Model Predictive control

• Κύριοι συγγραφείς: Camacho, E. F. (Συγγραφέας), Bordons, C. (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: London : Springer London : Imprint: Springer, 2007.
• Έκδοση: Second Edition.
• Σειρά: Advanced Textbooks in Control and Signal Processing,
• Θέματα: Engineering.; Chemical engineering.; System theory.; Control engineering.; Electronics.; Microelectronics.; Control.; Systems Theory, Control.; Industrial Chemistry/Chemical Engineering.; Electronics and Microelectronics, Instrumentation.

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...

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.