Advanced Control of Industrial Processes: Structures and Algorithms

Portada
Springer Science & Business Media, 23 feb. 2007 - 332 páginas

Advanced Control of Industrial Processes presents the concepts and algorithms of advanced industrial process control and on-line optimisation within the framework of a multilayer structure. Relatively simple unconstrained nonlinear fuzzy control algorithms and linear predictive control laws are covered, as are more involved constrained and nonlinear model predictive control (MPC) algorithms and on-line set-point optimisation techniques.

The major topics and key features are:

• Development and discussion of a multilayer control structure with interrelated direct control, set-point control and optimisation layers, as a framework for the subject of the book.

• Systematic presentation and stability analysis of fuzzy feedback control algorithms in Takagi-Sugeno structures for state-space and input-output models, in discrete and continuous time, presented as natural generalisations of well-known practical linear control laws (like the PID law) to the nonlinear case.

• Thorough derivation of most practical MPC algorithms with linear process models (dynamic matrix control, generalised predictive control, and with state-space models), both as fast explicit control laws (also embedded into appropriate structures to cope with process input constraints), and as more involved numerical constrained MPC algorithms.

• Development of computationally effective MPC structures for nonlinear process models, utilising on-line model linearisations and fuzzy reasoning.

• General presentation of the subject of on-line set-point improvement and optimisation, together with iterative algorithms capable of coping with uncertainty in process models and disturbance estimates.

• Complete theoretical stability analysis of fuzzy Takagi-Sugeno control systems, discussion of stability and feasibility issues of MPC algorithms as well as of tuning aspects, discussion of applicability and convergence of on-line set-point improvement algorithms.

• Thorough illustration of the methodologies and algorithms by worked examples in the text.

• Control and set-point optimisation algorithms together with results of simulations based on industrial process models, stemming primarily from the petrochemical and chemical industries.

Starting from important and well-known techniques (supplemented with the original work of the author), the book includes recent research results mainly concerned with nonlinear advanced feedback control and set-point optimisation. It is addressed to readers interested in the important basic mechanisms of advanced control, including engineers and practitioners, as well as to research staff and postgraduate students.

 

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Índice

Multilayer Control Structure1
1
12 Control Objectives
2
13 Control Layers
4
14 Process Modeling in a Multilayer Structure
9
15 Optimization Layer
24
16 Supervision Diagnosis Adaptation
29
Modelbased Fuzzy Control
33
21 TakagiSugeno TS Type Fuzzy Systems
35
334 GPC Algorithm in Numerical Version
170
34 MPC with Statespace Process Model
176
341 Algorithms with Measured State
177
342 Algorithms with Estimated State
186
343 Explicit Piecewiseaffine MPCS Constrained Controller
194
35 Nonlinear Predictive Control Algorithms
197
352 MPCNO MPC with Nonlinear Optimization
198
353 MPCNSL MPC Nonlinear with Successive Linearization
200

212 Fuzzy Reasoning
39
213 Design of TS Fuzzy Models
46
214 TS System as a Fuzzy Neural Network
48
22 Discretetime TS Fuzzy Control
55
221 Discrete TS Fuzzy Statefeedback Controllers
58
222 Discrete TS Fuzzy Outputfeedback Controllers
71
23 Continuoustime TS Fuzzy Control
83
231 Continuous TS Fuzzy Statefeedback Controllers
84
232 Continuous TS Fuzzy Outputfeedback Controllers
95
24 Feedforward Compensation Automatic Tuning
103
Modelbased Predictive Control
107
32 Dynamic Matrix Control DMC Algorithm
118
322 Unconstrained Explicit DMC Algorithm
123
323 Constraining the Controller Output by Projection
135
324 DMC Algorithm in Numerical Version
139
325 Model Uncertainty Disturbances
142
33 Generalized Predictive Control GPC Algorithm
149
331 GPC Algorithm for a SISO Process
151
332 GPC with Constant Output Disturbance Prediction
166
333 GPC Algorithm for a MIMO Process
168
354 MPCNPL MPC with Nonlinear Prediction and Linearization
202
355 MPC Algorithms Using Artificial Neural Networks
211
356 Comparative Simulation Studies
218
357 Fuzzy MPC FMPC Numerical Algorithms
228
358 Fuzzy MPC FMPC Explicit Unconstrained Algorithms
242
36 Stability Constraint Handling Parameter Tuning
249
362 Feasibility of Constraint Sets Parameter Tuning
262
Setpoint Optimization
272
42 Steadystate Optimization for Model Predictive Control
277
421 MPC Steadystate Target Optimization
280
422 Integrated Approach to MPC and Steadystate Optimization
287
423 Adaptive MPC Integrated with Steadystate Optimization
289
424 Comparative Example Results
292
43 Measurementbased Iterative Setpoint Optimization under Uncertainty
300
ISOPE
301
432 ISOPE for Problems with Output Constraints
314
References
317
Index
327
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Sobre el autor (2007)

Professor Piotr Tatjewski graduated in control engineering from Warsaw University of Technology, Poland in 1972. He obtained his Ph.D. in 1976, and his D.Sc. in 1988, both in control engineering. The first period of his academic career was spent in the group of professor Wladyslaw Findeisen, working on control and optimisation of complex processes, on projects for process industry and concerning water distribution networks. In 1986 he was given a SERC fellowship for research on on-line set-point optimisation and spent 6 months as a research fellow of the Control Engineering Center of The City University London. Since 1990 he has been head of the Process Control Group at the Institute of Control and Computation Engineering, Warsaw University of Technology, leading many research and application projects (including that on advanced control for the Polish Petroleum Company). Professor Tatjewski spent the academic year 1992/1993 at the University of Birmingham in a project supported by the EU TEMPUS program. In 1993 he took up a professorship at Warsaw University of Technology and in 1996 the directorship of the Institute of Control and Computation Engineering there. In 2004 he was elected a member of the Committee of Automation and Robotics of the Polish Academy of Sciences. He works for the Ministry of Education and Science as an expert on standards in university education.

An experienced researcher and teacher, Professor Tatjewski has published 4 books (two in English) and over 20 journal papers as author or co-author and participated in numerous international conferences. He has taught many courses, including Feedback Control, Digital Feedback Control, Hierarchical Control and Optimisation, and Advanced Control. Between 1994 and 1997 he was the coordinator and contractor of the large EU project Information Technology for Control and Decision Support – Curriculum Development within the TEMPUS program, co-ordinating work in 4 major Polish technical universities and 11 universities from EU countries (Germany, France, UK, Italy, Spain and Denmark). His main research interests are multilayer-multilevel process control and optimisation, fuzzy modelling and control, model predictive control of linear and nonlinear processes, soft computing methods, control and optimisation of large-scale processes.

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