Neurofuzzy self-tuning of the dissipation rate gain for model-free force-position exponential tracking of robots-Reference-Cited by-同舟云学术

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Neurofuzzy self-tuning of the dissipation rate gain for model-free force-position exponential tracking of robots

Published:2016-01 Issue: Volume:171 Page:209-219
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Parra-Vega Vicente,García-Rodríguez Rodolfo,Armendariz Jorge

Funder

CONICYT, Departamento de Relaciones Internacionales “Programa de Cooperación Científica Internacional” CONICYT/CONACYT

CONACYT of Mexico

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference39 articles.

1. A survey on analysis and design of model-based fuzzy control systems;Feng;IEEE Trans. Fuzzy Syst.,2006

2. An approach to fuzzy control of nonlinear systems;Wang;IEEE Trans. Fuzzy Syst.,1996

3. C.-T. Sun, A neuro-fuzzy classifier and its applications, in: IEEE International Conference on Fuzzy Systems, 1993, pp. 94–98.

4. Evolutionary design of a fuzzy classifier from data;Chang;IEEE Trans. Syst. Man Cybern. Part B: Cybern.,2004

5. Output feedback tracking control of robot manipulators with model uncertainty via adaptive fuzzy logic;Kim;IEEE Trans. Fuzzy Syst.,2004

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2. Time-varying isobaric surface reconstruction and path planning for robotic grinding of weak-stiffness workpieces;Robotics and Computer-Integrated Manufacturing;2020-08

3. Control of a manipulator robot by neuro-fuzzy subsets form approach control optimized by the genetic algorithms;ISA Transactions;2018-06

4. Normal and tangent force neuro-fuzzy control of a soft-tip robot with unknown kinematics;Engineering Applications of Artificial Intelligence;2017-10

5. Hybrid position/virtual-force control for obstacle avoidance of wheeled robots using Elman neural network training technique;International Journal of Advanced Robotic Systems;2017-05

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