Techniques for Adaptive Control

Vance VanDoren Ph.D. P.E.9780750674959, 0-7506-7495-4

Techniques for Adaptive Control compiles chapters from a team of expert contributors that allow readers to gain a perspective into a number of different approaches to adaptive control. In order to do this, each contributor provides an overview of a particular product, how it works, and reasons why a user would want it as well as an in depth explanation of their particular method.This is one of the latest technologies to emerge in the instrumentation and control field. These latest control methodologies offer a means to revolutionize plant and process efficiency, response time and profitability by allowing a process to be regulated by a form of rule-based AI, without human intervention.Rather than the common academic-based approach that books on this subject generally take, the contributions here outline practical applications of adaptive control technology allowing for a real look inside the industry and the new technologies available.* Written by a team of contributors from the industry’s best-known product manufacturers and software developers* Provides real insight into new technologies available in the industry* Outlines practical applications of adaptive control technology

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Table of contents :
Contents……Page 5
Contributors……Page 9
Preface……Page 10
Terminology……Page 12
Contents……Page 13
Commercial adaptive controllers……Page 14
Problems with PID……Page 15
Advantages of adaptive control……Page 16
The ideal adaptive controller……Page 17
Basic concepts……Page 19
Modern alternatives……Page 20
Curve-fitting challenges……Page 21
More challenges……Page 22
Still more challenges……Page 23
A model-free technique……Page 24
Pros and cons……Page 25
Fuzzy logic……Page 26
More pros and cons……Page 27
Pick one……Page 28
Assumptions……Page 29
Other considerations……Page 31
References……Page 32
1 Adaptive tuning methods of the Foxboro I/A system……Page 34
Controller structure……Page 37
Minimum variance control……Page 42
Control by minimizing sensitivity to process uncertainty……Page 44
Algebraic controller design for load rejection and shaped transient response……Page 46
Algebraic tuning of a controller with deadtime……Page 50
Robust adaption of feedback controller gain scheduling……Page 55
Feedforward control……Page 58
Adaption of feedforward load compensators……Page 59
Conclusion 1……Page 63
References 1……Page 64
Suggested reading 1……Page 65
2 The exploitation of adaptive modeling in the model predictive control of Connoisseur……Page 66
Model structures……Page 69
Issues for identification……Page 73
Adaptive modeling……Page 80
Other methods……Page 84
Simulated case study on a fluid catalytic cracking unit……Page 87
Conclusion 2……Page 107
References 2……Page 108
3 Adaptive predictive regulartory control with Brainwave……Page 110
The Laguerre modeling method……Page 112
Building the adaptive predictive controller based on a Laguerre state space model……Page 116
A Laguerre-based controller for integrating systems……Page 121
Practical issues for implementing adaptive predictive controllers……Page 125
Simulation examples……Page 131
Industrial application examples……Page 136
References 3……Page 153
Concept of MFA control……Page 156
Single-loop MFA control system……Page 160
Multivariable MFA control system……Page 169
Anti-delay MFA control system……Page 181
MFA cascade control system……Page 184
Feedforward MFA control……Page 188
Nonlinear MFA control……Page 191
Robust MFA control……Page 194
MFA control methodology……Page 197
The inside of MFA……Page 200
Case studies 4……Page 202
Suggested reading 4……Page 212
5 Expert-based adaptive control: Controlsoft’s Intune adaptive and diagnostic software……Page 214
On-demand tuning and adaptive tuning……Page 215
History and milestone literature……Page 216
Adaptive control structure and underlying principles……Page 218
Identification-based adaptive control……Page 220
Expert-based adaptive control: ControlSoft’s Intune……Page 228
Concluding observations 5……Page 241
References 5……Page 243
6 Knowledgescape, an object orientated real-time adaptive modeling and optimization expert control system for the process……Page 0
Intelligent software objects and their use in KnowledgeScope……Page 246
Artificial intelligence and process control……Page 250
Neural networks……Page 263
Genetic algorithms……Page 267
Documenting the performance of Intelligence Systems……Page 269
Putting it all together: combining intelligent technologies for process control……Page 272
Results: using intelligent control in the mineral-processing industry……Page 275
Conclusion 6……Page 277
References 6……Page 278
Appendix: table of artificial reference texts……Page 279
Author index……Page 282
Subject index……Page 284