Model Predictive control
From power plants to sugar refining, model predictive control (MPC) schemes have established themselves as the preferred control strategies for a wide variety of processes. The second edition of Model Predictive Control provides a thorough introduction to theoretical and practical aspects of the mos...
Κύριοι συγγραφείς: | , |
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Συγγραφή απο Οργανισμό/Αρχή: | |
Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
Γλώσσα: | English |
Έκδοση: |
London :
Springer London : Imprint: Springer,
2007.
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Έκδοση: | Second Edition. |
Σειρά: | Advanced Textbooks in Control and Signal Processing,
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Θέματα: | |
Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 1 Introduction to Model Predictive Control
- 1.1 MPC Strategy
- 1.2 Historical Perspective
- 1.3 Industrial Technology
- 1.4 Outline of the Chapters
- 2 Model Predictive Controllers
- 2.1 MPC Elements
- 2.2 Review of Some MPC Algorithms
- 2.3 State Space Formulation
- 3 Commercial Model Predictive Control Schemes
- 3.1 Dynamic Matrix Control
- 3.2 Model Algorithmic Control
- 3.3 Predictive Functional Control
- 3.4 Case Study: A Water Heater
- 3.5 Exercises
- 4 Generalized Predictive Control
- 4.1 Introduction
- 4.2 Formulation of Generalized Predictive Control
- 4.3 The Coloured Noise Case
- 4.4 An Example
- 4.5 Closed-Loop Relationships
- 4.6 The Role of the T Polynomial
- 4.7 The P Polynomial
- 4.8 Consideration of Measurable Disturbances
- 4.9 Use of a Different Predictor in GPC
- 4.10 Constrained Receding Horizon Predictive Control
- 4.11 Stable GPC
- 4.12 Exercises
- 5 Simple Implementation of GPC for Industrial Processes
- 5.1 Plant Model
- 5.2 The Dead Time Multiple of the Sampling Time Case
- 5.3 The Dead Time Nonmultiple of the Sampling Time Case
- 5.4 Integrating Processes
- 5.5 Consideration of Ramp Setpoints
- 5.6 Comparison with Standard GPC
- 5.7 Stability Robustness Analysis
- 5.8 Composition Control in an Evaporator
- 5.9 Exercises
- 6 Multivariable Model Predictive Control
- 6.1 Derivation of Multivariable GPC
- 6.2 Obtaining a Matrix Fraction Description
- 6.3 State Space Formulation
- 6.4 Case Study: Flight Control
- 6.5 Convolution Models Formulation
- 6.6 Case Study: Chemical Reactor
- 6.7 Dead Time Problems
- 6.8 Case Study: Distillation Column
- 6.9 Multivariable MPC and Transmission Zeros
- 6.10 Exercises
- 7 Constrained Model Predictive Control
- 7.1 Constraints and MPC
- 7.2 Constraints and Optimization
- 7.3 Revision of Main Quadratic Programming Algorithms
- 7.4 Constraints Handling
- 7.5 1-norm
- 7.6 Case Study: A Compressor
- 7.7 Constraint Management
- 7.8 Constrained MPC and Stability
- 7.9 Multiobjective MPC
- 7.10 Exercises
- 8 Robust Model Predictive Control
- 8.1 Process Models and Uncertainties
- 8.2 Objective Functions
- 8.3 Robustness by Imposing Constraints
- 8.4 Constraint Handling
- 8.5 Illustrative Examples
- 8.6 Robust MPC and Linear Matrix Inequalities
- 8.7 Closed-Loop Predictions
- 8.8 Exercises
- 9 Nonlinear Model Predictive Control
- 9.1 Nonlinear MPC Versus Linear MPC
- 9.2 Nonlinear Models
- 9.3 Solution of the NMPC Problem
- 9.4 Techniques for Nonlinear Predictive Control
- 9.5 Stability and Nonlinear MPC
- 9.6 Case Study: pH Neutralization Process
- 9.7 Exercises
- 10 Model Predictive Control and Hybrid Systems
- 10.1 Hybrid System Modelling
- 10.2 Example: A Jacket Cooled Batch Reactor
- 10.3 Model Predictive Control of MLD Systems
- 10.4 Piecewise Affine Systems
- 10.5 Exercises
- 11 Fast Methods for Implementing Model Predictive Control
- 11.1 Piecewise Affinity of MPC
- 11.2 MPC and Multiparametric Programming
- 11.3 Piecewise Implementation of MPC
- 11.4 Fast Implementation of MPC forUncertain Systems
- 11.5 Approximated Implementation for MPC
- 11.6 Fast Implementation of MPC and Dead Time Considerations
- 11.7 Exercises
- 12 Applications
- 12.1 Solar Power Plant
- 12.2 Pilot Plant
- 12.3 Model Predictive Control in a Sugar Refinery
- 12.4 Olive Oil Mill
- 12.5 Mobile Robot
- A Revision of the Simplex Method
- A.1 Equality Constraints
- A.2 Finding an Initial Solution
- A.3 Inequality Constraints
- B Dynamic Programming and Linear Quadratic Optimal Control
- B.1 LinearQuadratic Problem
- B.2 InfiniteHorizon
- References.