulation

  • Parameters of control system are

specified in section 2.3 of the paper (pp. 4-5)

Vehicle queues at intersections (AVG. vehicle queues for 20 secs at every approach)

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SLIDE 13

Simulation results

November 2016 13 3rd International Electronic Conference on Sensors and Applications

KPI (>100 00 scen enar ario io simul ulat ations ions) Open en-loo loop Closed-loo

  • op

Diff fference ces s rel elati tive ve to

  • pen

en-loo

  • op

Vehicle queues 1.

1 𝑢𝑔−𝑢𝑡

‖𝑦‖ 𝑒𝑢

𝑢𝑔 𝑢𝑡

μ 13,4815 12,0382 10,70 % max 15,0661 13,6345 9,50 % Global pollution 2.

1 𝑢𝑔−𝑢𝑡

‖𝜊‖ 𝑒𝑢

𝑢𝑔 𝑢𝑡

μ 2,3879·10-4 2.3791·10-4 0,37 % min 2,2732·10-4 2,1910·10-4 3,62 % The effect of balancing consensus variables in every TLC produces a global reduction of vehicle queues

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SLIDE 14

Conclusions

November 2016 14 3rd International Electronic Conference on Sensors and Applications

  • Discr

crete ete ev even ent syste tem m spec ecific ificatio ation n (DEV EVS) S) modeling paradigm permitted operations with systems of a different nature and temporal behavior.

  • Consen

ensus sus-based based control

  • l algorithms can be applied to

the specific problems of traffic optimization.

  • KPIs

KPIs and simulations showed that the number of vehicles in queue decreased, while consensus state variable at each intersection tended towards a common value, demonstrating the validity of the proposed solution.

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SLIDE 15

Thank you

November 2016 15 3rd International Electronic Conference on Sensors and Applications

Antonio Artuñedo, Raúl M. del Toro, Rodolfo Haber

{antonio.artunedo, raul.deltoro, rodolfo.haber}@car.upm-csic.es

Centre for Automation and Robotics (UPM-CSIC)

www.car.upm-csic.es