Open Journal Systems

Implementasi Pengendali Logika Fuzzy dengan Tapis Kalman pada Kendali Kecepatan Motor Pneumatik

       Budi Setiadi, Sudrajat Sudrajat, Endang Habinuddin, Siswoyo Siswoyo

Abstract


Motor pneumatik bekerja dengan cara mengubah energi tekanan udara menjadi putaran mekanik. Sistem kendali kecepatan motor pneumatik diperlukan agar motor dapat berputar sesuai dengan nilai yang diinginkan. Namun, pada penerapannya seringkali hasil pengukuran pada umpan balik oleh sensor mengandung derau yang menyebabkan menurunnya kinerja pengendalian. Penelitian ini mengimplementasikan kendali kecepatan motor pneumatik menggunakan logika fuzzy dengan menambahkan tapis Kalman. Logika fuzzy dirancang dengan masukan galat dan perubahannya, sedangkan keluaran berupa nilai bukaan katup. Tapis Kalman dirancang untuk meminimalkan derau pengukuran sensor dengan mengatur konstanta kovarian derau proses (Q). Hasil pengujian menunjukkan bahwa pengendali mampu menghasilkan respon kecepatan dengan rise time dan settling time kurang dari 5 detik, tanpa overshoot, error steady-state kurang dari 2 detik, dan waktu pemulihan kurang dari 6 detik ketika diberi gangguan.


  http://dx.doi.org/10.31544/jtera.v8.i1.2022.41-48

Keywords


motor pneumatik; logika fuzzy; tapis Kalman; kendali kecepatan

Full Text:

  PDF

References


K. A. Roni and C. Cekdin, Sistem Kendali Proses Produksi. Penerbit Andi, 2020.

A. Ma’arif, I. Iswanto, A. A. Nuryono, and R. I. Alfian, “Kalman Filter for Noise Reducer on Sensor Readings,†Signal Image Process. Lett., vol. 1, no. 2, pp. 11–22, 2019.

Q. Lim, Y. He, and U. X. Tan, “Real-Time Forward Collision Warning System Using Nested Kalman Filter for Monocular Camera,†2018 IEEE Int. Conf. Robot. Biomimetics, ROBIO 2018, pp. 868–873, 2018.

M. Schimmack, B. Haus, and P. Mercorelli, “An Extended Kalman Filter as an Observer in a Control Structure for Health Monitoring of a Metal-Polymer Hybrid Soft Actuator,†IEEE/ASME Trans. Mechatronics, vol. 23, no. 3, pp. 1477–1487, 2018.

A. Poulose, B. Senouci, and D. S. Han, “Performance Analysis of Sensor Fusion Techniques for Heading Estimation Using Smartphone Sensors,†IEEE Sens. J., vol. 19, no. 24, pp. 12369–12380, 2019.

F. A. Ruslan, A. M. Samad, and R. Adnan, “Modelling of flood prediction system using hybrid NNARX and Extended Kalman Filter,†Proc. - 2017 IEEE 13th Int. Colloq. Signal Process. its Appl. CSPA 2017, no. March, pp. 149–152, 2017.

W. P. Nwadiugwu, S. H. Kim, and D. S. Kim, “Precise-point-positioning estimations for recreational drones using optimized cubature-extended kalman filtering,†IEEE Access, vol. 9, pp. 134369–134383, 2021.

X. Liu, X. Liu, W. Zhang, and Y. Yang, “Interacting Multiple Model UAV Navigation Algorithm Based on a Robust Cubature Kalman Filter,†IEEE Access, vol. 8, pp. 81034–81044, 2020.

W. Song, “An integrated GPS/vision UAV navigation system based on Kalman filter,†Proc. 2020 IEEE Int. Conf. Artif. Intell. Inf. Syst. ICAIIS 2020, pp. 376–380, 2020.

P. Niedermayr, L. Alberti, S. Bolognani, and R. Abl, “Implementation and Experimental Validation of Ultrahigh-Speed PMSM Sensorless Control by Means of Extended Kalman Filter,†IEEE J. Emerg. Sel. Top. Power Electron., vol. 10, no. 3, pp. 3337–3344, 2022.

X. Lai, T. Yang, Z. Wang, and P. Chen, “IoT implementation of Kalman Filter to improve accuracy of air quality monitoring and prediction,†Appl. Sci., vol. 9, no. 9, 2019.

G. Ariante, U. Papa, S. Ponte, and G. Del Core, “UAS for positioning and field mapping using LIDAR and IMU sensors data: Kalman filtering and integration,†2019 IEEE 5th Int. Work. Metrol. Aerosp., no. July, pp. 522–527, 2019.

F. Z. Raihan, B. Setiadi, H. Purnama, V. A. Wijayakusuma, and K. Kunci, “Kendali Kecepatan Vane Motor Pneumatik Berbasis Kendali Fuzzy,†pp. 4–5, 2021.

B. Setiadi, S. W. Jadmiko, H. Purnama, and F. Z. Raihan, “Aplikasi Algoritma Fuzzy Sugeno pada Kendali Kestabilan Putaran Motor Pneumatik,†vol. 7, no. 1, pp. 143–148, 2022.

A. A. Ashari, E. Setiawan, and D. Syauqi, “Sistem Navigasi Waypoint Pada Robot Beroda Berdasarkan Global Positioning System Dan Filter Kalman,†vol. 4, no. 7, pp. 2075–2082, 2020.

Y. Xu, K. Xu, J. Wan, Z. Xiong, and Y. Li, “Research on Particle Filter Tracking Method Based on Kalman Filter,†Proc. 2018 2nd IEEE Adv. Inf. Manag. Commun. Electron. Autom. Control Conf. IMCEC 2018, no. Imcec, pp. 1564–1568, 2018.




DOI: http://dx.doi.org/10.31544/jtera.v8.i1.2022.41-48
Abstract 106 View    PDF viewed = 65 View

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 JTERA (Jurnal Teknologi Rekayasa)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Copyright @2016-2023 JTERA (Jurnal Teknologi Rekayasa) p-ISSN 2548-737X e-ISSN 2548-8678.

  Lisensi Creative Commons

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

JTERA Editorial Office:
Politeknik Sukabumi
Jl. Babakan Sirna 25, Sukabumi 43132, West Java, Indonesia
Phone/Fax: +62 266215417
Whatsapp: +62 81809214709
Website: https://jtera.polteksmi.ac.id
E-mail: jtera@polteksmi.ac.id