200,15 €
222,39 €
-10% with code: EXTRA
System Design and Control Inte
System Design and Control Inte
200,15
222,39 €
  • We will send in 10–14 business days.
Most existing robust design books address design for static systems, or achieve robust design from experimental data via the Taguchi method. Little work considers model information for robust design particularly for the dynamic system. This book covers robust design for both static and dynamic systems using the nominal model information or the hybrid model/data information, and also integrates design with control under a large operating region. This design can handle strong nonlinearity and mor…
  • Publisher:
  • ISBN-10: 1118822269
  • ISBN-13: 9781118822265
  • Format: 16 x 23.6 x 2.3 cm, hardcover
  • Language: English
  • SAVE -10% with code: EXTRA

System Design and Control Inte (e-book) (used book) | Li | bookbook.eu

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Most existing robust design books address design for static systems, or achieve robust design from experimental data via the Taguchi method. Little work considers model information for robust design particularly for the dynamic system. This book covers robust design for both static and dynamic systems using the nominal model information or the hybrid model/data information, and also integrates design with control under a large operating region. This design can handle strong nonlinearity and more uncertainties from model and parameters.

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  • Author: Li
  • Publisher:
  • ISBN-10: 1118822269
  • ISBN-13: 9781118822265
  • Format: 16 x 23.6 x 2.3 cm, hardcover
  • Language: English English

Most existing robust design books address design for static systems, or achieve robust design from experimental data via the Taguchi method. Little work considers model information for robust design particularly for the dynamic system. This book covers robust design for both static and dynamic systems using the nominal model information or the hybrid model/data information, and also integrates design with control under a large operating region. This design can handle strong nonlinearity and more uncertainties from model and parameters.

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