期刊
ISA TRANSACTIONS
卷 77, 期 -, 页码 133-145出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2018.03.023
关键词
Adaptive control; Fuzzy subsets form; Neural networks; Genetic algorithms; Neuro-fuzzy-genetic; Manipulator robot
In this paper, we describe a new form of neuro-fuzzy-genetic controller design for nonlinear system derived from a manipulator robot. The proposed method combines fuzzy logic and neuronal networks which are of growing interest in robotics, the neuro-fuzzy controller does not require the knowledge of the robot parameters values. Furtheremore, the genetic algorithms (GM) for complex motion planning of robots require an evaluation function which takes into account multiple factors. An optimizing algorithm based on the genetic algorithms is applied in order to provide the most adequate shape of the fuzzy subsets that are considered as an interpolation functions. The proposed approach provides a well learning of the manipulator robot dynamics whatever the assigned task. Simulation and practical results illustrate the effectiveness of the proposed strategy. The advantages of the proposed method and the possibilities of further improvements are discussed. (C) 2018 ISA. Published by Elsevier Ltd. All rights reserved.
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