Please use this identifier to cite or link to this item:
標題: Evolutionary neural networks and DNA computing algorithms for dual-axis motion control
作者: Huang, C.H.
Lin, C.L.
關鍵字: Evolutionary neural networks;DNA computing algorithm (DNACA);Dual-axis;Cross-coupling;PID controllers;control design;groundwater remediation;optimization;system;platform;robots
Project: Engineering Applications of Artificial Intelligence
期刊/報告no:: Engineering Applications of Artificial Intelligence, Volume 24, Issue 7, Page(s) 1263-1273.
A new method is proposed to deal with the dual-axis control of a multi-variables system with two induction motors. Investigation of resolving the cross-coupling problem of dual-axis platform is addressed by a neural net-based decoupling compensator and a sufficient condition ensuring closed-loop stability is derived. An evolutionary algorithm processing the universal seeking capability is proposed for finding the optimal connecting weights of the neural decoupling compensator and the gains of PID controllers. Extensive numerical studies verify the performance and applicability of the proposed design under a variety of operating conditions. (C) 2011 Elsevier Ltd. All rights reserved.
ISSN: 0952-1976
DOI: 10.1016/j.engappai.2011.06.013
Appears in Collections:電機工程學系所

Show full item record

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.