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Model Predictive Control

Baocang Ding | Yuanqing Yang
€117.99
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A01=Baocang Ding
A01=Yuanqing Yang
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Author_Baocang Ding
Author_Yuanqing Yang
automatic-update
Category1=Non-Fiction
Category=TJ
closed-loop stability
convex programming
COP=United States
Delivery_Delivery within 10-20 working days
dynamic matrix control
economic optimization
eq_bestseller
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_tech-engineering
finite impulse response
finite step response
fuzzy control
generalized predictive control
hierarchical control
industrial processes
Kalman filter
Language_English
linear empirical modeling
linear parameter varying model
linear programming
model predictive control
multiple priority-rank optimization
multivariable control system
networked control
norm-bounding technique
offset-free control
open-loop prediction
output feedback control
PA=Available
Price_€100 and above
Process control
PS=Active
quadratic boundedness
quadratic programming
robust control
SN=Wiley - IEEE
softlaunch
steady-state target calculation
two-step control

Product details

  • ISBN 9781119471394
  • Weight: 680g
  • Dimensions: 170 x 244mm
  • Publication Date: 09 May 2024
  • Publisher: John Wiley & Sons Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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Description
Model Predictive Control

Understand the practical side of controlling industrial processes

Model Predictive Control (MPC) is a method for controlling a process according to given parameters, derived in many cases from empirical models. It has been widely applied in industrial units to increase revenue and promoting sustainability. Systematic overviews of this subject, however, are rare, and few draw on direct experience in industrial settings.

Assuming basic knowledge of the relevant mathematical and algebraic modeling techniques, the book’s title combines foundational theories of MPC with a thorough sense of its practical applications in an industrial context. The result is a presentation uniquely suited to rapid incorporation in an industrial workplace.

Model Predictive Control readers will also find:

  • Two-part organization to balance theory and applications
  • Selection of topics directly driven by industrial demand
  • An author with decades of experience in both teaching and industrial practice

This book is ideal for industrial control engineers and researchers looking to understand MPC technology, as well as advanced undergraduate and graduate students studying predictive control and related subjects.
About Baocang Ding | Yuanqing Yang

Baocang Ding, PhD, teaches MPC to both undergraduate and graduate students in the School of Automation, Chongqing University of Posts and Telecommunications, China. His research interests include model predictive control, control of power network, process control, and control software development.

Yuanqing Yang, PhD, teaches MPC to both undergraduate and graduate students in the School of Automation, Chongqing University of Posts and Telecommunications, China. His research interests include model predictive control, fuzzy control, networked control, and distributed control systems.

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