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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40805

Title: Fuzzy Neural Networks Approaches for Robotic Gait Synthesis
Authors: Jih-Gau Juang
Contributors: 國立臺灣海洋大學:通訊與導航工程學系
Date: 2000
Issue Date: 2017-02-07T01:04:59Z
Publisher: IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS
Abstract: Abstract: In this paper, a learning scheme using a fuzzy controller to generate walking gaits is developed. The learning scheme uses a fuzzy controller combined with a linearized inverse biped model. The controller provides the control signals at each control time instant. The algorithm used to train the controller is "backpropagation through time". The linearized inverse biped model provides the error signals for backpropagation through the controller at control time instants. Given prespecified constraints such as the step length, crossing clearance, and walking speed, the control scheme can generate the gait that satisfies these constraints. Simulation results are reported for a five-link biped robot.
Relation: 30(4)
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40805
Appears in Collections:[通訊與導航工程學系] 期刊論文

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