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Autor(en): 
  • Andrea Garulli
  • Alberto Tesi
  • Antonio Vicino
  • Graziano Chesi
  • Homogeneous Polynomial Forms for Robustness Analysis of Uncertain Systems 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 14-24 Tagen versandfertig
    Veröffentlichung:  Juli 2009  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Algebra / Automatic control engineering / C / Civil Engineering / Control and Systems Theory / Control engineering / Control, Robotics, Automation / Control, Robotics, Mechatronics / Cybernetics & systems theory / Dynamical systems / Dynamics / Electronic devices & materials / engineering / Mathematical optimization / Mechanics of solids / Mechatronics / Optimization / Robotics / System Theory / Systems Theory, Control / Vibration / Vibration, Dynamical Systems, Control
    ISBN:  9781848827806 
    EAN-Code: 
    9781848827806 
    Verlag:  Springer Nature Singapore 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  #390 - Lecture Notes in Control and Information Sciences  
    Dimensionen:  H 241 mm / B 167 mm / D 23 mm 
    Gewicht:  480 gr 
    Seiten:  198 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    It is well known that a large number of problems relevant to the control ?eld can be formulatedas optimizationproblems. For long time, the classical approachhas been to look for a closed form solution to the speci?c optimizationproblems at hand. The last decade has seen a noticeable shift in the meaning of "closed form" solution. The formidable increase of computationalpower has dramatically changed the fe- ing of theoreticians as well as of practitioners about what is meant by tractable and untractableproblems. A main issue regardsconvexity. From a theoretical viewpoint, there has been a large amount of work in the directions of "convexifying" nonc- vex problems and studying structural features of convex problems. On the other hand, extremely powerful algorithmsfor the solution of convexproblemshave been devised in the last two decades. Clearly, the fact that a wide variety of engine- ing problems can be formulated as convex problems has strongly motivated efforts in this direction. The control ?eld is not an exception in this sense: many pr- lems in robust control, identi?cation and nonlinear control have been recognized as convex problems. Moreover, convex relaxations of nonconvex problems have been intensively investigated, as they provide an effective tool for bounding the optimal solution of the original problem. As far as robust control is concerned, it is known since long time that several classes of problemscan be reducedto testing positivity of suitable polynomials.

      



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