Process modelling, identification, and control:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u. a.]
Springer
2007
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | Literaturverz. S. 465 - 473 |
| Beschreibung: | XXIV, 480 S. graph. Darst. 24 cm |
| ISBN: | 9783540719694 3540719695 |
Internformat
MARC
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| 100 | 1 | |a Mikleš, Ján |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Process modelling, identification, and control |c Ján Mikleš ; Miroslav Fikar |
| 264 | 1 | |a Berlin [u. a.] |b Springer |c 2007 | |
| 300 | |a XXIV, 480 S. |b graph. Darst. |c 24 cm | ||
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| 500 | |a Literaturverz. S. 465 - 473 | ||
| 650 | 4 | |a Control Industrial - Modelos Matemáticos | |
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Chemical process control |x Mathematical models | |
| 650 | 4 | |a Process control |x Mathematical models | |
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Datensatz im Suchindex
| _version_ | 1804137318857048064 |
|---|---|
| adam_text | JAN MIKLES YY MIROSLAV FIKAR
PROCESS MODELLING,
IDENTIFICATION, AND CONTROL
WITH 187 FIGURES AND 13 TABLES
A
^
J SPRINGER
2008
AGI-INFORMATION MANAGEMENT CONSULTANTS
MAY BE USED FOR PERSONAL PURPORSES ONLY OR BY
LIBRARIES ASSOCIATED TO
DANDELON.COM
NETWORK.
CONTENTS
1 INTRODUCTIO
N
1
1.1 TOPICS IN PROCESS CONTROL 1
1.2 AN EXAMPLE OF PROCESS CONTROL 3
1.2.1 PROCESS 3
1.2.2 STEADY-STATE : YY 3
1.2.3 PROCESS CONTROL . . 4
1.2.4 DYNAMICAL PROPERTIES OF THE PROCESS 4
1.2.5 FEEDBACK PROCESS CONTROL 5
1.2.6 TRANSIENT PERFORMANCE OF FEEDBACK CONTROL. . : 6
1.2.7 BLOCK DIAGRAM 8
1.2.8 FEEDFORWARD CONTROL . 9
1.3 DEVELOPMENT OF PROCESS CONTROL 10
1.4 REFERENCES
11
2 MATHEMATICA
L MODELLIN
G OF PROCESSE
S
13
2.1 GENERAL PRINCIPLES OF MODELLING 13
2.2 EXAMPLES OF DYNAMIC MATHEMATICAL MODELS 16
2.2.1 LIQUID STORAGE SYSTEMS 16
2.2.2 HEAT TRANSFER PROCESSES 19
2.2.3 MASS TRANSFER PROCESSES 26
2.2.4 CHEMICAL AND BIOCHEMICAL REACTORS 31
2.3 GENERAL PROCESS MODELS 33
2.4 LINEARISATION 39
2.5 SYSTEMS, CLASSIFICATION OF SYSTEMS 45
2.6 REFERENCES 46
2.7 EXERCISES 47
3 ANALYSI
S OF PROCES
S MODEL
S
51
3.1 THE LAPLACE TRANSFORM 51
3.1.1 DEFINITION OF THE LAPLACE TRANSFORM 51
3.1.2 LAPLACE TRANSFORMS OF COMMON FUNCTIONS 53
XIV CONTENTS
3.1.3 PROPERTIES OF THE LAPLACE TRANSFORM 57
3.1.4 INVERSE LAPLACE TRANSFORM 61
3.1.5 SOLUTION OF LINEAR DIFFERENTIAL EQUATIONS
BY LAPLACE TRANSFORM TECHNIQUES 62
3.2 STATE-SPACE PROCESS MODELS 66
3.2.1 CONCEPT OF STATE 67
3.2.2 SOLUTION OF STATE-SPACE EQUATIONS 67
3.2.3 CANONICAL TRANSFORMATION 70
3.2.4 STABILITY, CONTROLLABILITY, AND OBSERVABILITY
OF CONTINUOUS-TIME SYSTEMS 71
3.2.5 CANONICAL DECOMPOSITION 83
3.3 INPUT-OUTPUT PROCESS MODELS 84
3.3.1 SISO CONTINUOUS SYSTEMS WITH CONSTANT COEFFICIENTS . . 84
3.3.2 TRANSFER FUNCTIONS OF SYSTEMS WITH TIME DELAYS 93
3.3.3 ALGEBRA OF TRANSFER FUNCTIONS FOR SISO SYSTEMS 96
3.3.4 INPUT OUTPUT MODELS OF MIMO SYSTEMS - MATRIX
OF TRANSFER FUNCTIONS 100
3.3.5 BIBO STABILITY 103
3.3.6 TRANSFORMATION OF I/
O MODELS INTO STATE-SPACE MODELS 104
3.3.7 I/
O MODELS OF MIMO SYSTEMS - MATRIX FRACTION
DESCRIPTIONS 107
3.4 REFERENCES 112
3.5 EXERCISES 114
4 DYNAMICA
L BEHAVIOU
R OF PROCESSE
S
117
4.1 TIME RESPONSES OF LINEAR SYSTEMS TO UNIT IMPULSE AND UNIT
STEP 117
4.1.1 UNIT IMPULSE RESPONSE 117
4.1.2 UNIT STEP RESPONSE YY. . . 119
4.2 COMPUTER SIMULATIONS 125
4.2.1 THE EULER METHOD 126
4.2.2 THE RUNGE-KUTTA METHOD 127
4.2.3 RUNGE-KUTTA METHOD FOR A SYSTEM OF DIFFERENTIAL
EQUATIONS 129
4.2.4 TIME RESPONSES OF LIQUID STORAGE SYSTEMS 135
4.2.5 TIME RESPONSES OF CSTR .137
4.3 FREQUENCY ANALYSIS 145
4.3.1 RESPONSE OF THE HEAT EXCHANGER TO SINUSOIDAL INPUT
SIGNAL 145
4.3.2 DEFINITION OF FREQUENCY RESPONSES 149
4.3.3 FREQUENCY CHARACTERISTICS OF A FIRST ORDER SYSTEM 153
4.3.4 FREQUENCY CHARACTERISTICS OF A SECOND ORDER SYSTEM . . . 156
4.3.5 FREQUENCY CHARACTERISTICS OF AN INTEGRATOR 158
4.3.6 FREQUENCY CHARACTERISTICS OF SYSTEMS IN A SERIES 159
4.4 STATISTICAL CHARACTERISTICS OF DYNAMIC SYSTEMS 162
CONTENTS XV
4.4.1 FUNDAMENTALS OF PROBABILITY THEORY 162
4.4.2 RANDOM VARIABLES 163
4.4.3 STOCHASTIC PROCESSES 169
4.4.4 WHITE NOISE 176
4.4.5 RESPONSE OF A LINEAR SYSTEM T
O STOCHASTIC INPUT 178
4.4.6 FREQUENCY DOMAIN ANALYSIS OF A LINEAR SYSTEM
WITH STOCHASTIC INPUT 183
4.5 REFERENCES 185
4.6 EXERCISES 187
5 DISCRETE-TIM
E PROCES
S MODEL
S
189
5.1 COMPUTER CONTROLLED AND SAMPLED DAT
A SYSTEMS 189
5.2
Z -
TRANSFORM 195
5.3 DISCRETE-TIME TRANSFER FUNCTIONS 200
5.4 INPUT-OUTPUT DISCRETE-TIME MODELS - DIFFERENCE EQUATIONS.
. . 204
5.4.1 DIRECT DIGITAL CONTROL 205
5.5 STATE-SPACE DISCRETE-TIME MODELS 207
5.6 PROPERTIES OF DISCRETE-TIME SYSTEMS 211
5.6.1 STABILITY 211
5.6.2 CONTROLLABILITY 212
5.6.3 OBSERVABILITY 212
5.6.4 DISCRETE-TIME FEEDBACK SYSTEMS - CONTROL PERFORMANCE 212
5.7 EXAMPLES OF DISCRETE-TIME PROCESS MODELS 214
5.7.1 DISCRETE-TIME TANK MODEL 214
5.7.2 DISCRETE-TIME MODEL OF TWO TANKS IN SERIES 215
5.7.3 STEADY-STATE DISCRETE-TIME MODEL OF HEAT EXCHANGERS
IN SERIES 216
5.8 REFERENCES 217
5.9 EXERCISES 218
6 PROCES
S IDENTIFICATIO
N
221
6.1 INTRODUCTION 221
6.1.1 MODELS OF LINEAR DYNAMIC SYSTEMS 223
6.2 IDENTIFICATION FROM STEP RESPONSES 225
6.2.1 FIRST ORDER SYSTEM 226
6.2.2 UNDERDAMPED SECOND ORDER SYSTEM 227
6.2.3 SYSTEM OF A HIGHER ORDER 230
6.3 LEAST SQUARES METHODS 233
6.3.1 RECURSIVE LEAST SQUARES METHOD 235
6.3.2 MODIFICATIONS OF RECURSIVE LEAST SQUARES 241
6.3.3 IDENTIFICATION OF A CONTINUOUS-TIME TRANSFER FUNCTION . . 245
6.4 REFERENCES YY 250
6.5 EXERCISES 251
XVI CONTENTS
7 THE CONTROL PROBLE
M AND DESIG
N OF SIMPLE CONTROLLERS
...
. 253
7.1 CLOSED-LOOP SYSTEM 253
7.1.1 FEEDBACK CONTROL PROBLEM DEFINITION 255
7.2 STEADYRSTATE BEHAVIOUR 256
7.3 CONTROL PERFORMANCE INDICES 257
7.3.1 TIME DOMAIN 257
7.3.2 INTEGRAL CRITERIA 259
7.3.3 CONTROL QUALITY AND FREQUENCY INDICES 260
7.3.4 POLES 263
7.4 PID CONTROLLER 266
7.4.1 DESCRIPTION OF COMPONENTS 266
7.4.2 PID CONTROLLER STRUCTURES 274
7.4.3 SETPOINT WEIGHTING 275
7.4.4 SIMPLE RULES FOR CONTROLLER SELECTION 276
7.4.5 PRACTICAL ASPECTS 277
7.4.6 CONTROLLER TUNING 281
7.5 REFERENCES 294
7.6 EXERCISES 294
8 OPTIMAL PROCES
S CONTROL
297
8.1 PROBLEM OF OPTIMAL CONTROL AND PRINCIPLE OF MINIMUM 297
8.2 FEEDBACK OPTIMAL CONTROL 305
8.3 OPTIMAL TRACKING, SERVO PROBLEM, AND DISTURBANCE REJECTION . 317
8.3.1 TRACKING PROBLEM 318
8.3.2 SERVO PROBLEM 320
8.3.3 LQ CONTROL WITH INTEGRAL ACTION 321
8.4 DYNAMIC PROGRAMMING 322
8.4.1 CONTINUOUS-TIME SYSTEMS 322
8.4.2 DYNAMIC PROGRAMMING FOR DISCRETE-TIME SYSTEMS 329
8.4.3 OPTIMAL FEEDBACK 331
8.5 OBSERVERS AND STATE ESTIMATION 335
8.5.1 STATE OBSERVATION 335
8.5.2 KALMAN FILTER 337
8.6 ANALYSIS OF STATE FEEDBACK WITH OBSERVER AND POLYNOMIAL
POLE PLACEMENT . 340
8.6.1 PROPERTIES OF STATE FEEDBACK WITH OBSERVER 340
8.6.2 INPUT-OUTPUT INTERPRETATION OF STATE FEEDBACK
WITH OBSERVER 345
8.6.3 DIOPHANTINE EQUATIONS 351
8.6.4 POLYNOMIAL POLE PLACEMENT CONTROL DESIGN 354
8.6.5 INTEGRATING BEHAVIOUR OF CONTROLLER . . 355
8.6.6 POLYNOMIAL POLE PLACEMENT DESIGN FOR MULTIVARIABLE
SYSTEMS 361
8.7 THE YOULA-KUCERA PARAMETRISATION 365
8.7.1 FRACTIONAL REPRESENTATION 366
CONTENTS XVII
8.7.2 PARAMETRISATION OF STABILISING CONTROLLERS 368
8.7.3 PARAMETRISED CONTROLLER IN THE STATE-SPACE
REPRESENTATION 370
8.7.4 PARAMETRISATION OF TRANSFER FUNCTIONS OF THE
CLOSED-LOOP SYSTEM 371
8.7.5 DUAL PARAMETRISATION 372
8.7.6 PARAMETRISATION OF STABILISING CONTROLLERS FOR
MULTIVARIABLE SYSTEMS 374
8.7.7 PARAMETRISATION OF STABILISING CONTROLLERS FOR
DISCRETE-TIME SYSTEMS 375
8.8 OBSERVER LQ CONTROL, STATE-SPACE AND POLYNOMIAL
INTERPRETATIONS 380
8.8.1 POLYNOMIAL LQ CONTROL DESIGN WITH OBSERVER
FOR SINGLEVARIABLE SYSTEMS 381
8.8.2 POLYNOMIAL LQ DESIGN WITH STATE ESTIMATION
FOR MULTIVARIABLE SYSTEMS 385
8.9 LQG CONTROL, STATE-SPACE AND POLYNOMIAL INTERPRETATION 388
8.9.1 SINGLEVARIABLE POLYNOMIAL LQG CONTROL DESIGN 388
8.9.2 MULTIVARIABLE POLYNOMIAL LQG CONTROL DESIGN 392
8.10
H
2
OPTIMAL CONTROL ... ....
. : . .YY 394
8.11 REFERENCES
. .
; 397
8.12 EXERCISES 400
PREDICTIV
E CONTROL
403
9.1 INTRODUCTION
:
: 403
9.2 INGREDIENTS OF MBPC 404
9.2.1 MODELS YY YY 404
9.2.2 COST FUNCTION : . . 406
9.3 DERIVATION AND IMPLEMENTATION OF PREDICTIVE CONTROL 407
9.3.1 DERIVATION OF THE PREDICTOR 407
9.3.2 CALCULATION OF THE OPTIMAL CONTROL 409
9.3.3 CLOSED-LOOP RELATIONS 411
9.3.4 DERIVATION OF THE PREDICTOR FROM STATE-SPACE MODELS . . . 412
9.3.5 MULTIVARIABLE INPUT-OUTPUT CASE 416
9.3.6 IMPLEMENTATION 416
9.3.7 RELATION TO OTHER APPROACHES 418
9.3.8 CONTINUOUS-TIME APPROACHES 418
9.4 CONSTRAINED CONTROL 419
9.5 STABILITY RESULTS 420
9.5.1 STABILITY RESULTS IN GPC 421
9.5.2 TERMINAL CONSTRAINTS 421
9.5.3 INFINITE HORIZONS 425
9.5.4 FINITE TERMINAL PENALTY 425
9.6 EXPLICIT PREDICTIVE CONTROL 426
9.6.1 QUADRATIC PROGRAMMING DEFINITION 426
XVIII CONTENTS
9.6.2 EXPLICIT SOLUTION : 427
9.6.3 MULTI-PARAMETRIC TOOLBOX 429
9.7 TUNING 431
9.7.1 TUNING BASED ON THE FIRST ORDER MODEL 431
9.7.2 MULTIVARIABLE TUNING BASED ON THE FIRST ORDER MODEL . . 432
9.7.3 OUTPUT HORIZON TUNING 433
9.7.4 A TUNING 434
9.7.5 TUNING BASED ON MODEL FOLLOWING 434
9.7.6 THE
C
POLYNOMIAL 435
9.8 EXAMPLES 436
9.8.1 A LINEAR PROCESS
-. 436
9.8.2 NEURAL NETWORK BASED GPC 438
9.8.3 PH CONTROL 439
9.9 REFERENCES 442
9.10 EXERCISES 444
10 ADAPTIV
E CONTROL
445
10.1 DISCRETE-TIME SELF-TUNING CONTROL 446
10.2 CONTINUOUS-TIME SELF-TUNING CONTROL 447
10.3 EXAMPLES OF SELF-TUNING CONTROL 447
10.3.1 DISCRETE-TIME ADAPTIVE DEAD-BEAT CONTROL OF A
SECOND ORDER SYSTEM 448
10.3.2 CONTINUOUS-TIME ADAPTIVE LQ CONTROL OF A SECOND
ORDER SYSTEM 451
10.3.3 CONTINUOUS-TIME ADAPTIVE MIMO POLE PLACEMENT
CONTROL 456
10.3.4 ADAPTIVE CONTROL OF A TUBULAR REACTOR 459
10.4 REFERENCES 463
REFERENCES
465
INDE
X
475
|
| adam_txt |
JAN MIKLES YY MIROSLAV FIKAR
PROCESS MODELLING,
IDENTIFICATION, AND CONTROL
WITH 187 FIGURES AND 13 TABLES
A \
^
J SPRINGER
2008
AGI-INFORMATION MANAGEMENT CONSULTANTS
MAY BE USED FOR PERSONAL PURPORSES ONLY OR BY
LIBRARIES ASSOCIATED TO
DANDELON.COM
NETWORK.
CONTENTS
1 INTRODUCTIO
N
1
1.1 TOPICS IN PROCESS CONTROL 1
1.2 AN EXAMPLE OF PROCESS CONTROL 3
1.2.1 PROCESS 3
1.2.2 STEADY-STATE : YY 3
1.2.3 PROCESS CONTROL . . 4
1.2.4 DYNAMICAL PROPERTIES OF THE PROCESS 4
1.2.5 FEEDBACK PROCESS CONTROL 5
1.2.6 TRANSIENT PERFORMANCE OF FEEDBACK CONTROL. . : 6
1.2.7 BLOCK DIAGRAM 8
1.2.8 FEEDFORWARD CONTROL .' 9
1.3 DEVELOPMENT OF PROCESS CONTROL 10
1.4 REFERENCES
11
2 MATHEMATICA
L MODELLIN
G OF PROCESSE
S
13
2.1 GENERAL PRINCIPLES OF MODELLING 13
2.2 EXAMPLES OF DYNAMIC MATHEMATICAL MODELS 16
2.2.1 LIQUID STORAGE SYSTEMS 16
2.2.2 HEAT TRANSFER PROCESSES 19
2.2.3 MASS TRANSFER PROCESSES 26
2.2.4 CHEMICAL AND BIOCHEMICAL REACTORS 31
2.3 GENERAL PROCESS MODELS 33
2.4 LINEARISATION 39
2.5 SYSTEMS, CLASSIFICATION OF SYSTEMS 45
2.6 REFERENCES 46
2.7 EXERCISES 47
3 ANALYSI
S OF PROCES
S MODEL
S
51
3.1 THE LAPLACE TRANSFORM 51
3.1.1 DEFINITION OF THE LAPLACE TRANSFORM 51
3.1.2 LAPLACE TRANSFORMS OF COMMON FUNCTIONS 53
XIV CONTENTS
3.1.3 PROPERTIES OF THE LAPLACE TRANSFORM 57
3.1.4 INVERSE LAPLACE TRANSFORM 61
3.1.5 SOLUTION OF LINEAR DIFFERENTIAL EQUATIONS
BY LAPLACE TRANSFORM TECHNIQUES 62
3.2 STATE-SPACE PROCESS MODELS 66
3.2.1 CONCEPT OF STATE 67
3.2.2 SOLUTION OF STATE-SPACE EQUATIONS 67
3.2.3 CANONICAL TRANSFORMATION 70
3.2.4 STABILITY, CONTROLLABILITY, AND OBSERVABILITY
OF CONTINUOUS-TIME SYSTEMS 71
3.2.5 CANONICAL DECOMPOSITION 83
3.3 INPUT-OUTPUT PROCESS MODELS 84
3.3.1 SISO CONTINUOUS SYSTEMS WITH CONSTANT COEFFICIENTS . . 84
3.3.2 TRANSFER FUNCTIONS OF SYSTEMS WITH TIME DELAYS 93
3.3.3 ALGEBRA OF TRANSFER FUNCTIONS FOR SISO SYSTEMS 96
3.3.4 INPUT OUTPUT MODELS OF MIMO SYSTEMS - MATRIX
OF TRANSFER FUNCTIONS 100
3.3.5 BIBO STABILITY 103
3.3.6 TRANSFORMATION OF I/
O MODELS INTO STATE-SPACE MODELS 104
3.3.7 I/
O MODELS OF MIMO SYSTEMS - MATRIX FRACTION
DESCRIPTIONS 107
3.4 REFERENCES 112
3.5 EXERCISES 114
4 DYNAMICA
L BEHAVIOU
R OF PROCESSE
S
117
4.1 TIME RESPONSES OF LINEAR SYSTEMS TO UNIT IMPULSE AND UNIT
STEP 117
4.1.1 UNIT IMPULSE RESPONSE 117
4.1.2 UNIT STEP RESPONSE YY. . . 119
4.2 COMPUTER SIMULATIONS 125
4.2.1 THE EULER METHOD 126
4.2.2 THE RUNGE-KUTTA METHOD 127
4.2.3 RUNGE-KUTTA METHOD FOR A SYSTEM OF DIFFERENTIAL
EQUATIONS 129
4.2.4 TIME RESPONSES OF LIQUID STORAGE SYSTEMS 135
4.2.5 TIME RESPONSES OF CSTR .137
4.3 FREQUENCY ANALYSIS 145
4.3.1 RESPONSE OF THE HEAT EXCHANGER TO SINUSOIDAL INPUT
SIGNAL 145
4.3.2 DEFINITION OF FREQUENCY RESPONSES 149
4.3.3 FREQUENCY CHARACTERISTICS OF A FIRST ORDER SYSTEM 153
4.3.4 FREQUENCY CHARACTERISTICS OF A SECOND ORDER SYSTEM . . . 156
4.3.5 FREQUENCY CHARACTERISTICS OF AN INTEGRATOR 158
4.3.6 FREQUENCY CHARACTERISTICS OF SYSTEMS IN A SERIES 159
4.4 STATISTICAL CHARACTERISTICS OF DYNAMIC SYSTEMS 162
CONTENTS XV
4.4.1 FUNDAMENTALS OF PROBABILITY THEORY 162
4.4.2 RANDOM VARIABLES 163
4.4.3 STOCHASTIC PROCESSES 169
4.4.4 WHITE NOISE 176
4.4.5 RESPONSE OF A LINEAR SYSTEM T
O STOCHASTIC INPUT 178
4.4.6 FREQUENCY DOMAIN ANALYSIS OF A LINEAR SYSTEM
WITH STOCHASTIC INPUT 183
4.5 REFERENCES 185
4.6 EXERCISES 187
5 DISCRETE-TIM
E PROCES
S MODEL
S
189
5.1 COMPUTER CONTROLLED AND SAMPLED DAT
A SYSTEMS 189
5.2
Z -
TRANSFORM 195
5.3 DISCRETE-TIME TRANSFER FUNCTIONS 200
5.4 INPUT-OUTPUT DISCRETE-TIME MODELS - DIFFERENCE EQUATIONS.
. . 204
5.4.1 DIRECT DIGITAL CONTROL 205
5.5 STATE-SPACE DISCRETE-TIME MODELS 207
5.6 PROPERTIES OF DISCRETE-TIME SYSTEMS 211
5.6.1 STABILITY 211
5.6.2 CONTROLLABILITY 212
5.6.3 OBSERVABILITY 212
5.6.4 DISCRETE-TIME FEEDBACK SYSTEMS - CONTROL PERFORMANCE 212
5.7 EXAMPLES OF DISCRETE-TIME PROCESS MODELS 214
5.7.1 DISCRETE-TIME TANK MODEL 214
5.7.2 DISCRETE-TIME MODEL OF TWO TANKS IN SERIES 215
5.7.3 STEADY-STATE DISCRETE-TIME MODEL OF HEAT EXCHANGERS
IN SERIES 216
5.8 REFERENCES 217
5.9 EXERCISES 218
6 PROCES
S IDENTIFICATIO
N
221
6.1 INTRODUCTION 221
6.1.1 MODELS OF LINEAR DYNAMIC SYSTEMS 223
6.2 IDENTIFICATION FROM STEP RESPONSES 225
6.2.1 FIRST ORDER SYSTEM 226
6.2.2 UNDERDAMPED SECOND ORDER SYSTEM 227
6.2.3 SYSTEM OF A HIGHER ORDER 230
6.3 LEAST SQUARES METHODS 233
6.3.1 RECURSIVE LEAST SQUARES METHOD 235
6.3.2 MODIFICATIONS OF RECURSIVE LEAST SQUARES 241
6.3.3 IDENTIFICATION OF A CONTINUOUS-TIME TRANSFER FUNCTION . . 245
6.4 REFERENCES YY 250
6.5 EXERCISES 251
XVI CONTENTS
7 THE CONTROL PROBLE
M AND DESIG
N OF SIMPLE CONTROLLERS
.
. 253
7.1 CLOSED-LOOP SYSTEM 253
7.1.1 FEEDBACK CONTROL PROBLEM DEFINITION 255
7.2 STEADYRSTATE BEHAVIOUR 256
7.3 CONTROL PERFORMANCE INDICES 257
7.3.1 TIME DOMAIN 257
7.3.2 INTEGRAL CRITERIA 259
7.3.3 CONTROL QUALITY AND FREQUENCY INDICES 260
7.3.4 POLES 263
7.4 PID CONTROLLER 266
7.4.1 DESCRIPTION OF COMPONENTS 266
7.4.2 PID CONTROLLER STRUCTURES 274
7.4.3 SETPOINT WEIGHTING 275
7.4.4 SIMPLE RULES FOR CONTROLLER SELECTION 276
7.4.5 PRACTICAL ASPECTS 277
7.4.6 CONTROLLER TUNING 281
7.5 REFERENCES 294
7.6 EXERCISES 294
8 OPTIMAL PROCES
S CONTROL
297
8.1 PROBLEM OF OPTIMAL CONTROL AND PRINCIPLE OF MINIMUM 297
8.2 FEEDBACK OPTIMAL CONTROL 305
8.3 OPTIMAL TRACKING, SERVO PROBLEM, AND DISTURBANCE REJECTION . 317
8.3.1 TRACKING PROBLEM 318
8.3.2 SERVO PROBLEM 320
8.3.3 LQ CONTROL WITH INTEGRAL ACTION 321
8.4 DYNAMIC PROGRAMMING 322
8.4.1 CONTINUOUS-TIME SYSTEMS 322
8.4.2 DYNAMIC PROGRAMMING FOR DISCRETE-TIME SYSTEMS 329
8.4.3 OPTIMAL FEEDBACK 331
8.5 OBSERVERS AND STATE ESTIMATION 335
8.5.1 STATE OBSERVATION 335
8.5.2 KALMAN FILTER 337
8.6 ANALYSIS OF STATE FEEDBACK WITH OBSERVER AND POLYNOMIAL
POLE PLACEMENT '. 340
8.6.1 PROPERTIES OF STATE FEEDBACK WITH OBSERVER 340
8.6.2 INPUT-OUTPUT INTERPRETATION OF STATE FEEDBACK
WITH OBSERVER 345
8.6.3 DIOPHANTINE EQUATIONS 351
8.6.4 POLYNOMIAL POLE PLACEMENT CONTROL DESIGN 354
8.6.5 INTEGRATING BEHAVIOUR OF CONTROLLER . . 355
8.6.6 POLYNOMIAL POLE PLACEMENT DESIGN FOR MULTIVARIABLE
SYSTEMS 361
8.7 THE YOULA-KUCERA PARAMETRISATION 365
8.7.1 FRACTIONAL REPRESENTATION 366
CONTENTS XVII
8.7.2 PARAMETRISATION OF STABILISING CONTROLLERS 368
8.7.3 PARAMETRISED CONTROLLER IN THE STATE-SPACE
REPRESENTATION 370
8.7.4 PARAMETRISATION OF TRANSFER FUNCTIONS OF THE
CLOSED-LOOP SYSTEM 371
8.7.5 DUAL PARAMETRISATION 372
8.7.6 PARAMETRISATION OF STABILISING CONTROLLERS FOR
MULTIVARIABLE SYSTEMS 374
8.7.7 PARAMETRISATION OF STABILISING CONTROLLERS FOR
DISCRETE-TIME SYSTEMS 375
8.8 OBSERVER LQ CONTROL, STATE-SPACE AND POLYNOMIAL
INTERPRETATIONS 380
8.8.1 POLYNOMIAL LQ CONTROL DESIGN WITH OBSERVER
FOR SINGLEVARIABLE SYSTEMS 381
8.8.2 POLYNOMIAL LQ DESIGN WITH STATE ESTIMATION
FOR MULTIVARIABLE SYSTEMS 385
8.9 LQG CONTROL, STATE-SPACE AND POLYNOMIAL INTERPRETATION 388
8.9.1 SINGLEVARIABLE POLYNOMIAL LQG CONTROL DESIGN 388
8.9.2 MULTIVARIABLE POLYNOMIAL LQG CONTROL DESIGN 392
8.10
H
2
OPTIMAL CONTROL .'.
. : . .YY 394
8.11 REFERENCES
.'.
; 397
8.12 EXERCISES 400
PREDICTIV
E CONTROL
403
9.1 INTRODUCTION
:
: 403
9.2 INGREDIENTS OF MBPC 404
9.2.1 MODELS YY YY 404
9.2.2 COST FUNCTION : . . 406
9.3 DERIVATION AND IMPLEMENTATION OF PREDICTIVE CONTROL 407
9.3.1 DERIVATION OF THE PREDICTOR 407
9.3.2 CALCULATION OF THE OPTIMAL CONTROL 409
9.3.3 CLOSED-LOOP RELATIONS 411
9.3.4 DERIVATION OF THE PREDICTOR FROM STATE-SPACE MODELS . . . 412
9.3.5 MULTIVARIABLE INPUT-OUTPUT CASE 416
9.3.6 IMPLEMENTATION 416
9.3.7 RELATION TO OTHER APPROACHES 418
9.3.8 CONTINUOUS-TIME APPROACHES 418
9.4 CONSTRAINED CONTROL 419
9.5 STABILITY RESULTS 420
9.5.1 STABILITY RESULTS IN GPC 421
9.5.2 TERMINAL CONSTRAINTS 421
9.5.3 INFINITE HORIZONS 425
9.5.4 FINITE TERMINAL PENALTY 425
9.6 EXPLICIT PREDICTIVE CONTROL 426
9.6.1 QUADRATIC PROGRAMMING DEFINITION 426
XVIII CONTENTS
9.6.2 EXPLICIT SOLUTION : 427
9.6.3 MULTI-PARAMETRIC TOOLBOX 429
9.7 TUNING 431
9.7.1 TUNING BASED ON THE FIRST ORDER MODEL 431
9.7.2 MULTIVARIABLE TUNING BASED ON THE FIRST ORDER MODEL . . 432
9.7.3 OUTPUT HORIZON TUNING 433
9.7.4 A TUNING 434
9.7.5 TUNING BASED ON MODEL FOLLOWING 434
9.7.6 THE
C
POLYNOMIAL 435
9.8 EXAMPLES 436
9.8.1 A LINEAR PROCESS
-. 436
9.8.2 NEURAL NETWORK BASED GPC 438
9.8.3 PH CONTROL 439
9.9 REFERENCES 442
9.10 EXERCISES 444
10 ADAPTIV
E CONTROL
445
10.1 DISCRETE-TIME SELF-TUNING CONTROL 446
10.2 CONTINUOUS-TIME SELF-TUNING CONTROL 447
10.3 EXAMPLES OF SELF-TUNING CONTROL 447
10.3.1 DISCRETE-TIME ADAPTIVE DEAD-BEAT CONTROL OF A
SECOND ORDER SYSTEM 448
10.3.2 CONTINUOUS-TIME ADAPTIVE LQ CONTROL OF A SECOND
ORDER SYSTEM 451
10.3.3 CONTINUOUS-TIME ADAPTIVE MIMO POLE PLACEMENT
CONTROL 456
10.3.4 ADAPTIVE CONTROL OF A TUBULAR REACTOR 459
10.4 REFERENCES 463
REFERENCES
465
INDE
X
475 |
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| author | Mikleš, Ján Fikar, Miroslav |
| author_facet | Mikleš, Ján Fikar, Miroslav |
| author_role | aut aut |
| author_sort | Mikleš, Ján |
| author_variant | j m jm m f mf |
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| callnumber-first | T - Technology |
| callnumber-label | TS156 |
| callnumber-raw | TS156.8 |
| callnumber-search | TS156.8 |
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| callnumber-subject | TS - Manufactures |
| classification_rvk | ZQ 5000 |
| ctrlnum | (OCoLC)154712275 (DE-599)DNB983974497 |
| dewey-full | 629.8312 629.8 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 629 - Other branches of engineering |
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| dewey-search | 629.8312 629.8 |
| dewey-sort | 3629.8312 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Maschinenbau / Maschinenwesen Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| discipline_str_mv | Maschinenbau / Maschinenwesen Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| format | Book |
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| id | DE-604.BV023074175 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:34:19Z |
| indexdate | 2024-07-09T21:10:24Z |
| institution | BVB |
| isbn | 9783540719694 3540719695 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016277291 |
| oclc_num | 154712275 |
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| owner_facet | DE-898 DE-BY-UBR DE-1051 DE-634 |
| physical | XXIV, 480 S. graph. Darst. 24 cm |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Springer |
| record_format | marc |
| spelling | Mikleš, Ján Verfasser aut Process modelling, identification, and control Ján Mikleš ; Miroslav Fikar Berlin [u. a.] Springer 2007 XXIV, 480 S. graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Literaturverz. S. 465 - 473 Control Industrial - Modelos Matemáticos Mathematisches Modell Chemical process control Mathematical models Process control Mathematical models Reglerentwurf (DE-588)4177447-4 gnd rswk-swf Prozessregelung (DE-588)4222919-4 gnd rswk-swf Prozessmodell (DE-588)4237203-3 gnd rswk-swf Systemidentifikation (DE-588)4121753-6 gnd rswk-swf Prozessregelung (DE-588)4222919-4 s Prozessmodell (DE-588)4237203-3 s Systemidentifikation (DE-588)4121753-6 s Reglerentwurf (DE-588)4177447-4 s DE-604 Fikar, Miroslav Verfasser aut http://www.agi-imc.de/intelligentSEARCH.nsf/alldocs/DC94FE8878BE263AC12572C600752EB2/$File/420000194626.PDF?OpenElement Inhaltsverzeichnis DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016277291&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Mikleš, Ján Fikar, Miroslav Process modelling, identification, and control Control Industrial - Modelos Matemáticos Mathematisches Modell Chemical process control Mathematical models Process control Mathematical models Reglerentwurf (DE-588)4177447-4 gnd Prozessregelung (DE-588)4222919-4 gnd Prozessmodell (DE-588)4237203-3 gnd Systemidentifikation (DE-588)4121753-6 gnd |
| subject_GND | (DE-588)4177447-4 (DE-588)4222919-4 (DE-588)4237203-3 (DE-588)4121753-6 |
| title | Process modelling, identification, and control |
| title_auth | Process modelling, identification, and control |
| title_exact_search | Process modelling, identification, and control |
| title_exact_search_txtP | Process modelling, identification, and control |
| title_full | Process modelling, identification, and control Ján Mikleš ; Miroslav Fikar |
| title_fullStr | Process modelling, identification, and control Ján Mikleš ; Miroslav Fikar |
| title_full_unstemmed | Process modelling, identification, and control Ján Mikleš ; Miroslav Fikar |
| title_short | Process modelling, identification, and control |
| title_sort | process modelling identification and control |
| topic | Control Industrial - Modelos Matemáticos Mathematisches Modell Chemical process control Mathematical models Process control Mathematical models Reglerentwurf (DE-588)4177447-4 gnd Prozessregelung (DE-588)4222919-4 gnd Prozessmodell (DE-588)4237203-3 gnd Systemidentifikation (DE-588)4121753-6 gnd |
| topic_facet | Control Industrial - Modelos Matemáticos Mathematisches Modell Chemical process control Mathematical models Process control Mathematical models Reglerentwurf Prozessregelung Prozessmodell Systemidentifikation |
| url | http://www.agi-imc.de/intelligentSEARCH.nsf/alldocs/DC94FE8878BE263AC12572C600752EB2/$File/420000194626.PDF?OpenElement http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016277291&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT miklesjan processmodellingidentificationandcontrol AT fikarmiroslav processmodellingidentificationandcontrol |
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