Abstract
This paper proposes a novel self-structuring algorithm for the online adaptive fuzzy controller (SA-OAFC). The SA-OAFC capable of adding and deleting inference rules autonomously can start operating with an empty set of fuzzy rules based on the desired output and actual output of the system to avoid conventional differential operation. It also takes advantage of the auxiliary fuzzy system to evaluate the approximated results with little information of the plant. The SA-OAFC is characterized by its good engineering approachability, robustness for all kinds of perturbations of the plant, and the ability to perform variable selection among a group of candidate input variables. Moreover, it manages to remarkably reduce the amount of computation and decrease the complexity of the system. This paper demonstrates the capabilities of SA-OAFC by a simulation example and then hardware-in-the-loop (HIL) experiment.
Highlights
With the popularization of artificial intelligence in control field, the fuzzy control has been widely used in recent years [1,2,3]
Chen et al [12] adopted the “pseudo fuzzy output” method to determine the initial consequents of the new rules, successfully resolving the oscillation problem and according to the contribution of the rules, they deleted rules to limit the number of rules, but they only removed the redundant rules, ignoring the redundant membership functions (MFs)
Performance is evaluated using the root mean square error (RMSE) and the results show that the error of the SA-OAFC is smaller than other self-structuring algorithms
Summary
Key Laboratory of Electronic Equipment Structure Design of Ministry of Education, Xidian University, 2 Taibai Road, Xi’an 710071, China. The SA-OAFC capable of adding and deleting inference rules autonomously can start operating with an empty set of fuzzy rules based on the desired output and actual output of the system to avoid conventional differential operation. It takes advantage of the auxiliary fuzzy system to evaluate the approximated results with little information of the plant. The SA-OAFC is characterized by its good engineering approachability, robustness for all kinds of perturbations of the plant, and the ability to perform variable selection among a group of candidate input variables. This paper demonstrates the capabilities of SA-OAFC by a simulation example and hardware-in-the-loop (HIL) experiment
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