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Advanced process identification and control

Author: Enso Ikonen; K Najim
Publisher: New York : M. Dekker, ©2002.
Series: Control engineering (Marcel Dekker, Inc.), 9.
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
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Document Type: Book
All Authors / Contributors: Enso Ikonen; K Najim
ISBN: 082470648X 9780824706487
OCLC Number: 48038084
Description: xiii, 310 pages : illustrations ; 26 cm.
Contents: I Identification --
1.1 Where are models needed? 3 --
1.2 What kinds of models are there? 4 --
1.2.1 Identification vs. first-principle modeling 7 --
1.3 Steps of identification 8 --
2 Linear Regression 13 --
2.1 Linear systems 13 --
2.2 Method of least squares 17 --
2.2.1 Derivation 18 --
2.2.2 Algorithm 20 --
2.2.3 Matrix representation 21 --
2.2.4 Properties 25 --
2.3 Recursive LS method 28 --
2.3.1 Derivation 28 --
2.3.2 Algorithm 31 --
2.3.3 A posteriori prediction error 33 --
2.4 RLS with exponential forgetting 34 --
2.4.1 Derivation 36 --
2.4.2 Algorithm 36 --
2.5 Kalman filter 37 --
2.5.1 Derivation 40 --
2.5.2 Algorithm 42 --
2.5.3 Kalman filter in parameter estimation 44 --
3 Linear Dynamic Systems 47 --
3.1 Transfer function 47 --
3.1.1 Finite impulse response 47 --
3.1.2 Transfer function 50 --
3.2 Deterministic disturbances 53 --
3.3 Stochastic disturbances 53 --
3.3.1 Offset in noise 55 --
3.3.2 Box-Jenkins 55 --
3.3.3 Autoregressive exogenous 57 --
3.3.4 Output error 59 --
3.3.5 Other structures 61 --
3.3.6 Diophantine equation 66 --
3.3.7 i-step-ahead predictions 69 --
4 Non-linear Systems 77 --
4.1 Basis function networks 78 --
4.1.1 Generalized basis function network 78 --
4.1.2 Basis functions 79 --
4.1.3 Function approximation 81 --
4.2 Non-linear black-box structures 82 --
4.2.1 Power series 83 --
4.2.2 Sigmoid neural networks 89 --
4.2.3 Nearest neighbor methods 95 --
4.2.4 Fuzzy inference systems 98 --
5 Non-linear Dynamic Structures 113 --
5.1 Non-linear time-series models 114 --
5.1.1 Gradients of non-linear time-series models 117 --
5.2 Linear dynamics and static non-linearities 120 --
5.2.1 Wiener systems 121 --
5.2.2 Hammerstein systems 124 --
5.3 Linear dynamics and steady-state models 125 --
5.3.1 Transfer function with unit steady-state gain 126 --
5.3.2 Wiener and Hammerstein predictors 126 --
5.3.3 Gradients of the Wiener and Hammerstein predictors 128 --
5.4.1 Inverse of Hammerstein and Wiener systems 133 --
5.4.2 ARX dynamics 134 --
6 Estimation of Parameters 137 --
6.1 Prediction error methods 138 --
6.1.1 First-order methods 139 --
6.1.2 Second-order methods 140 --
6.1.3 Step size 141 --
6.1.4 Levenberg-Marquardt algorithm 142 --
6.2 Optimization under constraints 149 --
6.2.1 Equality constraints 149 --
6.2.2 Inequality constraints 151 --
6.3 Guided random search methods 153 --
6.3.1 Stochastic learning automaton 155 --
6.4 Simulation examples 159 --
6.4.1 Pneumatic value: identification of a Wiener system 160 --
6.4.2 Binary distillation column: identification of Hammerstein model under constraints 167 --
6.4.3 Two-tank system: Wiener modeling under constraints 172 --
II Control --
7 Predictive Control 181 --
7.1 Introduction to model-based control 181 --
7.3 Linear quadratic predictive control 183 --
7.3.1 Plant and model 184 --
7.3.2 i-step ahead predictions 185 --
7.3.3 Cost function 186 --
7.3.5 Closed-loop behavior 188 --
7.4 Generalized predictive control 189 --
7.4.1 ARMAX/ARIMAX model 190 --
7.4.2 i-step-ahead predictions 191 --
7.4.3 Cost function 193 --
7.4.5 Closed-loop behavior 197 --
7.5 Simulation example 197 --
8 Multivariable Systems 203 --
8.1 Relative gain array method 204 --
8.1.2 Algorithm 206 --
8.2 Decoupling of interactions 209 --
8.2.1 Multivariable PI-controller 210 --
8.3 Multivariable predictive control 213 --
8.3.1 State-space model 213 --
8.3.2 i-step ahead predictions 216 --
8.3.3 Cost function 217 --
9 Time-varying and Non-linear Systems 223 --
9.1 Adaptive control 223 --
9.1.1 Types of adaptive control 225 --
9.2 Control of Hammerstein and Wiener systems 232 --
9.2.2 Second order Hammerstein systems 242 --
9.3 Control of non-linear systems 247 --
9.3.1. Predictive control 248 --
9.3.2 Sigmoid neural networks 248 --
9.3.3 Stochastic approximation 252 --
9.3.4 Control of a fermenter 254 --
9.3.5 Control of a tubular reactor 266 --
A State-Space Representation 273 --
A.1 State-space description 273 --
A.1.1 Control and observer canonical forms 274 --
A.2 Controllability and observability 275 --
A.2.1 Pole placement 276 --
A.2.2 Observers 280 --
B Fluidized Bed Combustion 283 --
B.1 Model of a bubbling fluidized bed 283 --
B.1.1 Bed 285 --
B.1.2 Freeboard 286 --
B.1.3 Power 286 --
B.1.4 Steady-state 287 --
B.2 Tuning of the model 288 --
B.2.1 Initial values 288 --
B.2.2 Steady-state behavior 288 --
B.2.3 Dynamics 290 --
B.2.4 Performance of the model 291 --
B.3 Linearization of the model 293.
Series Title: Control engineering (Marcel Dekker, Inc.), 9.
Responsibility: Enso Ikonen, Kaddour Najim.
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