A modified version of the self-toning regulator having limited adaptability has been successfully implemented on a large-scale chemical pilot plant. The new algorithm uses a least-squares estimator with variable weighting of past data; at each step a weighting factor is chosen to maintain constant a
Comments on: ‘Implementation of self-tuning regulators with variable forgetting factors’
✍ Scribed by S.P. Sanoff; P.E. Wellstead
- Publisher
- Elsevier Science
- Year
- 1983
- Tongue
- English
- Weight
- 170 KB
- Volume
- 19
- Category
- Article
- ISSN
- 0005-1098
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✦ Synopsis
Almtract--The similarities are pointed out between the variable forgetting factor given in the paper by Fortescue, Kershenbaum and Ydstie and that given in Wellstead and Sanoff. Moreover, it is shown that the method of Fortescue, Kershenbaum and Ydstie can be made significantly more efficient, computationally, than is indicated.
FOR THE purposes of this discussion, the variable forgetting factor of Fortescue, Kershenbaum and Ydsfie (1981) is given by the following algorithm: Innovation ~(t) = y(t) -dpT(t)l~(t -1).
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📜 SIMILAR VOLUMES
Ahstraet--A modified version of Fortescue's adaptive regulator is described, based on a minimum-variance estimator with variable forgetting factor and a d-step ahead control law. As in the Fortescue algorithm, the forgetting factor is chosen at each step to keep a measure of the information content