One important Artificial Intelligence tool for automatic control is the use of fuzzy logic controllers, which are fuzzy rule-based systems comprising expert knowledge in form of linguistic rules. These rules are usually constructed by an expert in the field of interest who can link the facts with the conclusions. However, this way to work sometimes fails to obtain an optimal behaviour. To solve this problem, within the framework of Machine Learning, some Artificial Intelligence techniques could be successfully applied to enhance the controller behaviour.
Rule selection methods directly obtain a subset of rules from a given fuzzy rule set, removing inefficient and redundant rules and, thereby, enhancing the controller interpretability, robustness, flexibility and control capability. Besides, different parameter optimization techniques could be applied to improve the system accuracy by inducing a better cooperation among the rules composing the final rule base.
This work presents a study of how two new tuning approaches can be applied to improve FLCs obtained from the expert’s experience in non trivial problems. Additionally, we analyze the positive synergy between rule selection and tuning techniques as a way to enhance the capability of these methods to obtain more accurate and compact FLCs. Finally, in order to show the good performance of these approaches, we solve a real-world problem for the control of a heating, ventilating and air conditioning system.
HVAC systems Fuzzy logic controllers Genetic tuning Linguistic 2-tuples representation Linguistic 3-tuples representation Rule selection
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