Araştırma Makalesi
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TWO DOF ROBOT CONTROL WITH FUZZY BASED NEURAL NETWORKS

Yıl 2017, Cilt: 18 Sayı: 4, 819 - 830, 31.10.2017
https://doi.org/10.18038/aubtda.340002

Öz

In this study, trajectory control of robotic arm which has two degrees of
freedom (DOF) is conducted by using the control methods of
Proportional-Derivative (PD), Adaptive Neuro Fuzzy System (Anfis), hybrid
PD-Anfis and its performance analysis is carried out. In the design of the robot,
forward kinematics, inverse kinematics and dynamic equations are used. Firstly,
the PD controller is executed, and then the PD controller and Anfis controller
are compared applying to a different controller approach with the Anfis of
Matlab/Simulink software. The positive and negative sides of the Anfis
controller are compared and hybrid PD-Anfis controller method is conducted as a
different approach to eliminate the negative sides. While the system constants
Kp and Kv are kept constant by the classical PD control
method, the output of PD controller is trained with Anfis in the new method and
the output value is adjusted according to the error and the change rate of the
error. By this way, outputs which have less error rate and which are able to
follow the reference better are obtained.

Kaynakça

  • Islam S, Liu P. PD Output Feedback Control Design for Industrial Robotic Manipulators. IEEE Transactions on Mechatronics 2010; 16: 187-197.
  • Ko C, Park S, Yoon T, Kim D, Chung G. Comparative study of fuzzy PD control and PI control for heavy duty robot. IEEE International Conference on Industrial Technology (ICIT); 10-13 Feb. 2009; IEEE pp.1-5.
  • Rashidifar M A, Rashidifar A A, Ahmadi D. Modeling and Control of 5DOF Robot Arm Using Fuzzy Logic Supervisory Control. International Journal of Robotics and Automation (IJRA) 2013; 2, pp: 56- 68.
  • Kumar C R, Sudha K R, Pushpalatha D V. Modeling and Control of 5DOF Robot Arm Using Neuro-Fuzzy. International Journal of Engineering Research& Technology (IJERT) 2012; 1, pp: 1-8.
  • Bachir O, Zoubir A. Adaptive Neuro-Fuzzy Inference System Based Control of Puma 600 Robot Manipulator. International Journal of Electrical and Computer Engineering (IJECE) 2012; 2: 90–97.
  • Arslan Ş, Korkmaz M. Control of a four-degree-of-freedom robot arm with fuzzy artificial neural network. Sakarya University Science Magazine 2013; 17, pp: 131-138.
  • Görel G, Altaş İ H. Fuzzy Logic Controlled Double Jointed Robot Arm. Chamber of Electrical Engineers of Turkey Electrical, Electronics and Computer Symposium 2012; pp. 152–156.
  • Agnihotri P, Banga V, Singh G. Modeling and Control of 5DOF Robot Arm Using ANFIS Toolbox. International Journal of Innovative Research in Computer and Communication Engineering 2015; 3: 8405-8411.
  • Yousif Z M O, Fathelrahman M. Neuro-Fuzzy Inference System based Controller in 6 DOF in Puma 560. IEEE International Conf. on Computing Electrical and Electronics Engineering 2013, pp: 1–7.
  • Hussian R, Massoud R, Al-Mawaldi M. ANFIS–Hybrid Reference Control for Improving Transient Response of Controlled Systems Using PID Controller. J. Biomedical Science and Engineering 2014; 7: 208-217.
  • Joelianto E, Anura D, Priyanto T. ANFIS-PID Control FES-Supported Sit-to-Stand in Paraplegics. International Journal of Artificial Intelligence 2013; 10: 88-111.
  • Ankarali A, Cilli M. ANFIS Inverse Kinematics and Hybrid Control of a Human Leg Gait Model. Academic Platform Journal of Engineering and Science 2013; 1: 34-49.
  • Ortatepe Z. Robot Control With Fuzzy Based Neural Networks. Eskişehir Osmangazi University, Eskişehir, Turkey, 2015.
  • Hidayat T, Sasongko S H. Performance Analysis of Hybrid PID-ANFIS for Speed Control of Brushless DC Motor Base on Identification Model System. International Journal of Computer and Information Technology 2013; 2: 694-700.
Yıl 2017, Cilt: 18 Sayı: 4, 819 - 830, 31.10.2017
https://doi.org/10.18038/aubtda.340002

Öz

Kaynakça

  • Islam S, Liu P. PD Output Feedback Control Design for Industrial Robotic Manipulators. IEEE Transactions on Mechatronics 2010; 16: 187-197.
  • Ko C, Park S, Yoon T, Kim D, Chung G. Comparative study of fuzzy PD control and PI control for heavy duty robot. IEEE International Conference on Industrial Technology (ICIT); 10-13 Feb. 2009; IEEE pp.1-5.
  • Rashidifar M A, Rashidifar A A, Ahmadi D. Modeling and Control of 5DOF Robot Arm Using Fuzzy Logic Supervisory Control. International Journal of Robotics and Automation (IJRA) 2013; 2, pp: 56- 68.
  • Kumar C R, Sudha K R, Pushpalatha D V. Modeling and Control of 5DOF Robot Arm Using Neuro-Fuzzy. International Journal of Engineering Research& Technology (IJERT) 2012; 1, pp: 1-8.
  • Bachir O, Zoubir A. Adaptive Neuro-Fuzzy Inference System Based Control of Puma 600 Robot Manipulator. International Journal of Electrical and Computer Engineering (IJECE) 2012; 2: 90–97.
  • Arslan Ş, Korkmaz M. Control of a four-degree-of-freedom robot arm with fuzzy artificial neural network. Sakarya University Science Magazine 2013; 17, pp: 131-138.
  • Görel G, Altaş İ H. Fuzzy Logic Controlled Double Jointed Robot Arm. Chamber of Electrical Engineers of Turkey Electrical, Electronics and Computer Symposium 2012; pp. 152–156.
  • Agnihotri P, Banga V, Singh G. Modeling and Control of 5DOF Robot Arm Using ANFIS Toolbox. International Journal of Innovative Research in Computer and Communication Engineering 2015; 3: 8405-8411.
  • Yousif Z M O, Fathelrahman M. Neuro-Fuzzy Inference System based Controller in 6 DOF in Puma 560. IEEE International Conf. on Computing Electrical and Electronics Engineering 2013, pp: 1–7.
  • Hussian R, Massoud R, Al-Mawaldi M. ANFIS–Hybrid Reference Control for Improving Transient Response of Controlled Systems Using PID Controller. J. Biomedical Science and Engineering 2014; 7: 208-217.
  • Joelianto E, Anura D, Priyanto T. ANFIS-PID Control FES-Supported Sit-to-Stand in Paraplegics. International Journal of Artificial Intelligence 2013; 10: 88-111.
  • Ankarali A, Cilli M. ANFIS Inverse Kinematics and Hybrid Control of a Human Leg Gait Model. Academic Platform Journal of Engineering and Science 2013; 1: 34-49.
  • Ortatepe Z. Robot Control With Fuzzy Based Neural Networks. Eskişehir Osmangazi University, Eskişehir, Turkey, 2015.
  • Hidayat T, Sasongko S H. Performance Analysis of Hybrid PID-ANFIS for Speed Control of Brushless DC Motor Base on Identification Model System. International Journal of Computer and Information Technology 2013; 2: 694-700.
Toplam 14 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Zafer Ortatepe

Osman Parlaktuna Bu kişi benim

Yayımlanma Tarihi 31 Ekim 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 18 Sayı: 4

Kaynak Göster

APA Ortatepe, Z., & Parlaktuna, O. (2017). TWO DOF ROBOT CONTROL WITH FUZZY BASED NEURAL NETWORKS. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering, 18(4), 819-830. https://doi.org/10.18038/aubtda.340002
AMA Ortatepe Z, Parlaktuna O. TWO DOF ROBOT CONTROL WITH FUZZY BASED NEURAL NETWORKS. AUBTD-A. Ekim 2017;18(4):819-830. doi:10.18038/aubtda.340002
Chicago Ortatepe, Zafer, ve Osman Parlaktuna. “TWO DOF ROBOT CONTROL WITH FUZZY BASED NEURAL NETWORKS”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 18, sy. 4 (Ekim 2017): 819-30. https://doi.org/10.18038/aubtda.340002.
EndNote Ortatepe Z, Parlaktuna O (01 Ekim 2017) TWO DOF ROBOT CONTROL WITH FUZZY BASED NEURAL NETWORKS. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 18 4 819–830.
IEEE Z. Ortatepe ve O. Parlaktuna, “TWO DOF ROBOT CONTROL WITH FUZZY BASED NEURAL NETWORKS”, AUBTD-A, c. 18, sy. 4, ss. 819–830, 2017, doi: 10.18038/aubtda.340002.
ISNAD Ortatepe, Zafer - Parlaktuna, Osman. “TWO DOF ROBOT CONTROL WITH FUZZY BASED NEURAL NETWORKS”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 18/4 (Ekim 2017), 819-830. https://doi.org/10.18038/aubtda.340002.
JAMA Ortatepe Z, Parlaktuna O. TWO DOF ROBOT CONTROL WITH FUZZY BASED NEURAL NETWORKS. AUBTD-A. 2017;18:819–830.
MLA Ortatepe, Zafer ve Osman Parlaktuna. “TWO DOF ROBOT CONTROL WITH FUZZY BASED NEURAL NETWORKS”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering, c. 18, sy. 4, 2017, ss. 819-30, doi:10.18038/aubtda.340002.
Vancouver Ortatepe Z, Parlaktuna O. TWO DOF ROBOT CONTROL WITH FUZZY BASED NEURAL NETWORKS. AUBTD-A. 2017;18(4):819-30.