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Predictive Learning Control for Unknown Nonaffine Nonlinear Systems - Qiongxia Yu - Bog

Bag om Predictive Learning Control for Unknown Nonaffine Nonlinear Systems

This book investigates both theory and various applications of predictive learning control (PLC) which is an advanced technology for complex nonlinear systems. To avoid the difficult modeling problem for complex nonlinear systems, this book begins with the design and theoretical analysis of PLC method without using mechanism model information of the system, and then a series of PLC methods is designed that can cope with system constraints, varying trial lengths, unknown time delay, and available and unavailable system states sequentially. Applications of the PLC on both railway and urban road transportation systems are also studied. The book is intended for researchers, engineers, and graduate students who are interested in predictive control, learning control, intelligent transportation systems and related fields.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9789811988592
  • Indbinding:
  • Paperback
  • Sideantal:
  • 228
  • Udgivet:
  • 18. februar 2024
  • Udgave:
  • 24001
  • Størrelse:
  • 155x12x235 mm.
  • Vægt:
  • 394 g.
  • Ukendt - mangler pt..

Normalpris

  • BLACK NOVEMBER

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Prøv i 30 dage for 45 kr.
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Beskrivelse af Predictive Learning Control for Unknown Nonaffine Nonlinear Systems

This book investigates both theory and various applications of predictive learning control (PLC) which is an advanced technology for complex nonlinear systems. To avoid the difficult modeling problem for complex nonlinear systems, this book begins with the design and theoretical analysis of PLC method without using mechanism model information of the system, and then a series of PLC methods is designed that can cope with system constraints, varying trial lengths, unknown time delay, and available and unavailable system states sequentially. Applications of the PLC on both railway and urban road transportation systems are also studied. The book is intended for researchers, engineers, and graduate students who are interested in predictive control, learning control, intelligent transportation systems and related fields.

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