Premium
A genetic search approach to unfalsified PI control design for a weigh belt feeder
Author(s) -
Collins Emmanuel G.,
Zhao Yanan,
Millett Richard
Publication year - 2001
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.687
Subject(s) - engineering , set (abstract data type) , control engineering , control (management) , genetic algorithm , closed loop , control theory (sociology) , control system , key (lock) , computer science , artificial intelligence , machine learning , electrical engineering , computer security , programming language
This paper proposes and experimentally demonstrates an approach to automated PI tuning for an industrial weigh belt feeder that is based on unfalsified control concepts. Unfalsified control is used here as a means of using either open‐ or closed‐loop test data to identify a subset of controllers (from an initial set) that is not proved to violate the multiple objectives specified by the control engineer. A novel feature of the unfalsified approach is that it allows controllers to be eliminated from consideration by predicting their performance without actually inserting the controllers in the loop. In addition, this methodology does not require an explicit model. However, in practice, it does require some closed‐loop experimentation to determine the cost functions used to perform the unfalsification. When the unfalsified PI autotuning approach is applied to the industrial weigh belt feeder, it is able to successfully identify a subset of PI control laws that meets the performance specs. A key feature of this paper is the use of a genetic search algorithm to reduce the computational requirements of unfalsified control, especially when the initial set of controllers is large. Copyright © 2001 John Wiley & Sons, Ltd.