Model Predictive control

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...

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Detalles Bibliográficos
Autor principal: Camacho, E. F. (-)
Autor Corporativo: SpringerLink (-)
Otros Autores: Bordons, C.
Formato: Libro electrónico
Idioma:Inglés
Publicado: London : Springer London 2007.
Edición:2nd ed
Colección:Advanced Textbooks in Control and Signal Processing.
Springer eBooks.
Acceso en línea:Conectar con la versión electrónica
Ver en Universidad de Navarra:https://innopac.unav.es/record=b32709870*spi
Tabla de Contenidos:
  • 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.

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