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Fuzzy-Neural Controller and Real-Time Implementation of a Ball Balancing Beam
BibTeX
@MISC{Ng_fuzzy-neuralcontroller,
author = {K. C. Ng and M. M. Trivedi},
title = {Fuzzy-Neural Controller and Real-Time Implementation of a Ball Balancing Beam},
year = {}
}
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Abstract
Nonlinear dynamic ball balancing beam has been successfully controlled by applying conventional methods, neural networks, and fuzzy logic respectively. Conventional methods necessitate strong mathematical and control background to derive equations. Neural networks learn to balance a ball, but the ball never settles down due to the fact that "discrete resolution of the boxes representation" was used. Fuzzy logic has continuous representation; however, it takes a lot of efforts to incorporate human knowledge into rules. In order to have a continuous representation learning system with less rules and mathematics, a system with blend of neural networks and fuzzy logic is proposed. Fuzzy logic membership functions are utilized to fuzzify input parameters; neural network interpolates the fuzzy rule set; after defuzzification, the output is used to train a smaller size of neural network; the weights of the later neural network can be adjusted to #ne tune the controller. This controller balances balls with one third of the required 27 rules. With learning capability, it approaches its goal more frequently in general. In this paper, the design of the fuzzy-neural controller is discussed, the hardware setup is shown, and the performance is evaluated.
Keyphrases
neural network fuzzy-neural controller real-time implementation ball balancing beam fuzzy logic continuous representation conventional method human knowledge derive equation later neural network box representation hardware setup discrete resolution control background fuzzy logic membership function nonlinear dynamic ball fuzzy rule input parameter learning capability
