𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Tuning fuzzy logic controllers using response envelope method

✍ Scribed by H.B. Gürocak


Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
649 KB
Volume
115
Category
Article
ISSN
0165-0114

No coin nor oath required. For personal study only.

✦ Synopsis


A fuzzy logic controller (FLC) is designed based on a human expert's knowledge of the process. The performance of this initial design attempt will, in general, not be satisfactory in terms of certain design criteria such as steady-state error, the oscillatory behavior of the system, etc. This is due to the fact that no standard method exists for transforming human knowledge or experience into the rule base of the FLC. In this paper, a method to tune the rule base of an initial FLC design attempt is presented. Results of four experiments are reported and discussed.


📜 SIMILAR VOLUMES


Tuning cascade PID controllers using fuz
✍ M.X Li; P.M. Bruijn; H.B. Verbruggen 📂 Article 📅 1994 🏛 Elsevier Science 🌐 English ⚖ 697 KB

## Cascade control configurations offer interesting possibilities to improve the control of processes. However, the tuning of cascade controllers can be quite complex, which hampers a wider spread of applications. Using an on-line pattern recognition approach and fuzzy inferencing it is possible

A genetic-algorithm-based method for tun
✍ H.B. Gürocak 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 234 KB

It has been demonstrated many times in practice that fuzzy logic controllers have an important role in rule-based expert systems. However, it is essential for a fuzzy logic controller to have an appropriate set of rules to perform at the desired level. The linguistic structure of the fuzzy logic con

Tuning fuzzy logic controllers by geneti
✍ F. Herrera; M. Lozano; J.L. Verdegay 📂 Article 📅 1995 🏛 Elsevier Science 🌐 English ⚖ 692 KB

The performance of a fuzzy logic controller depends on its control rules and membership functions. Hence, it is very important to adjust these parameters to the process to be controlled. A method is presented for tuning fuzzy control rules by genetic algorithms to make the fuzzy logic control system