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MPC with MATLAB springer 经典书籍.pdf

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    Cover Page
    Other titles published in this series
    Title: Model Predictive Control System Design and Implementation Using MATLAB®
    ISBN 1848823304
    Advances in Industrial Control
    Series Editors’ Foreword
    Preface
    About this Book
    Development of this Book
    Theory
    Practice
    Presentation
    Book Structure
    Acknowledgements
    Contents (with page links)
    List of Symbols and Abbreviations
    1 Discrete-time MPC for Beginners
    2 Discrete-time MPC with Constraints
    3 Discrete-time MPC Using Laguerre Functions
    4 Discrete-time MPC with Prescribed Degree of Stability
    5 Continuous-time Orthonormal Basis Functions
    6 Continuous-time MPC
    7 Continuous-time MPC with Constraints
    8 Continuous-time MPC with Prescribed Degree of Stability
    9 Classical MPC Systems in State-space Formulation
    10 Implementation of Predictive Control Systems
    References
    Index
    List of Symbols and Abbreviations
    Symbols
    Abbreviations
    1 Discrete-time MPC for Beginners
    1.1 Introduction
    1.1.1 Day-to-day Application Example of Predictive Control
    1.1.2 Models Used in the Design
    1.2 State-space Models with Embedded Integrator
    1.2.1 Single-input and Single-output System
    1.2.2 MATLAB Tutorial: Augmented Design Model
    1.3 Predictive Control within One Optimization Window
    1.3.1 Prediction of State and Output Variables
    1.3.2 Optimization
    1.3.3 MATLAB Tutorial: Computation of MPC Gains
    1.4 Receding Horizon Control
    1.4.1 Closed-loop Control System
    1.4.2 MATLAB Tutorial: Implementation of Receding Horizon Control
    1.5 Predictive Control of MIMO Systems
    1.5.1 General Formulation of the Model
    1.5.2 Solution of Predictive Control for MIMO Systems
    1.6 State Estimation
    1.6.1 Basic Ideas About an Observer
    1.6.2 Basic Results About Observability De.nition of Observability
    1.6.3 Kalman Filter
    1.6.4 Tuning Observer Dynamics
    1.7 State Estimate Predictive Control
    1.8 Summary
    Problems
    2 Discrete-time MPC with Constraints
    2.1 Introduction
    2.2 Motivational Examples
    2.3 Formulation of Constrained Control Problems
    2.3.1 Frequently Used Operational Constraints
    2.3.2 Constraints as Part of the Optimal Solution
    2.4 Numerical Solutions Using Quadratic Programming
    2.4.1 Quadratic Programming for Equality Constraints
    2.4.2 Minimization with Inequality Constraints
    2.4.3 Primal-Dual Method
    2.4.4 Hildreth’s Quadratic Programming Procedure
    2.4.5 MATLAB Tutorial: Hildreth’s Quadratic Programming
    2.4.6 Closed-form Solution of λ*
    2.5 Predictive Control with Constraints on Input Variables
    2.5.1 Constraints on Rate of Change
    2.5.2 Constraints on Amplitude of the Control
    2.5.3 Constraints on Amplitude and Rate of Change
    2.5.4 Constraints on the Output Variable
    2.6 Summary
    Problems
    3 Discrete-time MPC Using Laguerre Functions
    3.1 Introduction
    3.2 Laguerre Functions and DMPC
    3.2.1 Discrete-time Laguerre Networks
    3.2.2 Use of Laguerre Networks in System Description
    3.2.3 MATLAB Tutorial: Use of Laguerre Functions in System Modelling
    3.3 Use of Laguerre Functions in DMPC Design
    3.3.1 Design Framework
    3.3.2 Cost Functions
    3.3.3 Minimization of the Objective Function
    3.3.4 Convolution Sum
    3.3.5 Receding Horizon Control
    3.3.6 The Optimal Trajectory of Incremental Control
    3.4 Extension to MIMO Systems
    3.5 MATLAB Tutorial Notes
    3.5.1 DMPC Computation
    3.5.2 Predictive Control System Simulation
    3.6 Constrained Control Using Laguerre Functions
    3.6.1 Constraints on the Di.erence of the Control Variable
    3.6.2 Constraints on the Amplitudes of the Control Signal
    3.7 Stability Analysis
    3.7.1 Stability with Terminal-State Constraints
    3.7.2 Stability with Large Prediction Horizon
    3.8 Closed-form Solution of Constrained Control for SISO Systems
    3.8.1 MATLAB Tutorial: Constrained Control of DC Motor
    3.9 Summary
    Problems
    4 Discrete-time MPC with Prescribed Degree of Stability
    4.1 Introduction
    4.2 Finite Prediction Horizon: Re-visited
    4.2.1 Motivational Example
    4.2.2 Origin of the Numerical Conditioning Problem
    4.3 Use of Exponential Data Weighting
    4.3.1 The Cost Function
    4.3.2 Optimization of Exponentially Weighted Cost Function
    4.3.3 Interpretation of Results from Exponential Weighting
    4.4 Asymptotic Closed-loop Stability with Exponential Weighting
    4.4.1 Modification of Q and R Matrices
    4.4.2 Interpretation of the Results
    4.5 Discrete-time MPC with Prescribed Degree of Stability
    4.6 Tuning Parameters for Closed-loop Performance
    4.6.1 The Relationship Between P∞ and Jmin
    4.6.2 Tuning Procedure Once More
    4.7 Exponentially Weighted Constrained Control
    4.8 Additional Benefit
    4.9 Summary
    Problems
    5 Continuous-time Orthonormal Basis Functions
    5.1 Introduction
    5.2 Orthonormal Expansion
    5.3 Laguerre Functions
    5.4 Approximating Impulse Responses
    5.5 Kautz Functions
    5.5.1 Kautz Functions in the Time Domain
    5.5.2 Modelling the System Impulse Response
    5.6 Summary
    Problems
    6 Continuous-time MPC
    6.1 Introduction
    6.2 Model Structures for CMPC Design
    6.2.1 Model Structure
    6.2.2 Controllability and Observability of the Model
    6.3 Model Predictive Control Using Finite Prediction Horizon
    6.3.1 Modelling the Control Trajectory
    6.3.2 Predicted Plant Response
    6.3.3 Analytical Solution of the Predicted Response
    6.3.4 The Recursive Solution
    6.4 Optimal Control Strategy
    6.5 Receding Horizon Control
    6.6 Implementation of the Control Law in Digital Environment
    6.6.1 Estimation of the States
    6.6.2 MATLAB Tutorial: Closed-loop Simulation
    6.7 Model Predictive Control Using Kautz Functions
    6.8 Summary
    Problems
    7 Continuous-time MPC with Constraints
    7.1 Introduction
    7.2 Formulation of the Constraints
    7.2.1 Frequently Used Constraints
    7.2.2 Constraints as Part of the Optimal Solution
    7.3 Numerical Solutions for the Constrained Control Problem
    7.4 Real-time Implementation of Continuous-time MPC
    7.5 Summary
    Problems
    8 Continuous-time MPC with Prescribed Degree of Stability
    8.1 Introduction
    8.2 Motivating Example
    8.3 CMPC Design Using Exponential Data Weighting
    8.4 CMPC with Asymptotic Stability
    8.5 Continuous-time MPC with Prescribed Degree of Stability
    8.5.1 The Original Anderson and Moore’s Results
    8.5.2 CMPC with a Prescribed Degree of Stability
    8.5.3 Tuning Parameters and Design Procedure Selection of the Exponential Weighting Factor
    8.6 Constrained Control with Exponential Data Weighting
    8.7 Summary
    Problems
    9 Classical MPC Systems in State-space Formulation
    9.1 Introduction
    9.2 Generalized Predictive Control in State-space Formulation
    9.2.1 Special Class of Discrete-time State-space Structures
    9.2.2 General NMSS Structure for GPC Design
    9.2.3 Generalized Predictive Control in State-space Formulation
    9.3 Alternative Formulation to GPC
    9.3.1 Alternative Formulation for SISO Systems
    9.3.2 Closed-loop Poles of the Predictive Control System
    9.3.3 Transfer Function Interpretation
    9.4 Extension to MIMO Systems
    9.4.1 MNSS Model for MIMO Systems
    9.4.2 Case Study of NMSS Predictive Control System
    9.5 Continuous-time NMSS model
    9.6 Case Studies for Continuous-time MPC
    9.7 Predictive Control Using Impulse Response Models
    9.8 Summary
    Problems
    10 Implementation of Predictive Control Systems
    10.1 Introduction
    10.2 Predictive Control of DC Motor Using a Micro-controller
    10.2.1 Hardware Configuration
    10.2.2 Model Development
    10.2.3 DMPC Tuning
    10.2.4 DMPC Implementation
    10.2.5 Experimental Results Bene.ts of Imposing Constraints
    10.3 Implementation of Predictive Control Using xPC Target
    10.3.1 Overview
    10.3.2 Creating a SIMULINK Embedded Function
    10.3.3 Constrained Control of DC Motor Using xPC Target
    10.4 Control of Magnetic Bearing Systems
    10.4.1 System Identification
    10.4.2 Experimental Results
    10.5 Continuous-time Predictive Control of Food Extruder
    10.5.1 Experimental Setup
    10.5.2 Mathematical Models
    10.5.3 Operation of the Model Predictive Controller
    10.5.4 Controller Tuning Parameters
    10.6 Summary
    References
    Index (with page links)
    Other titles published in this series (continued)
    Advances in Industrial Control
    Other titles published in this series: Digital Controller Implementation and Fragility Robert S.H. Istepanian and James F. Whidborne (Eds.) Optimisation of Industrial Processes at Supervisory Level Doris Sáez, Aldo Cipriano and Andrzej W. Ordys Robust Control of Diesel Ship Propulsion Nikolaos Xiros Hydraulic Servo-systems Mohieddine Jelali and Andreas Kroll Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques Silvio Simani, Cesare Fantuzzi and Ron J. Patton Strategies for Feedback Linearisation Freddy Garces, Victor M. Becerra, Chandrasekhar Kambhampati and Kevin Warwick Robust Autonomous Guidance Alberto Isidori, Lorenzo Marconi and Andrea Serrani Dynamic Modelling of Gas Turbines Gennady G. Kulikov and Haydn A. Thompson (Eds.) Control of Fuel Cell Power Systems Jay T. Pukrushpan, Anna G. Stefanopoulou and Huei Peng Fuzzy Logic, Identification and Predictive Control Jairo Espinosa, Joos Vandewalle and Vincent Wertz Optimal Real-time Control of Sewer Networks Magdalene Marinaki and Markos Papageorgiou Process Modelling for Control Benoît Codrons Computational Intelligence in Time Series Forecasting Ajoy K. Palit and Dobrivoje Popovic Modelling and Control of Mini-Flying Machines Pedro Castillo, Rogelio Lozano and Alejandro Dzul Ship Motion Control Tristan Perez Hard Disk Drive Servo Systems (2nd Ed.) Ben M. Chen, Tong H. Lee, Kemao Peng and Venkatakrishnan Venkataramanan Measurement, Control, and Communication Using IEEE 1588 John C. Eidson Piezoelectric Transducers for Vibration Control and Damping S.O. Reza Moheimani and Andrew J. Fleming Manufacturing Systems Control Design Stjepan Bogdan, Frank L. Lewis, Zdenko Kovačić and José Mireles Jr. Windup in Control Peter Hippe Nonlinear H2/H∞ Constrained Feedback Control Murad Abu-Khalaf, Jie Huang and Frank L. Lewis Practical Grey-box Process Identification Torsten Bohlin Control of Traffic Systems in Buildings Sandor Markon, Hajime Kita, Hiroshi Kise and Thomas Bartz-Beielstein Wind Turbine Control Systems Fernando D. Bianchi, Hernán De Battista and Ricardo J. Mantz Advanced Fuzzy Logic Technologies in Industrial Applications Ying Bai, Hanqi Zhuang and Dali Wang (Eds.) Practical PID Control Antonio Visioli (continued after Index)
    Liuping Wang Model Predictive Control System Design and Implementation Using MATLAB® 123
    Liuping Wang, PhD School of Electrical and Computer Engineering RMIT University Melbourne, VIC 3000 Australia ISBN 978-1-84882-330-3 e-ISBN 978-1-84882-331-0 DOI 10.1007/978-1-84882-331-0 Advances in Industrial Control ISSN 1430-9491 A catalogue record for this book is available from the British Library Library of Congress Control Number: 2008940691 © 2009 Springer-Verlag London Limited MATLAB® and Simulink® are registered trademarks of The MathWorks, Inc., 3 Apple Hill Drive, Natick, MA 01760-2098, USA. http://www.mathworks.com Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Cover design: eStudio Calamar S.L., Girona, Spain Printed on acid-free paper 9 8 7 6 5 4 3 2 1 springer.com
    Advances in Industrial Control Series Editors Professor Michael J. Grimble, Professor of Industrial Systems and Director Professor Michael A. Johnson, Professor (Emeritus) of Control Systems and Deputy Director Industrial Control Centre Department of Electronic and Electrical Engineering University of Strathclyde Graham Hills Building 50 George Street Glasgow G1 1QE United Kingdom Series Advisory Board Professor E.F. Camacho Escuela Superior de Ingenieros Universidad de Sevilla Camino de los Descubrimientos s/n 41092 Sevilla Spain Professor S. Engell Lehrstuhl für Anlagensteuerungstechnik Fachbereich Chemietechnik Universität Dortmund 44221 Dortmund Germany Professor G. Goodwin Department of Electrical and Computer Engineering The University of Newcastle Callaghan NSW 2308 Australia Professor T.J. Harris Department of Chemical Engineering Queen’s University Kingston, Ontario K7L 3N6 Canada Professor T.H. Lee Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576
    Professor (Emeritus) O.P. Malik Department of Electrical and Computer Engineering University of Calgary 2500, University Drive, NW Calgary, Alberta T2N 1N4 Canada Professor K.-F. Man Electronic Engineering Department City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong Professor G. Olsson Department of Industrial Electrical Engineering and Automation Lund Institute of Technology Box 118 221 00 Lund Sweden Professor A. Ray Department of Mechanical Engineering Pennsylvania State University 0329 Reber Building University Park PA 16802 USA Professor D.E. Seborg Chemical Engineering 3335 Engineering II University of California Santa Barbara Santa Barbara CA 93106 USA Doctor K.K. Tan Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576 Professor I. Yamamoto Department of Mechanical Systems and Environmental Engineering The University of Kitakyushu Faculty of Environmental Engineering 1-1, Hibikino,Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135 Japan
    In memory of my parents
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