On-line genetic algorithm-based fuzzy-neural sliding mode controller using improved adaptive bound reduced-form genetic algorithm

DOI10.1080/00207720902750011zbMath1291.93079OpenAlexW2038854376MaRDI QIDQ5168724

Chi-Hsu Wang, Ping-Zong Lin, Wei-Yen Wang, Tsu-Tian Lee

Publication date: 21 July 2014

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207720902750011

zbMATH Keywords

robot manipulatoradaptive bound reduced-form genetic algorithmfuzzy-neural sliding mode controlleron-line genetic algorithm-based controller

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Fuzzy control/observation systems (93C42) Adaptive control/observation systems (93C40) Automated systems (robots, etc.) in control theory (93C85) Variable structure systems (93B12)

Related Items (3)

Constrained evolutionary computing approach to Web service compositions ⋮ A hybrid of electromagnetism-like mechanism and back-propagation algorithms for recurrent neural fuzzy systems design ⋮ Robust tracking control of a unicycle-type wheeled mobile manipulator using a hybrid sliding mode fuzzy neural network

Uses Software

• Genocop

Cites Work

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• Adaptive motion control design of robot manipulators: an input-output approach
• A new controller design for manipulators using the theory of variable structure systems
• System identification and control using genetic algorithms
• Controller Design for a Manipulator Using Theory of Variable Structure Systems
• Model reduction with time delay combining the least-squares method with the genetic algorithm

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