Position Control of 1-DOF High-Precision Rotary Table using Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller

  • Hendri Maja Saputra Research Center for Electrical Power and Mechatronics, Indonesian Institute of Sciences, Indonesia
  • Abdurrahman Nurhakim Department of Electrical Engineering, UIN Sunan Gunung Djati Bandung, Indonesia
  • Sapdo Utomo Research Center for Electrical Power and Mechatronics, Indonesian Institute of Sciences, Indonesia
Keywords: ANFIS, position control, BLDC motor, rotary table, response time


Research of position control of 1-DOF high-precision rotary table using adaptive Neuro-Fuzzy inference system (ANFIS) controller has been done. In the closed-loop system without a controller, the response was oscillating and pounding caused by inertial torque. It because a rotary table receives a considerable load. Based on this, the ANFIS controller is needed to eliminate oscillations and compensate for the inertia. The result shows that there was no oscillation or overshoot with the steady-state error value of 2.27% for the reference angle of 45°, valued at 0.10% reference angle of 180°, and valued at 0% reference angle of 360°. The result proves that ANFIS controllers can eliminate oscillations with and compensate for inertia.


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C. Xu, Y. Li, Q. Liu, Y. Huang, H. Wang, and Y. Xia, “Target Detection Based on a Rotary Table-Mounted Synthetic Aperture Radar System,†in 22nd International Conference on Digital Signal Processing (DSP), 2017. DOI: https://doi.org/10.1109/ICDSP.2017.8096147

Y. Jiao, Z. Dong, Y. Ding, and P. Liu, “Optimal arrangements of scanning heads for self-calibration of angle encoders,†IOP Sci., vol. 28, no. 10, 2017. DOI: https://doi.org/10.1088/1361-6501/aa8545

F. W. Huo, D. M. Guo, G. Feng, R. K. Kang, and R. L. Wang, “A new kinematics for ultra precision grinding of conical surfaces using a rotary table and a cup wheel,†Int. J. Mach. Tools Manuf., vol. 59, pp. 34–45, 2012. DOI: https://doi.org/10.1016/j.ijmachtools.2012.03.008

A. T. M. Willemsen, C. Frigo, and H. B. K. Boom, “Lower extremity angle measurement with accelerometers - error and sensitivity analysis,†IEEE Trans. Biomed. Eng., vol. 38, no. 12, pp. 1186–1193, 1991.

B. Denkena, D. Dahlmann, F. Floeter, and T. Bruehne, “Conceptual design for electromagnetic guided rotary table in machine tools,†in Procedia CIRP, 2014, vol. 24, no. Mic, pp. 80–85. DOI: https://doi.org/10.1016/j.procir.2014.08.010

F. Huo, D. Guo, Z. Li, G. Feng, and R. Kang, “Generation of rotationally symmetric surfaces by infeed grinding with a rotary table and a cup wheel,†Precis. Eng., vol. 37, no. 2, pp. 286–298, 2013. DOI: https://doi.org/10.1016/j.precisioneng.2012.09.007

W. Kokuyama, T. Watanabe, H. Nozato, and A. Ota, “Measurement of Angle Error of Gyroscopes Using a Rotary Table Enhanced by Self-calibratable Rotary Encoder,†in International Symposium on Inertial Sensors and Systems (ISISS) Proceedings, 2015, pp. 0–3. DOI: https://doi.org/10.1109/ISISS.2015.7102363

J. S. Lee, S. W. Jang, J. G. Choi, and T. G. Lee, “North-Finding System using Multi-Position Method with a 2-Axis Rotary Table for a Mortar,†no. c, pp. 1–6, 2016. DOI: https://doi.org/10.1109/JSEN.2016.2582504

R. V Ermakov, “Angular Velocity Estimation of Rotary Table Bench Using Aggregate Information from the Sensors of Different Physical Nature,†in Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 2017, pp. 820–825. DOI: https://doi.org/10.1109/EIConRus.2017.7910683

K. Ito, W. Maebashi, J. Ikeda, and M. Iwasaki, “Fast and Precise Positioning of Rotary Table Systems by Feedforward Disturbance Compensation Considering Interference Force,†in 37th Annual Conference of the IEEE Industrial Electronics, 2011, pp. 3382–3387. DOI: https://doi.org/10.1109/IECON.2011.6119855

J. Mo, Z. Qiu, J. Wei, and X. Zhang, “Adaptive positioning control of an ultrasonic linear motor system,†Robot. Comput. Integr. Manuf., vol. 44, pp. 156–173, 2017. DOI: https://doi.org/10.1016/j.rcim.2016.08.011

R. Rahmayanti, S. Utomo, H. M. Sapdutra, “Effect of Digital PWM Command Signal on Steady State Speed Response of BLDC Motor,†J. Teknol. Indones., vol. 38, no. 3, 2015.

M. R. Faieghi and S. M. Azimi, “Design an Optimized PID Controller for Brushless DC Motor by Using PSO and Based on NARMAX Identified Model with ANFIS,†in 12th International Conference on Computer Modelling and Simulation, 2010, no. 1, pp. 1–6. DOI: https://doi.org/10.1109/UKSIM.2010.12

K. Premkumar and B. V Manikandan, “Speed control of Brushless DC motor using bat algorithm optimized Adaptive Neuro-Fuzzy Inference System,†Appl. Soft Comput. J., vol. 32, pp. 403–419, 2015. DOI: https://doi.org/10.1016/j.asoc.2015.04.014

D. M. Karantonis, M. R. Narayanan, M. Mathie, N. H. Lovell, and B. G. Celler, “Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring,†IEEE Trans. Inf. Technol. Biomed., vol. 10, no. 1, pp. 156–167, 2006. DOI: https://doi.org/10.1109/TITB.2005.856864

A. M. Zaki, M. El-bardini, F. A. S. Soliman, and M. Mabrouk, “Embedded two level direct adaptive fuzzy controller for DC motor speed control,†AIN SHAMS Eng. J., 2015.

H. Wongsuwarn and D. Laowattana, “Neuro-Fuzzy Algorithm for a Biped Robotic System,†vol. 10140, no. 3, pp. 858–864, 2008.

W. M. Jasim and E. T. Yassen, “High Order Robotics ARM Modelling Based on ANFIS Technique,†J. Engineering Appl. Sci., vol. 12, no. 9, 2017.

N. T. Vu, N. P. Tran, and N. H. Nguyen, “Adaptive Neuro-Fuzzy Inference System Based Path Planning for Excavator Arm,†J. Robot., vol. 2018, p. 7, 2018.

Y. I. Al and M. Mieee, “ANFIS-Inverse-Controlled PUMA 560 Workspace Robot with Spherical Wrist,†in International Symposium on Robotics and Intelligent Sensors 2012 (IRIS 2012), 2012, vol. 41, pp. 700–709. DOI: https://doi.org/10.1016/j.proeng.2012.07.232

B. A. A. Omar, A. Y. M. Haikal, and F. F. G. Areed, “Design adaptive neuro-fuzzy speed controller for an electro-mechanical system,†Ain Shams Eng. J., vol. 2, no. 2, pp. 99–107, 2011. DOI: https://doi.org/10.1016/j.asej.2011.07.003

J. R. Jang, “ANFIS : Adaptive-Network-Based Fuzzy Inference System,†vol. 23, no. 3, pp. 665–685, 1993. DOI: https://doi.org/10.1109/21.256541

How to Cite
Saputra, H. M., Nurhakim, A., & Utomo, S. (2019). Position Control of 1-DOF High-Precision Rotary Table using Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller. EMITTER International Journal of Engineering Technology, 7(2), 511-523. https://doi.org/10.24003/emitter.v7i2.399