Model Predictive control [electronic resource] / by E. F. Camacho, C. Bordons.

By: Camacho, E. F [author.]Contributor(s): Bordons, C [author.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Advanced Textbooks in Control and Signal Processing: Publisher: London : Springer London : Imprint: Springer, 2007Edition: Second EditionDescription: XXII, 405 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9780857293985Subject(s): Engineering | Chemical engineering | Systems theory | Electronics | Engineering | Control | Systems Theory, Control | Industrial Chemistry/Chemical Engineering | Electronics and Microelectronics, InstrumentationAdditional physical formats: Printed edition:: No titleDDC classification: 629.8 LOC classification: TJ212-225Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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 most commonly used MPC strategies. It bridges the gap between the powerful but often abstract techniques of control researchers and the more empirical approach of practitioners. Model Predictive Control demonstrates that a powerful technique does not always require complex control algorithms. The text features material on the following subjects: general MPC elements and algorithms; commercial MPC schemes; generalized predictive control multivariable, robust, constrained nonlinear and hybrid MPC; fast methods for MPC implementation; applications. All of the material is thoroughly updated for the second edition with the chapters on nonlinear MPC, MPC and hybrid systems and MPC implementation being entirely new. Many new exercises and examples have also have also been added throughout and Matlab® programs to aid in their solution can be downloaded from the authors' website at http://www.esi.us.es/MPCBOOK. The text is an excellent aid for graduate and advanced undergraduate students and will also be of use to researchers and industrial practitioners wishing to keep abreast of a fast-moving field.
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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.

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 most commonly used MPC strategies. It bridges the gap between the powerful but often abstract techniques of control researchers and the more empirical approach of practitioners. Model Predictive Control demonstrates that a powerful technique does not always require complex control algorithms. The text features material on the following subjects: general MPC elements and algorithms; commercial MPC schemes; generalized predictive control multivariable, robust, constrained nonlinear and hybrid MPC; fast methods for MPC implementation; applications. All of the material is thoroughly updated for the second edition with the chapters on nonlinear MPC, MPC and hybrid systems and MPC implementation being entirely new. Many new exercises and examples have also have also been added throughout and Matlab® programs to aid in their solution can be downloaded from the authors' website at http://www.esi.us.es/MPCBOOK. The text is an excellent aid for graduate and advanced undergraduate students and will also be of use to researchers and industrial practitioners wishing to keep abreast of a fast-moving field.

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