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Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

Dalibor Petković, Shahaboddin Shamshirband, Nor Badrul Anuar, Aznul Qalid Md Sabri, Zulkanain Bin Abdul Rahman, Nenad D. Pavlović
2015 Journal of Intelligent and Robotic Systems  
Here, an adaptive neuro fuzzy inference system (ANFIS) for controlling input displacement and object recognition of a new adaptive compliant gripper is presented.  ...  The grasping function of the proposed adaptive multi-fingered gripper relies on the physical contact of the finger with an object.  ...  Acknowledgments This work also funded by the University of Malaya, Malaysia, under grant RP005A-13ICT.  ... 
doi:10.1007/s10846-015-0182-6 fatcat:n77idb5etzgg3ign7sqlngpdtu

Gait detection based stable locomotion control system for biped robots

Ming-Yuan Shieh, Chien-Sheng Chen, Chen-Hsin Chuang, Yi-Rong Liou, Jeng-Han Li
2012 Computers and Mathematics with Applications  
In addition, a neuro-fuzzy control scheme is also proposed to control the ZMP trajectories and relative balancing by integrating the data of a 3-axis gyroscope to adaptive posture.  ...  The experimental results show that the designed control system has improved the biped robot adaptive ability to overcome landform changes and the stability of locomotion while standing or walking on uneven  ...  The equipment and software of the experiments were supported by the Robotics Research Center and the Optoelectronics Semiconductor Center in Southern Taiwan University.  ... 
doi:10.1016/j.camwa.2012.03.090 fatcat:3qrwiuoaz5dl3boeynmm3syxiq

A Practical Neuro-fuzzy Mapping and Control for a 2 DOF Robotic Arm System

Ebrahim Mattar
2013 International Journal of Computing and Digital Systems  
This research is presenting a practical use of Neuro-Fuzzy system to solve inverse kinematics problem that used for a two links robotic arm.  ...  Neuro-fuzzy system is to generalize and produce an appropriate output.  ...  The main controller concept was based on the use of inverse learning Adaptive Neuro-Fuzzy Inference System (ANFIS) model only to train itself from certain given robot trajectories.  ... 
doi:10.12785/ijcds/020302 fatcat:f3x52ecqazef3kru3y2lm52rde

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Agustin Gajate, Rodolfo Haber, Raul del Toro, Pastora Vega, Andres Bustillo
2010 Journal of Intelligent Manufacturing  
The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist  ...  , transductive and evolving neuro-fuzzy systems).  ...  Acknowledgments This work was supported by DPI2008-01978 "Networked cognitive control system for high-performance machining processes (COGNETCON)" and CIT-420000-2008-13 "InteUigent monitoring of nano-scale  ... 
doi:10.1007/s10845-010-0443-y fatcat:nhzjdffmjnhcbkjwa5lubl255y

A tutorial on learning human welder's behavior: Sensing, modeling, and control

Y.K. Liu, W.J. Zhang, Y.M. Zhang
2014 Journal of Manufacturing Processes  
Specifically, different sensing methods of the weld pool are reviewed and a novel 3D vision-based sensing system developed at University of Kentucky is introduced.  ...  Characterization of the weld pool is performed and human intelligent model is constructed, including an extensive survey on modeling human dynamics and neuro-fuzzy techniques.  ...  Jang [107, 108] developed the Adaptive Neuro-Fuzzy Inference System (ANFIS) by using a hybrid learning procedure.  ... 
doi:10.1016/j.jmapro.2013.09.004 fatcat:iasi6gwrpnharnm5twonwddzjm

A model-reference impedance control of robot manipulators using an adaptive fuzzy uncertainty estimator

Gholamreza Nazmara, Mohammad Mehdi Fateh, Seyed Mohammad Ahmadi
2018 International Journal of Computational Intelligence Systems  
Furthermore, two control terms namely a robustifying term and a fuzzy uncertainty estimator are added in the structure of control design in order to improve the performance of the control system as well  ...  This paper aims at developing a voltage -based impedance model-reference controller using fuzzy uncertainty estimator for the robust control of electrically driven robot manipulators.  ...  An adaptive fuzzy neural network force approach [22] , robust neural network [23] and robust adaptive neuro-fuzzy controller [24] for hybrid position/force control of robot manipulators have been  ... 
doi:10.2991/ijcis.11.1.74 fatcat:vojv43rczzdolb7dxeptcxuaqi

Control of Efficient Intelligent Robotic Gripper Using Fuzzy Inference System [chapter]

A.M. Zaki, O.A. Mahgoub, A.M. El-Shafei, A.M. Solim
2012 Fuzzy Inference System - Theory and Applications  
Adaptive Neuro-Fuzzy Inference Systems are Fuzzy Sugeno models put in the framework of adaptive systems to facilitate learning and adaptation.  ...  Adaptive Neuro-Fuzzy Inference Systems are realized by an appropriate combination of neural and fuzzy systems and provide a valuable modeling approach of complex systems (Denai et al., 2004; Rezaeeian  ... 
doi:10.5772/37010 fatcat:dwfvkvu7qvgjbbcz72vlzcrjey

SURVEY OF INTELLIGENT CONTROL ALGORITHMS FOR HUMANOID ROBOTS

Dusko Katić, Miomir Vukobratović
2005 IFAC Proceedings Volumes  
This paper focusses on the application of intelligent control techniques (neural networks, fuzzy logic and genetic algorithms) and their hybrid forms (neuro-fuzzy networks, neuro-genetic and fuzzy-genetic  ...  algorithms) in the area of humanoid robotic systems.  ...  In this perspective, the proposed control system combines the fuzzy expert knowledge and neural network into an adaptive neuro-fuzzy inference system.  ... 
doi:10.3182/20050703-6-cz-1902.01276 fatcat:wrzes2vitbf4rkkfn3c6evxuxe

ANFIS Control Double-Inverted Pendulum

P. Shen
2015 Chemical Engineering Transactions  
For double inverted pendulum multivariable, strong coupling and nonlinear proposed adaptive fuzzy neural inference system (ANFIS) is applied inverted pendulum stabilization control process.  ...  Adaptive control algorithm, fully able to meet the requirements of double inverted pendulum control, ANFIS system after training, will be applied to the inverted pendulum system controller has better control  ...  the application of neural learning techniques, adjusting parameters and structure neuro-fuzzy control system.  ... 
doi:10.3303/cet1546150 doaj:569ef5260529461787501da31171dc79 fatcat:ulvgevovs5hj3dze6ie6mwrkvy

Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System

Adnan Tawafan, Marizan Bin Sulaiman, Zulkifilie Bin Ibrahim
2012 IAES International Journal of Artificial Intelligence (IJ-AI)  
It is integrating the learning capabilities of neural network to the fuzzy logic system robustness in the sense that fuzzy logic concepts are embedded in the network structure.  ...  This paper proposes an intelligent algorithm using an adaptive neural-Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault.  ...  Acknowledgements The authors wish to thank the Faculty of Electrical Engineering, UTeM for providing the facilities for this research.  ... 
doi:10.11591/ij-ai.v1i2.425 fatcat:tfc3qgkyd5d7nhbp5vgaa3ws6y

Intelligent learning and control of autonomous robotic agents operating in unstructured environments

Hani Hagras, Tarek Sobh
2002 Information Sciences  
Because environments and users of systems continuously change, robotic agents have to be adaptive.  ...  sensing and control.  ...  Position and force sensor values are encoded in the state of the system by means of neural networks. The paper entitled " Application of Online Neuro Fuzzy Controller to AUV", by T. Kim and J.  ... 
doi:10.1016/s0020-0255(02)00221-9 fatcat:ul5wno4oonhvphivc3257pkgqa

Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera

Ali M. Abdulshahed, Andrew P. Longstaff, Simon Fletcher, Alan Myers
2015 Applied Mathematical Modelling  
An Adaptive Neuro-Fuzzy Inference System with fuzzy c-means clustering (FCM-ANFIS) was employed to design the thermal prediction model.  ...  2015) Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera.  ...  Adaptive Neuro-Fuzzy Inference System (ANFIS) The Adaptive Neuro Fuzzy Inference System (ANFIS), was first introduced by Jang, in 1993 [20] .  ... 
doi:10.1016/j.apm.2014.10.016 fatcat:agp6yqprczceficiefa5zxs27m

Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera

F. Sdao, D. S. Lioi, S. Pascale, D. Caniani, I. M. Mancini
2013 Natural Hazards and Earth System Sciences  
The method described in this paper is a novel technique based on a neuro-fuzzy system.  ...  The evaluation model for the susceptibility presented in this paper is very focused on the use of innovative techniques of artificial intelligence such as Neural Network, Fuzzy Logic and Neuro-fuzzy Network  ...  We thank Serena Parisi for critical reading of the paper. Edited by: R. Lasaponara Reviewed by: B. Haneberg and two anonymous referees  ... 
doi:10.5194/nhess-13-395-2013 fatcat:nqpgylylwzb73ayfejytk4rqfi

Design Intelligent Model base Online Tuning Methodology for Nonlinear System

Ali Roshanzamir, Farzin Piltan, Narges Gholami mozafari, Azita Yazdanpanah, Marjan Mirshekari
2014 International Journal of Modern Education and Computer Science  
This research is used to reduce or eliminate the PID controller problems based on model reference fuzzy logic theory to control of flexible robot manipulator system and testing of the quality of process  ...  In various dynamic parameters systems that need to be training on-line adaptive control methodology is used.  ...  This work was supported by the SSP Institute of Advance Science and Technology Program of Iran under grant no. 2013-Persian Gulf-2A.  ... 
doi:10.5815/ijmecs.2014.04.07 fatcat:qocdv6b7efgrnk6jnooronjy6m

Predicting Machining Errors in Turning Using Hybrid Learning

X. Li, P. K. Venuvinod, A. Djorjevich, Z. Liu
2001 The International Journal of Advanced Manufacturing Technology  
A new method of achieving the same objective through the use of the learning capability of an adaptive neuro-fuzzy network is developed and tested against experimental data for cylindrical turning.  ...  It is conducted by a novel contact sensor that probes with the tool and facilitates automation by providing proximity information as the tool approaches the workpiece.  ...  The work reported in the present paper uses an example of such a system: the adaptive neuro-fuzzy inference system (ANFIS) [17] .  ... 
doi:10.1007/pl00003954 fatcat:ymlzcbxr45agfpxuxc2pyil5me
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