A comparative study of PID and neuro-fuzzy based control schemes for a 6-DoF robotic arm

Abstract

The robotic arm is one of the most prominent manipulators used in the field of robotics and therefore serves as a benchmark problem in control systems. Recent developments in the field of artificial intelligence demonstrate the use of intelligent control techniques for various systems. This paper presents a neuro-fuzzy based control scheme for a robotic arm with 6 degrees of freedom used for the sorting and placing of coloured balls. A webcam based image acquisition scheme is used to locate and identify various colored balls and subsequently the robotic arm is moved according to required trajectory. The performance of the proposed neuro-fuzzy controller is compared with a traditional PID control to illustrate the superiority of the intelligent control scheme. Experiments performed on a hardware test bench validate the improvement in performance achieved using the neuro-fuzzy control scheme.

Keywords

Robotic arm image processing neuro-fuzzy control PID

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References

Toledo Fuentes

, Kipfmueller

, and José Prieto

M.A.

, 6 DOF articulated-arm robot and mobile platform: Dynamic modelling as Multibody System and its validation via Experimental Modal Analysis, IOP Conference Series: Materials Science and Engineering257 doi:10.1088/1757-899X/257/1/012008

Al-Halimi

and Moussa

, Complex tasks by users with upper-extremity disability using a 6-DOF robotic arm: A study, IEEE Trans Neural Syst Rehabil Eng (2016) Published online2016Aug26 doi:10.1109/TNSRE.2016.2603472

Cáceres Flórez

, Maurício Rosário

, and Amaya

, Approach to assistive robotics based on an EEG sensor and a 6-DoF robotic arm, International Review of Mechanical Engineering (2016) 10. 253–260. 10.15866/ireme.v10i4.9323

Taryudi , and Wang , Eye to hand calibration using ANFIS for stereo vision-based object manipulation system, MS. Microsyst Technol24 (2018),305 doi:10.1007/s00542-017-3315-y

Mohammadi

, Tavakoli

, Marquez

H.J.

and Hashemzadeh

, Nonlinear disturbance observer design for robotic manipulators, Control Engineering Practice21(3) (2013), 253–267. ISSN 0967-0661 doi:10.1016/j.conengprac.2012.10.008

Cheong

, Kyun Chung

, and Youm

, Inverse kinematics of multilink flexible robots for high-speed applications, IEEE Transactions on Robotics and Automation20(2) 2004, 269–282. doi:10.1109/ISDA.2006.253807

Paul

, Robot manipulators: Mathematics, programming, and control: The computer control of robot manipulators. MIT Press, Cambridge, MA. ISBN 978-0-262-2-7, 1981.

Gaing

Z.L.

, A particle swarm optimization approach for optimum design of PID controller in AVR system, IEEE Transaction on Energy Conversion19(2) (2004),384–391. doi:10.1109/TEC.2003.821821

Das

and Mahapatra

S.S.

, Prediction of inverse kinematics of a 5-DOF pioneer robotic arm hng 6-DOF end-effector using ANFIS, Int J of Computational Vision and Robotics5(4) (2015),365 doi:38410.1504/IJCVR.2015.072190

10.

Brady

, Gerhardt

L.A.

and Davidson

H.F.

, Robotics and Artificial IntelligenceSpringer, Berlin, Heidelberg, 1984 doi: 10.1007/978-3-642-82153-0

11.

Visioli

, Practical PID controller, Springer, London(UK), 2006 doi:10.1007/1-84628-586-0

12.

McCarthy

J.M.

, Introduction to Theoretical Kinematics, MIT Press, Cambridge, MA,1990.

13.

Vincent

J.L.

, Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms, IEEE Transactions on Image Processing2 (1993), 176–201. doi:10.1109/83.217222

14.

Alavandar

and Nigam

M.J.

, Adaptive neuro-fuzzy inference system based control of six DOF robot manipulator,, Journal of Engineering Science and Technology Review-1 (2008).

15.

Lewis

F.L.

, Abdallah

C.T.

and Dawson

D.M.

, Control of Robot Manipulators. USA: Macmillan Publishing Company, 1993.

16.

Mamdani

E.H.

, Application of fuzzy algorithms for control of simple dynamic plant, in Electrical Engineers, Proceedings of the Institution of121(12) (1974),1585–1588. doi:10.1049/piee.1974.0328

17.

Jang

J.S.R.

, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man, and Cybernetics23(3) (1993),665–685. doi:10.1109/21.256541

18.

Sahu

, Biswal

and Subudhi

, A novel method for representing robot kinematics using quaternion theory, , IEEE Conference on Computational Intelligence, Control and Automation, (2008),& 76–82.

19.

Patel

, Adaptive neuro fuzzy inference system for the kinematics of 6 DOF robotic arm, International Journal of Scientific & Engineering Research7(1) (2016).

20.

and Huang

T.S.

, Nonstationary color tracking for vision-based human–computer interaction, IEEE Transactions On Neural Networks13(4) (2012). doi:10.1109/TNN.2002.1021895

21.

Hagedoom

, Pattern matching using similarity measures, PhD thesis, Utrecht University, 2000. doi:1874/850