000 06764nam a22005055i 4500
001 978-0-85729-398-5
003 DE-He213
005 20260521092039.0
007 cr nn 008mamaa
008 110826s2007 xxk| s |||| 0|eng d
020 _a9780857293985
020 _a99780857293985
024 7 _a10.1007/978-0-85729-398-5
_2doi
082 0 4 _a629.8
_223
100 1 _aCamacho, E. F.
_eauthor.
245 1 0 _aModel Predictive control
_h[electronic resource] /
_cby E. F. Camacho, C. Bordons.
250 _aSecond Edition.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2007.
300 _aXXII, 405 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvanced Textbooks in Control and Signal Processing,
_x1439-2232
505 0 _a1 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.
520 _aFrom 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.
650 0 _aENGINEERING.
650 0 _aCHEMICAL ENGINEERING.
650 0 _aSYSTEMS THEORY.
650 0 _aELECTRONICS.
650 1 4 _aENGINEERING.
650 2 4 _aCONTROL.
650 2 4 _aSYSTEMS THEORY, CONTROL.
650 2 4 _aINDUSTRIAL CHEMISTRY/CHEMICAL ENGINEERING.
650 2 4 _aELECTRONICS AND MICROELECTRONICS, INSTRUMENTATION.
700 1 _aBordons, C.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781852336943
830 0 _aAdvanced Textbooks in Control and Signal Processing,
_x1439-2232
856 4 0 _uhttp://dx.doi.org/10.1007/978-0-85729-398-5
_zVer el texto completo en las instalaciones del CICY
912 _aZDB-2-ENG
942 _2ddc
_cER
999 _c35958
_d35958