Modelling and Control for Intelligent Industrial Systems: Adaptive Algorithms in Robotics and Industrial Engineering

409 Pages·2011·11.72 MB·English

by  Gerasimos G. Rigatos (auth.)

GerasimosG.Rigatos
ModellingandControlforIntelligentIndustrialSystems
IntelligentSystemsReferenceLibrary,Volume7
Editors-in-Chief
Prof.JanuszKacprzyk Prof.LakhmiC.Jain
SystemsResearchInstitute UniversityofSouthAustralia
PolishAcademyofSciences Adelaide
ul.Newelska6 MawsonLakesCampus
01-447Warsaw SouthAustralia5095
Poland Australia
E-mail:[email protected] E-mail:[email protected]
Furthervolumesofthisseriescanbefoundonourhomepage:springer.com
Vol.1.ChristineL.MumfordandLakhmiC.Jain(Eds.)
ComputationalIntelligence:Collaboration,Fusion
andEmergence,2009
ISBN978-3-642-01798-8
Vol.2.YuehuiChenandAjithAbraham
Tree-StructureBasedHybrid
ComputationalIntelligence,2009
ISBN978-3-642-04738-1
Vol.3.AnthonyFinnandSteveScheding
DevelopmentsandChallengesfor
AutonomousUnmannedVehicles,2010
ISBN978-3-642-10703-0
Vol.4.LakhmiC.JainandCheePengLim(Eds.)
HandbookonDecisionMaking:Techniques
andApplications,2010
ISBN978-3-642-13638-2
Vol.5.GeorgeA.Anastassiou
IntelligentMathematics:ComputationalAnalysis,2010
ISBN978-3-642-17097-3
Vol.6.LudmilaDymowa
SoftComputinginEconomicsandFinance,2011
ISBN978-3-642-17718-7
Vol.7.GerasimosG.Rigatos
ModellingandControlforIntelligentIndustrialSystems,2011
ISBN978-3-642-17874-0
Gerasimos G.Rigatos
Modelling and Control for
Intelligent Industrial Systems
Adaptive Algorithms in Robotics and
Industrial Engineering
123
GerasimosG.Rigatos
UnitofIndustrialAutomation,
IndustrialSystemsInstitute,
RionPatras,
Greece26504
E-mail:[email protected]
ISBN 978-3-642-17874-0 e-ISBN978-3-642-17875-7
DOI 10.1007/978-3-642-17875-7
Intelligent SystemsReference Library ISSN1868-4394
(cid:2)c 2011 Springer-VerlagBerlin Heidelberg
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Foreword
Therearetwomainrequirementsforthedevelopmentofintelligentindustrial
systems: (i) learning and adaptationin unknown environments,(ii) compen-
sation of model uncertainties as well as of unknown or stochastic external
disturbances. Learning can be performed with the use of gradient-type al-
gorithms (also applied to nonlinear modeling techniques) or with the use
of derivative-free stochastic algorithms. The compensation of uncertainties
in the model’s parameters as well as of external disturbances can be per-
formedthroughstochasticestimationalgorithms(usually appliedto filtering
and identification problems), and through the design of adaptive and ro-
bust controlschemes.The bookaims atprovidinga thoroughanalysisofthe
aforementioned issues.
Dr. Gerasimos G. Rigatos
Senior Researcher
Unit of Industrial Automation
Industrial Systems Institute
Greece
Preface
Incorporating intelligence in industrial systems can help to increase produc-
tivity,cut-offproductioncosts,andtoimproveworkingconditionsandsafety
in industrial environments. This need has resulted in the rapid development
of modeling and control methods for industrial systems and robots, of fault
detection and isolation methods for the prevention of critical situations in
industrial work-cellsand production plants, of optimization methods aiming
atamoreprofitablefunctioningofindustrialinstallationsandroboticdevices
and of machine intelligence methods aiming at reducing human intervention
in industrial systems operation.
To this end, the book defines and analyzes some main directions of re-
searchinmodelingandcontrolforindustrialsystems.Theseare:(i)industrial
robots,(ii) mobile robotsandautonomousvehicles,(iii) adaptive androbust
control of electromechanicalsystems, (iv) filtering and stochastic estimation
for multi-sensor fusion and sensorless control of industrial systems (iv) fault
detectionandisolationinroboticandindustrialsystems,(v)optimizationin
industrial automation and robotic systems design, (vi) machine intelligence
for robots autonomy, and (vii) vision-based industrial systems.
Intheareaofindustrialrobotsonecandistinguishbetweentwomainprob-
lems: (i) robots operating in a free working space, as in the case of robotic
welding, painting, or laser and plasma cutting and (ii) robots performing
compliance tasks, as in the case of assembling, finishing of metal surfaces
andpolishing. When the robotic manipulator operatesin a free environment
then kinematic and dynamic analysis providethe means for designing a con-
trollaw thatwillmoveappropriatelythe robot’sendeffector andwillenable
the completion of the scheduled tasks. In the case of compliance tasks, the
objective is not only to control the end effector’s position but also to regu-
latethe forcedevelopeddue to contactwith the processedsurface.Thereare
establishedapproachesfor simultaneouspositionandforcecontrolof robotic
manipulators which were initially designed for rigid-link robots and which
were subsequently extended to flexible-link robots.