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Nonlinear control system using learning Petri network
Author(s) -
Ohbayashi Masanao,
Hirasawa Kotaro,
Sakai Singo,
Hu Jinglu
Publication year - 2000
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/(sici)1520-6416(200003)131:3<58::aid-eej7>3.0.co;2-f
Subject(s) - nonlinear system , controller (irrigation) , petri net , artificial neural network , sort , computer science , control theory (sociology) , control (management) , function (biology) , control engineering , artificial intelligence , engineering , algorithm , physics , quantum mechanics , information retrieval , evolutionary biology , agronomy , biology
According to recent understanding of brain science, it is suggested that there is a distribution of functions in the brain, which means that different neurons are activated depending on which sort of sensory information the brain receives. We have already developed a learning network with a function distribution which is called the Learning Petri Network (LPN) and have shown that this network could learn nonlinear and discontinuous mappings which the Neural Network (NN) cannot. In this paper, a more realistic application which has dynamic characteristics is studied. From simulation results of a nonlinear crane control system using LPN controller, it is clarified that the control performance of LPN controller is superior to that of NN controller. © 2000 Scripta Technica, Electr Eng Jpn, 131(3): 58‐69, 2000