Research Papers

Control Design of Robotic Manipulator Based on Quantum Neural Network

[+] Author and Article Information
Hayder Mahdi Abdulridha

Department of Electrical Engineering,
University of Babylon,
P.O. Box No. 4,
Babylon, Iraq
e-mail: drenghaider@uobabylon.edu.iq

Zainab Abdullah Hassoun

Department of Electrical Engineering,
University of Babylon,
P.O. Box No. 4,
Babylon, Iraq
e-mail: Zainabeng43@gmail.com

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received November 2, 2016; final manuscript received November 12, 2017; published online December 19, 2017. Editor: Joseph Beaman.

J. Dyn. Sys., Meas., Control 140(6), 061002 (Dec 19, 2017) (11 pages) Paper No: DS-16-1530; doi: 10.1115/1.4038492 History: Received November 02, 2016; Revised November 12, 2017

In this study, a control system was designed to control the robot's movement (The Mitsubishi RM-501 robot manipulator) based on the quantum neural network (QNN). A proposed method was used to solve the inverse kinematics in order to determine the angles values for the arm's joints when it follows through any path. The suggested method is the QNN algorithm. The forward kinematics was derived according to Devavit–Hartenberg representation. The dynamics model for the arm was modeled based on Lagrange method. The dynamic model is considered to be a very important step in the world of robots. In this study, two methods were used to improve the system response. In the first method, the dynamic model was used with the traditional proportional–integral–derivative (PID) controller to find its parameters (Kp, Ki, Kd) by using Ziegler Nichols method. In the second method, the PID parameters were selected depending on QNN without the need to a mathematical model of the robot manipulator. The results show a better response to the system when replacing the traditional PID controller with the suggested controller.

Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.


Spong, M. W. , Hutchinson, S. , and Vidyasagar, M. , 2005, Robot Modeling and Control, 1st ed., Wiley, New York.
Romdhane, L. , and Duffy, J. , 1991, “ Optimum Grasp for Multi-Fingered Hands With Point Contact With Friction Using the Modified Singular Value Decomposition,” IEEE International Conference on Advanced Robotics (ICAR), Pisa, Italy, June 19–22, pp. 1614–1617.
Ishiia, C. , and Futatsugia, T. , 2013, “ Design and Control of a Robotic Forceps Manipulator With Screw-Drive Bending Mechanism and Extension of Its Motion Space,” Conference on Biomanufacturing, Tokyo, Japan, Mar. 3–5, pp. 104–109.
Denavit, J. , and Hartenberg, R. S. , 1955, “ A Kinematic Notation for Lower-Pair Mechanisms Based on Matrices,” ASME J. Appl. Mech., 22, pp. 215–221. https://www.scribd.com/document/177131768/a-kinematic-notation-for-lower-pair-mechanisms-based-on-matrices-denavit-hartenberg-pdf
Iliukin, V. N. , Mitkovskii, K. B. , Bizyanova, D. A. , and Akopyan, A. A. , 2017, “ The Modeling of Inverse Kinematics for 5 DOF Manipulator,” Dynamics and Vibroacoustics of Machines (DVM 2016), Samara, Russia, June 29–July 1, pp. 498–505.
Xia, Y. , and Wang, J. , 2001, “ A Dual Neural Network for Kinematic Control of Redundant Robot Manipulators,” IEEE Trans. Syst. Man Cybern., Part B, 31(1), pp. 147–154. [CrossRef]
Kucuk, S. , and Bingul, Z. , 2004, “ The Inverse Kinematics Solutions of Industrial Robot Manipulators,” IEEE International Conference on Mechatronics (ICM), Istanbul, Turkey, June 3–5, pp. 274–279.
Manocha, D. , and Canny, J. F. , 1994, “ Efficient Inverse Kinematics for General 6R Manipulators,” IEEE Trans. Rob. Autom., 10(5), pp. 648–657. [CrossRef]
Xu, D. , Acosta Calderon, C. A. , Gan, J. Q. , and Hu, H. , 2005, “ An Analysis of the Inverse Kinematics for a 5-DOF Manipulator,” Int. J. Autom. Comput., 2(2), pp. 114–124. [CrossRef]
Sadjadian, H. , Taghirad, H. D. , and Fatehi, A. , 2005, “ Neural Networks Approaches for Computing the Forward Kinematics of a Redundant Parallel Manipulator,” Int. J. Comput. Intell., 2(1), pp. 40–47.
Abu Qassem, M. , Abuhadrous, I. , and Elaydi, H. , 2010, “ Modeling and Simulation of 5 DOF Educational Robot Arm,” IEEE International Conference on Advanced Computer Control (ICACC), Shenyang, China, Mar. 27–29, pp. 569–574.
Meng, Z. , Liang, X. , Andersen, H. , and Ang, M. H. , 2016, “ Modeling and Control of a 2-Link Mobile Manipulator With Virtual Prototyping,” International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Xi'an, China, Aug. 19–22, pp. 363–368.
Linsker, R. , 2008, “ Neural Network Learning of Optimal Kalman Prediction and Control,” Neural Networks, 21(9), pp. 1328–1343. [CrossRef] [PubMed]
Li, S. , Wang, H. , and Rafique, M. U. , 2017, “ A Novel Recurrent Neural Network for Manipulator Control With Improved Noise Tolerance,” IEEE Trans. Neural Networks Learn. Systems, PP(99), pp. 1–11.
Jin, L. , Li, S. , La, H. M. , and Lou, X. , 2017, “ Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks,” IEEE Tran. Ind. Electron., 64(6), pp. 4710–4720. [CrossRef]
Li, S. , Zhang, Y. , and Jin, L. , 2017, “ Kinematic Control of Redundant Manipulators Using Neural Networks,” IEEE Trans. Neural Networks Learn. Syst., 28(10), pp. 2243–2254. [CrossRef]
Li, S. , He, J. , Li, Y. , and Rafique, M. U. , 2017, “ Distributed Recurrent Neural Networks for Cooperative Control of Manipulators: A Game-Theoretic Perspective,” IEEE Trans. Neural Networks Learn. Syst., 28(2), pp. 415–426. [CrossRef]
Jin, L. , Zhang, Y. , Li, S. , and Zhang, Y. , 2016, “ Modified ZNN for Time-Varying Quadratic Programming With Inherent Tolerance to Noises and Its Application to Kinematic Redundancy Resolution of Robot Manipulators,” IEEE Tran. Ind. Electron., 63(11), pp. 6978–6988. [CrossRef]
Jin, L. , Zhang, Y. , and Li, S. , 2016, “ Integration-Enhanced Neural Network for Real-Time-Varying Matrix Inversion in the Presence of Various Kinds of Noises,” IEEE Trans. Neural Networks Learn. Syst., 27(12), pp. 2615–2627. [CrossRef]
Xiao, L. , Bingjie, G. , and Lei, W. , 2007, “ Motion Control of Mini Underwater Robots Based on Sigmoid Fuzzy Neural Network,” IEEE International Conference on Automation and Logistics, Jinan, China, Aug. 18–21, pp. 918–922.
Purushothaman, G. , and Karayiannis, N. B. , 1997, “ Quantum Neural Networks (QNN's): Inherently Fuzzy Feed Forward Neural Networks,” IEEE Int. Conf. Neural Networks, 8(3), pp. 679–693.
Kucuk, S. , and Bingul, Z. , 2014, “ Inverse Kinematics Solutions for Industrial Robot Manipulators With Offset Wrists,” Appl. Math. Model., 38(7–8), pp. 1983–1999. [CrossRef]
Bingul, Z. , Ertunc, H. M. , and Oysu, C. , 2005, “ Comparison of Inverse Kinematics Solutions Using Neural Network for 6R Robot Manipulator With Offset,” ICSC Congress on Computational Intelligence Methods and Applications, Istanbul, Turkey, Dec. 15–17, pp. 1–5.
Crenganis, M. , Breaz, R. , Racz, G. , and Bologa, O. , 2012, “ Inverse Kinematics of a 7 DOF Manipulator Using Adaptive Neuro-Fuzzy Inference Systems,” IEEE International Conference on Control Automation Robotics and Vision (ICARCV), Guangzhou, China, Dec. 5–7, pp. 1232–1237.
Hendarto, H. A. , Munadi , and Setiawan, J. D. , 2014, “ ANFIS Application for Calculating Inverse Kinematics of Programmable Universal Machine for Assemble (PUMA) Robot,” First International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), Semarang, Indonesia, Nov. 8–9, pp. 35–40.
Bingul, Z. , and Karahan, O. , 2011, “ A Fuzzy Logic Controller Tuned With PSO for 2 DOF Robot Trajectory Control,” Expert Syst. Appl., 38(1), pp. 1017–1031. [CrossRef]
He, J. , Luo, M. , Zhang, Q. , Zhao, J. , and Xu, L. , 2016, “ Adaptive Fuzzy Sliding Mode Controller With Nonlinear Observer for Redundant Manipulators Handling Varying External Force,” J. Bionic Eng., 13(4), pp. 600–611. [CrossRef]
SharmaGaur, R. P. , and Mittal, A. P. , 2016, “ Design of Two-Layered Fractional Order Fuzzy Logic Controllers Applied to Robotic Manipulator With Variable Payload,” Appl. Soft Comput., 47, pp. 565–576. [CrossRef]
Vijay, M. , and Jena, D. , 2017, “ PSO Based Neuro Fuzzy Sliding Mode Control for a Robot Manipulator,” J. Electr. Syst. Inf. Technol., 4(1), pp. 243–256.
Sharma, R. , Kumar, V. , Gaur, P. , and Mittal, A. P. , 2016, “ An Adaptive PID Like Controller Using Mix Locally Recurrent Neural Network for Robotic Manipulator With Variable Payload,” ISA Trans., 62, pp. 258–267. [CrossRef] [PubMed]
Chaudhary, H. , Panwar, V. , Sukavanum, N. , and Prasad, R. , 2014, “ Fuzzy PD+I Based Hybrid Force/Position Control of an Industrial Robot Manipulator,” IFAC Proc., 47(1), pp. 429–436. [CrossRef]


Grahic Jump Location
Fig. 1

Mitsubishi RM-501 robot

Grahic Jump Location
Fig. 2

Coordinate frames of Mitsubishi RM-501 robotic arm

Grahic Jump Location
Fig. 3

Trajectory planning flowchart

Grahic Jump Location
Fig. 4

The proposed controller

Grahic Jump Location
Fig. 5

Position control of the robot

Grahic Jump Location
Fig. 6

Flowchart of the position control of one joint

Grahic Jump Location
Fig. 7

Main window of GUI program

Grahic Jump Location
Fig. 8

Block diagram of the interface card

Grahic Jump Location
Fig. 9

A photograph of the RM-501 manipulator system

Grahic Jump Location
Fig. 11

The robot arm configuration

Grahic Jump Location
Fig. 12

End-effector track

Grahic Jump Location
Fig. 13

(a) The desired and estimated values for θ1, (b) the desired and estimated values for θ2, (c) the desired and estimated values for θ3, and (d) the desired and estimated values for θ4

Grahic Jump Location
Fig. 15

Quantic polynomial trajectory

Grahic Jump Location
Fig. 16

Closed-loop PID controller

Grahic Jump Location
Fig. 17

Joint 1 response, with Kp = 5, Ki = 10, and Kd = 0.5

Grahic Jump Location
Fig. 18

Joint 2 response, with Kp = 5.2, Ki = 8.4, and Kd = 0.3

Grahic Jump Location
Fig. 19

Joint 3 response, with Kp = 5.4, Ki = 10.5, and Kd = 0.6

Grahic Jump Location
Fig. 20

Joint 4 response, with Kp = 5.1, Ki = 10, and Kd = 0.5

Grahic Jump Location
Fig. 21

Response of the base joint motor with PID and optimized PID controllers

Grahic Jump Location
Fig. 22

Response of the shoulder joint motor with PID and optimized PID controllers

Grahic Jump Location
Fig. 23

Response of the elbow joint motor with PID and optimized PID controllers

Grahic Jump Location
Fig. 24

Response of the pitch joint motor with PID and optimized PID controllers



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In