Neural-network-based fuzzy logic control system with applications on compliant robot control

MawKae Hor; Hui Ling Lu

doi:10.1117/12.188915

10 October 1994 Neural-network-based fuzzy logic control system with applications on compliant robot control

MawKae Hor, Hui Ling Lu

Proceedings Volume 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision; (1994) https://doi.org/10.1117/12.188915
Event: Photonics for Industrial Applications, 1994, Boston, MA, United States

Abstract

In view of the success of neural network applications in inverted pendulum control, speech recognition, and other problem solving, we believe that one could inject the noise removing concepts and learning spirits into the algorithm in constructing the neural networks and apply it to the various tasks such as compliant coordinated motion using multiple robots. Based on the fuzzy logic, a fuzzy logical control system is a logical system which is much closer to human thinking than any other logical systems. During recent years, fuzzy logic control has emerged as a fruitful area in applications, especially the applications lacking quantitative data regarding the input-output relations. Whereas, the connectionist model injects the learning ability to the fuzzy logic system. This model, proposed by Lin and Lee, is a connected neural network that embedded the fuzzy rules in the architecture. Since this model is general enough and we expect the embedded fuzzy concepts can solve the problems caused by the defective training data, it is chosen as our base structure. Appropriate modifications have been made to this model to reflect the real situations encountered in the robot applications. Our goal is to control two different types of robots for coordinated motion using sensory feedback information.

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MawKae Hor and Hui Ling Lu "Neural-network-based fuzzy logic control system with applications on compliant robot control", Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); https://doi.org/10.1117/12.188915

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KEYWORDS

Fuzzy logic

Control systems

Neural networks

Sensors

Evolutionary algorithms

Data modeling

Fuzzy systems

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