Model predictive control:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
London [u.a.]
Springer
2004
|
| Edition: | 2. ed. |
| Series: | Advanced textbooks in control and signal processing
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke. - Literaturverz. S. 389 - 399 |
| Physical Description: | XXII, 405 S. graph. Darst. |
| ISBN: | 1852336943 9781852336943 |
Staff View
MARC
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| 100 | 1 | |a Camacho, E. F. |d 1950- |e Verfasser |0 (DE-588)118087983 |4 aut | |
| 245 | 1 | 0 | |a Model predictive control |c E. F. Camacho and C. Bordons |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a London [u.a.] |b Springer |c 2004 | |
| 300 | |a XXII, 405 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Advanced textbooks in control and signal processing | |
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke. - Literaturverz. S. 389 - 399 | ||
| 650 | 4 | |a Predictive control | |
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Record in the Search Index
| _version_ | 1809768508278439936 |
|---|---|
| adam_text |
E.F CAMACHO AND C. BORDONS MODEL PREDICTIVE CONTROL SECOND EDITION WITH
139 FIGURES SPRINGER CONTENTS 1 INTRODUCTION TO MODEL PREDICTIVE CONTROL
1 1.1 MPC STRATEGY 2 1.2 HISTORICAL PERSPECTIVE 5 1.3 INDUSTRIAL
TECHNOLOGY :. 8 1.4 OUTLINE OF THE CHAPTERS 10 2 MODEL PREDICTIVE
CONTROLLERS 13 2.1 MPC ELEMENTS 13 2.1.1 PREDICTION MODEL 13 2.1.2
OBJECTIVE FUNCTION 18 2.1.3 OBTAINING THE CONTROL LAW 21 2.2 REVIEW OF
SOME MPC ALGORITHMS 22 2.3 STATE SPACE FORMULATION 27 3 COMMERCIAL MODEL
PREDICTIVE CONTROL SCHEMES 31 3.1 DYNAMIC MATRIX CONTROL ^31 3.1.1
PREDICTION 32 3.1.2 MEASURABLE DISTURBANCES 34 3.1.3 CONTROL ALGORITHM.
34 3.2 MODEL ALGORITHMIC CONTROL 36 3.2.1 PROCESS MODEL AND PREDICTION
36 3.2.2 CONTROL LAW 38 3.3 PREDICTIVE FUNCTIONAL CONTROL 39 3.3.1
FORMULATION 39 3.4 CASE STUDY: A WATER HEATER 42 3.5 EXERCISES 45 4
GENERALIZED PREDICTIVE CONTROL 47 4.1 INTRODUCTION 47 4.2 FORMULATION OF
GENERALIZED PREDICTIVE CONTROL 48 4.3 THE COLOURED NOISE CASE 53 XVIII
CONTENTS 4.4 AN EXAMPLE 54 4.5 CLOSED-LOOP RELATIONSHIPS 57 4.6 THE ROLE
OF THE T POLYNOMIAL 61 4.6.1 SELECTION OF THE T POLYNOMIAL 61 4.6.2
RELATIONSHIPS WITH OTHER FORMULATIONS 62 4.7 THE P POLYNOMIAL 62 4.8
CONSIDERATION OF MEASURABLE DISTURBANCES 63 4.9 USE OF A DIFFERENT
PREDICTOR IN GPC 66 4.9.1 EQUIVALENT STRUCTURE 66 4.9.2 A COMPARATIVE
EXAMPLE 70 4.10 CONSTRAINED RECEDING HORIZON PREDICTIVE CONTROL 71
4.10.1 COMPUTATION OF THE CONTROL LAW 72 4.10.2 PROPERTIES 75 4.11
STABLE GPC , 76 4.11.1 FORMULATION OF THE CONTROL LAW 77 ALL EXERCISES
78 5 SIMPLE IMPLEMENTATION OF GPC FOR INDUSTRIAL PROCESSES 81 5.1 PLANT
MODEL 82 5.1.1 PLANT IDENTIFICATION: THE REACTION CURVE METHOD 82 5.2
THE DEAD TIME MULTIPLE OF THE SAMPLING TIME CASE 84 5.2.1 DISCRETE PLANT
MODEL 84 5.2.2 PROBLEM FORMULATION 85 5.2.3 COMPUTATION OF THE
CONTROLLER PARAMETERS 87 5.2.4 ROLE OF THE CONTROL-WEIGHTING FACTOR 89
5.2.5 IMPLEMENTATION ALGORITHM 90 5.2.6 AN IMPLEMENTATION EXAMPLE . { 90
5.3 THE DEAD TIME NONMULTIPLE OF THE SAMPLING TIME CASE 93 5.3.1
DISCRETE MODEL OF THE PLANT 93 5.3.2 CONTROLLER PARAMETERS 95 5.3.3
EXAMPLE 98 5.4 INTEGRATING PROCESSES 99 5.4.1 DERIVATION OF THE CONTROL
LAW 100 5.4.2 CONTROLLER PARAMETERS 102 5.4.3 EXAMPLE 104 5.5
CONSIDERATION OF RAMP SETPOINTS 105 5.5.1 EXAMPLE 108 5.6 COMPARISON
WITH STANDARD GPC 108 5.7 STABILITY ROBUSTNESS ANALYSIS ILL 5.7.1
STRUCTURED UNCERTAINTIES 112 5.7.2 UNSTRUCTURED UNCERTAINTIES 113 5.7.3
GENERAL COMMENTS 116 5.8 COMPOSITION CONTROL IN AN EVAPORATOR 117 5.8.1
DESCRIPTION OF THE PROCESS 117 5.8.2 OBTAINING THE LINEAR MODEL 119
CONTENTS XIX 5.8.3 CONTROLLER DESIGN 121 5.8.4 RESULTS 122 5.9 EXERCISES
125 6 MULTIVARIABLE MODEL PREDICTIVE CONTROL 127 6.1 DERIVATION OF
MULTIVARIABLE GPC 127 6.1.1 WHITE NOISE CASE 128 6.1.2 COLOURED NOISE
CASE 132 6.1.3 MEASURABLE DISTURBANCES 135 6.2 OBTAINING A MATRIX
FRACTION DESCRIPTION 138 6.2.1 TRANSFER MATRIX REPRESENTATION 138 6.2.2
PARAMETRIC IDENTIFICATION 141 6.3 STATE SPACE FORMULATION 143 6.3.1
MATRIX FRACTION AND STATE SPACE EQUIVALENCES 144 6.4 CASE STUDY: FLIGHT
CONTROL.- 147 6.5 CONVOLUTION MODELS FORMULATION 149 6.6 CASE STUDY:
CHEMICAL REACTOR 152 6.6.1 PLANT DESCRIPTION 152 6.6.2 OBTAINING THE
PLANT MODEL 154 6.6.3 CONTROL LAW 156 6.6.4 SIMULATION RESULTS 157 6.7
DEAD TIME PROBLEMS 157 6.8 CASE STUDY: DISTILLATION COLUMN 163 6.9
MULTIVARIABLE MPC AND TRANSMISSION ZEROS 166 6.9.1 SIMULATION EXAMPLE
170 6.9.2 TUNING MPC FOR PROCESSES WITH OUD ZEROS 173 6.10 EXERCISES
.;. 175 7 CONSTRAINED MODEL PREDICTIVE CONTROL 177 7.1 CONSTRAINTS AND
MPC 177 * 7.1.1 CONSTRAINT GENERAL FORM 183 7.1.2 ILLUSTRATIVE EXAMPLES
183 7.2 CONSTRAINTS AND OPTIMIZATION 187 7.3 REVISION OF MAIN QUADRATIC
PROGRAMMING ALGORITHMS 188 7.3.1 THE ACTIVE SET METHODS 189 7.3.2
FEASIBLE DIRECTION METHODS 191 7.3.3 INITIAL FEASIBLE POINT 192 7.3.4
PIVOTING METHODS 193 7.4 CONSTRAINTS HANDLING 196 7.4.1 SLEW RATE
CONSTRAINTS 196 7.4.2 AMPLITUDE CONSTRAINTS 198 7.4.3 OUTPUT CONSTRAINTS
199 7.4.4 CONSTRAINT REDUCTION 199 7.5 1-NORM 201 7.6 CASE STUDY: A
COMPRESSOR 203 XX CONTENTS 7.7 CONSTRAINT MANAGEMENT 206 7.7.1
FEASIBILITY 206 7.7.2 TECHNIQUES FOR IMPROVING FEASIBILITY 207 7.8
CONSTRAINED MPC AND STABILITY 209 7.9 MULTIOBJECTIVE MPC 212 7.9.1
PRIORIZATION OF OBJECTIVES 214 7.10 EXERCISES 216 8 ROBUST MODEL
PREDICTIVE CONTROL 217 8.1 PROCESS MODELS AND UNCERTAINTIES 218 8.1.1
TRUNCATED IMPULSE RESPONSE UNCERTAINTIES 219 8.1.2 MATRIX FRACTION
DESCRIPTION UNCERTAINTIES 220 8.1.3 GLOBAL UNCERTAINTIES 221 8.2
OBJECTIVE FUNCTIONS 224 8.2.1 QUADRATIC COST FUNCTION 225 8.2.2 OO-OO
NORM 226 8.2.3 1-NORM 228 8.3 ROBUSTNESS BY IMPOSING CONSTRAINTS 230 8.4
CONSTRAINT HANDLING 231 8.5 ILLUSTRATIVE EXAMPLES 232 8.5.1 BOUNDS ON
THE OUTPUT 232 8.5.2 UNCERTAINTIES IN THE GAIN 232 8.6 ROBUST MPC AND
LINEAR MATRIX INEQUALITIES 234 8.7 CLOSED-LOOP PREDICTIONS 237 8.7.1 AN
ILLUSTRATIVE EXAMPLE 238 8.7.2 INCREASING THE NUMBER OF DECISION
VARIABLES 239 8.7.3 DYNAMIC PROGRAMMING APPROACH 241 8.7.4 LINEAR
FEEDBACK 243 8.7.5 AN ILLUSTRATIVE EXAMPLE 245 8.8 EXERCISES 247 9
NONLINEAR MODEL PREDICTIVE CONTROL 249 9.1 NONLINEAR MPC VERSUS LINEAR
MPC 250 9.2 NONLINEAR MODELS 251 9.2.1 EMPIRICAL MODELS 252 9.2.2
FUNDAMENTAL MODELS 261 9.2.3 GREY-BOX MODELS 262 9.2.4 MODELLING EXAMPLE
262 9.3 SOLUTION OF THE NMPC PROBLEM 266 9.3.1 PROBLEM FORMULATION 267
9.3.2 SOLUTION 267 9.4 TECHNIQUES FOR NONLINEAR PREDICTIVE CONTROL 269
9.4.1 EXTENDED LINEAR MPC 269 9.4.2 LOCALMODELS 270 9.4.3 SUBOPTIMAL
NPMC 271 CONTENTS XXI 9.4.4 USE OF SHORT HORIZONS 271 9.4.5
DECOMPOSITION OF THE CONTROL SEQUENCE 272 9.4.6 FEEDBACK LINEARIZATION
274 9.4.7 MPC BASED ON VOLTERRA MODELS 274 9.4.8 NEURAL NETWORKS 277
9.4.9 COMMERCIAL PRODUCTS 277 9.5 STABILITY AND NONLINEAR MPC 279 9.6
CASE STUDY: PH NEUTRALIZATION PROCESS 282 9.6.1 PROCESS MODEL 284 9.6.2
RESULTS 285 9.7 EXERCISES 287 10 MODEL PREDICTIVE CONTROL AND HYBRID
SYSTEMS 28 9 10.1 HYBRID SYSTEM MODELLING 289 10.2 EXAMPLE: A JACKET
COOLED BATCH REACTOR 292 10.2.1 MIXED LOGICAL DYNAMICAL SYSTEMS 293
10.2.2 EXAMPLE 296 10.3 MODEL PREDICTIVE CONTROL OF MLD SYSTEMS 298
10.3.1 BRANCH AND BOUND MIXED INTEGER PROGRAMMING 299 10.3.2 AN
ILLUSTRATIVE EXAMPLE 302 10.4 PIECEWISE AFFINE SYSTEMS 303 10.4.1
EXAMPLE: TANK WITH DIFFERENT AREA SECTIONS 307 10.4.2 REACH SET,
CONTROLLABLE SET, AND STG ALGORITHM 308 10.5 EXERCISES 309 11 FAST
METHODS FOR IMPLEMENTING MODEL PREDICTIVE CONTROL 311 11.1 PIECEWISE
AFFINITY OF MPC 311 11.2 MPC AND MULTIPARAMETRIC PROGRAMMING 314 11.3
PIECEWISE IMPLEMENTATION OF MPC 316 11.3.1 ILLUSTRATIVE EXAMPLE: THE
DOUBLE INTEGRATOR 317 11.3.2 NONCONSTANT REFERENCES AND MEASURABLE
DISTURBANCES 320 11.3.3 EXAMPLE 321 11.3.4 THE 1-NORM AND OO-NORM CASES
322 11.4 FAST IMPLEMENTATION OF MPC FOR UNCERTAIN SYSTEMS 326 11.4.1
EXAMPLE 329 11.4.2 THE CLOSED-LOOP MIN-MAX MPC 330 11.5 APPROXIMATED
IMPLEMENTATION FOR MPC 333 11.6 FAST IMPLEMENTATION OF MPC AND DEAD TIME
CONSIDERATIONS . 334 11.7 EXERCISES 335 12 APPLICATIONS 337 12.1 SOLAR
POWER PLANT 337 12.1.1 SELFTUNING GPC CONTROL STRATEGY 339 12.1.2 GAIN
SCHEDULING GENERALIZED PREDICTIVE CONTROL 342 XXII CONTENTS 12.2 PILOT
PLANT 352 12.2.1 PLANT DESCRIPTION 352 12.2.2 PLANT CONTROL 353 12.2.3
FLOW CONTROL 354 12.2.4 TEMPERATURE CONTROL AT THE EXCHANGER OUTPUT 355
12.2.5 TEMPERATURE CONTROL IN THE TANK 357 12.2.6 LEVEL CONTROL 358
12.2.7 REMARKS 358 12.3 MODEL PREDICTIVE CONTROL IN A SUGAR REFINERY 359
12.4 OLIVE OIL MILL 362 12.4.1 PLANT DESCRIPTION 364 12.4.2 PROCESS
MODELLING AND VALIDATION 365 12.4.3 CONTROLLER SYNTHESIS 366 12.4.4
EXPERIMENTAL RESULTS 368 12.5 MOBILE ROBOT 371 12.5.1 PROBLEM DEFINITION
371 12.5.2 PREDICTION MODEL 372 12.5.3 PARAMETRIZATION OF THE DESIRED
PATH 374 12.5.4 POTENTIAL FUNCTION FOR CONSIDERING FIXED OBSTACLES .
374 12.5.5 THE NEURAL NETWORK APPROACH 376 12.5.6 TRAINING PHASE 378
12.5.7 RESULTS 379 A REVISION OF THE SIMPLEX METHOD 381 A.I EQUALITY
CONSTRAINTS 381 A.2 FINDING AN INITIAL SOLUTION 382 A.3 INEQUALITY
CONSTRAINTS 383 B DYNAMIC PROGRAMMING AND LINEAR QUADRATIC OPTIMAL
CONTROL . 385 B.I LINEAR QUADRATIC PROBLEM 385 B.2 INFINITE HORIZON
387 REFERENCES 389 INDEX 401 |
| any_adam_object | 1 |
| author | Camacho, E. F. 1950- Bordons, Carlos 1962- |
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| id | DE-604.BV019347531 |
| illustrated | Illustrated |
| indexdate | 2024-09-10T00:55:43Z |
| institution | BVB |
| isbn | 1852336943 9781852336943 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-012811774 |
| oclc_num | 249347516 |
| open_access_boolean | |
| owner | DE-29T DE-91G DE-BY-TUM DE-83 DE-91 DE-BY-TUM |
| owner_facet | DE-29T DE-91G DE-BY-TUM DE-83 DE-91 DE-BY-TUM |
| physical | XXII, 405 S. graph. Darst. |
| publishDate | 2004 |
| publishDateSearch | 2004 |
| publishDateSort | 2004 |
| publisher | Springer |
| record_format | marc |
| series2 | Advanced textbooks in control and signal processing |
| spelling | Camacho, E. F. 1950- Verfasser (DE-588)118087983 aut Model predictive control E. F. Camacho and C. Bordons 2. ed. London [u.a.] Springer 2004 XXII, 405 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advanced textbooks in control and signal processing Hier auch später erschienene, unveränderte Nachdrucke. - Literaturverz. S. 389 - 399 Predictive control Prädiktive Regelung (DE-588)4271836-3 gnd rswk-swf Prädiktive Regelung (DE-588)4271836-3 s DE-604 Bordons, Carlos 1962- Verfasser (DE-588)120071290 aut Erscheint auch als Online-Ausgabe 978-0-85729-398-5 HEBIS Datenaustausch Darmstadt application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=012811774&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Camacho, E. F. 1950- Bordons, Carlos 1962- Model predictive control Predictive control Prädiktive Regelung (DE-588)4271836-3 gnd |
| subject_GND | (DE-588)4271836-3 |
| title | Model predictive control |
| title_auth | Model predictive control |
| title_exact_search | Model predictive control |
| title_full | Model predictive control E. F. Camacho and C. Bordons |
| title_fullStr | Model predictive control E. F. Camacho and C. Bordons |
| title_full_unstemmed | Model predictive control E. F. Camacho and C. Bordons |
| title_short | Model predictive control |
| title_sort | model predictive control |
| topic | Predictive control Prädiktive Regelung (DE-588)4271836-3 gnd |
| topic_facet | Predictive control Prädiktive Regelung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=012811774&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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