{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T17:39:46Z","timestamp":1761154786848,"version":"3.41.2"},"reference-count":20,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2005,6,1]],"date-time":"2005-06-01T00:00:00Z","timestamp":1117584000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2005,6,1]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>The paper aims to advance methodologies to optimize fuzzy logic controller parameters via neural network and use the neuro\u2010fuzzy scheme to control two cooperating robots.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>The paper presents a special neural network architecture that can be converted to fuzzy logic controller. Concepts of model predictive control (MPC) have been used to generate optimal signal to be used to train the neural network via backpropagation. Subsequently, a trained neural network is used to obtain fuzzy logic controller parameters.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>The proposed neuro\u2010fuzzy scheme is able to precisely learn the control relation between input\u2010output training data generated by the learning algorithm. From the experiments performed on the industrial grade robots at Robotics and Manufacturing Automation (RAMA) Laboratory, it was found that the neuro\u2010fuzzy controller was able to learn fuzzy logic rules and parameters accurately.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Research limitations\/implications<\/jats:title><jats:p>The backpropagation method, used in this research, is extremely dependent on initial choice of parameters, and offers no mechanism to restrict the parameters within specified range during training. Use of alternative learning mechanisms, such as reinforcement learning, needs to be investigated.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Practical implications<\/jats:title><jats:p>The neuro\u2010fuzzy scheme presented can be used to develop controller for plants for which it is difficult to obtain analytical model or sufficient information about input\u2010output heuristic relation is not available.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>The paper presents the neural network architecture and introduces a learning mechanism to train this architecture online.<\/jats:p><\/jats:sec>","DOI":"10.1108\/01439910510593929","type":"journal-article","created":{"date-parts":[[2005,5,31]],"date-time":"2005-05-31T20:02:59Z","timestamp":1117569779000},"page":"234-239","source":"Crossref","is-referenced-by-count":8,"title":["Neuro\u2010fuzzy control applied to multiple cooperating robots"],"prefix":"10.1108","volume":"32","author":[{"given":"Manish","family":"Kumar","sequence":"first","affiliation":[]},{"given":"Devendra P.","family":"Garg","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022031519520390300_b1","doi-asserted-by":"crossref","unstructured":"Garg, D., Ananthraman, S. and Prabhu, S. 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