Non-linear Predictive Control
Theory and practice
Published on 26. October 2001
Book
Hardback
276 pages
978-0-85296-984-7 (ISBN)
Description
Model-based predictive control (MPC) has proved to be a fertile area of research. It has gained enormous success within industry, especially in the context of process control. Nonlinear model-based predictive control (NMPC) is of particular interest as this best represents the dynamics of most real plant. This book collects together the important results which have emerged in this field, illustrating examples by means of simulations on industrial models. In particular there are contributions on feedback linearisation, differential flatness, control Lyapunov functions, output feedback, and neural networks. The international contributors to the book are all respected leaders within the field, which makes for essential reading for advanced students, researchers and industrialists in the field of control of complex systems.
More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 239 mm
Width: 155 mm
Thickness: 18 mm
Weight
499 gr
ISBN-13
978-0-85296-984-7 (9780852969847)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
01/2009
1st Edition
Institution of Engineering and Technology
€174.59
Available for download
Persons
Basil Kouvaritakis is Professor of Engineering Science at Oxford University and has been researching MPC and computationally efficient NMPC for the last 12 years, publishing over 50 papers on the subject.
Mark Cannon is departmental lecturer at the Engineering Department at Oxford University and has been working on MPC for the past 5 years, including the development of computationally efficient NMPC.
Mark Cannon is departmental lecturer at the Engineering Department at Oxford University and has been working on MPC for the past 5 years, including the development of computationally efficient NMPC.
Content
Part I
Chapter 1: Review of nonlinear model predictive control applications
Chapter 2: Nonlinear model predictive control: issues and applications
Part II
Chapter 3: Model predictive control: output feedback and tracking of nonlinear systems
Chapter 4: Model predictive control of nonlinear parameter varying systems via receding horizon control Lyapunov functions
Chapter 5: Nonlinear model-algorithmic control for multivariable nonminimum-phase processes
Part III
Chapter 6: Open-loop and closed-loop optimality in interpolation MPC
Chapter 7: Closed-loop predictions in model based predictive control linear and nonlinear systems
Chapter 8: Computationally efficient non linear predictive control algorithm for control of constrained nonlinear systems
Part IV
Chapter 9: Long-prediction-horizon nonlinear model predictive control
Chapter 10: Nonlinear control of industrial processes
Chapter 11: Nonlinear model based predictive control using multiple local models
Chapter 12: Neural network control of a gasoline engine with rapid sampling
Chapter 1: Review of nonlinear model predictive control applications
Chapter 2: Nonlinear model predictive control: issues and applications
Part II
Chapter 3: Model predictive control: output feedback and tracking of nonlinear systems
Chapter 4: Model predictive control of nonlinear parameter varying systems via receding horizon control Lyapunov functions
Chapter 5: Nonlinear model-algorithmic control for multivariable nonminimum-phase processes
Part III
Chapter 6: Open-loop and closed-loop optimality in interpolation MPC
Chapter 7: Closed-loop predictions in model based predictive control linear and nonlinear systems
Chapter 8: Computationally efficient non linear predictive control algorithm for control of constrained nonlinear systems
Part IV
Chapter 9: Long-prediction-horizon nonlinear model predictive control
Chapter 10: Nonlinear control of industrial processes
Chapter 11: Nonlinear model based predictive control using multiple local models
Chapter 12: Neural network control of a gasoline engine with rapid sampling