000 03965nam a22004455i 4500
001 u375610
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005 20160812084332.0
007 cr nn 008mamaa
008 110131s2011 gw | s |||| 0|eng d
020 _a9783642178757
_9978-3-642-17875-7
040 _cMX-MeUAM
050 4 _aQ342
082 0 4 _a006.3
_223
100 1 _aRigatos, Gerasimos G.
_eauthor.
245 1 0 _aModelling and Control for Intelligent Industrial Systems
_h[recurso electrónico] :
_bAdaptive Algorithms in Robotics and Industrial Engineering /
_cby Gerasimos G. Rigatos.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2011.
300 _aXXX, 382p. 220 illus., 134 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v7
505 0 _aIndustrial robots in contact-free operation -- Industrial robots in compliance tasks -- Mobile robots and autonomous vehicles -- Adaptive control methods for industrial systems .-Robust control methods for industrial systems -- Filtering and estimation methods for industrial systems -- Sensor fusion-based control for industrial systems -- Fault detection and isolation for industrial systems -- Application of fault diagnosis to industrial systems -- Optimization methods for motion planning of multi-robot systems -- Optimization methods for target tracking by multi-robot systems -- Optimization methods for industrial automation -- Machine learning methods for industrial systems control -- Machine learning methods for industrial systems fault diagnosis -- Applications of machine vision to industrial systems.
520 _aIncorporating intelligence in industrial systems can help to increase productivity, cut-off production costs, and to improve working conditions and safety 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-cells and production plants, of optimization methods aiming at a more profitable functioning of industrial installations and robotic devices and of machine intelligence methods aiming at reducing human intervention in industrial systems operation. To this end, the book analyzes and extends some main directions of research in modeling and control for industrial systems. These are: (i) industrial robots, (ii) mobile robots and autonomous vehicles, (iii) adaptive and robust control of electromechanical systems, (iv) filtering and stochastic estimation for multisensor fusion and sensorless control of industrial systems (iv) fault detection and isolation in robotic and industrial systems, (v) optimization in industrial automation and robotic systems design, and (vi) machine intelligence for robots autonomy. The book will be a useful companion to engineers and researchers since it covers a wide spectrum of problems in the area of industrial systems. Moreover, the book is addressed to undergraduate and post-graduate students, as an upper-level course supplement of automatic control and robotics courses.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aRobotics and Automation.
650 2 4 _aArtificial Intelligence (incl. Robotics).
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642178740
830 0 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v7
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-17875-7
596 _a19
942 _cLIBRO_ELEC
999 _c203490
_d203490