Real-time control of load cell based two-wheel balancing robot using PID controller

Abstract

In this study, real-time application of load cell-based two-wheel balancing robot (TWBR) has been realized. The control of the system is provided by the PID controller with four load cells placed on the twowheel balancing robot. The analog signals of the weight information coming from the load cell are converted into digital signals with the 24-bit resolution HX711 module. The data converted into digital signals are interpreted through the microprocessor over the synchronous serial communication protocol. The output of the dynamic model of the system can be updated instantly, according to the measured weight information. This process updates the maximum pitch attitude. Thus, the control of the two-wheel balancing robot is facilitated and the risk of the user falling off the vehicle is reduced. The control of the system is carried out using an ARM architecture based STM32F103C8T6 microprocessor. Current values and motor rotation speed information of the Brushless Direct Current Motor (BLDC) are obtained during the real time application of the vehicle. In addition, 12 different data are recorded on the SD card with using the SD card module. As a result of the data recorded on the card, the correlation value of each load cell is obtained as 0.99 in the repetition test of the load cells. As a result, the measurement error rate of the weight on the two-wheel balancing robot is obtained as 1%.

Keywords

• Two-Wheel balancing robot (TWBR)
• Loadcell
• Electrical vehicle
• PID controller
• Microprocessor

Yük hücresi temelli iki tekerlekli denge robotunun PID kontrolör kullanarak gerçek zamanlı kontrolü

Öz

Bu çalışmada, yük hücresi temelli iki tekerlekli denge robotu (İTDR)’nin gerçek zamanlı uygulaması gerçekleştirilmiştir. İki tekerlekli denge robotun üzerine yerleştirilen dört adet yük hücresi ile sistemin kontrolü PID kontrolör tarafından sağlanmaktadır. Yük hücresinden gelen kütle bilgilerine ait analog sinyaller 24 bit çözünürlüklü HX711 tümleşik entegre ile sayısal sinyallere dönüştürülmektedir. Sayısal sinyallere dönüştürülen veriler senkron seri haberleşme protokolü üzerinden mikroişlemci ile aracılığı ile anlamlandırılmaktadır. Ölçülen kütle bilgisine göre sistemin dinamik modelinin çıkışı anlık olarak güncellenebilmektedir. Bu işlem maksimum yunuslama açısını güncellemektedir. Böylelikle iki tekerlekli denge robotunun kontrolü kolaylaşmakta ve kullanıcının araç üzerinden düşme riski azaltılmaktadır. Sistemin kontrolü ARM mimariye sahip STM32F103C8T6 mikroişlemci kullanılarak gerçekleştirilmiştir. Aracın gerçek zamanlı uygulaması sırasında Fırçasız Doğru Akım Motoru’nun (FDAM) akım değerleri ve motor dönüş hız bilgileri elde edilmektedir. Ayrıca SD kart modülü yardımıyla 12 adet farklı veri SD kart üzerine kaydedilmektedir. Karta kaydedilen veriler sonucunda yük hücrelerinin tekrarlama testinde her bir yük hücresinin korelasyon değeri 0,99 olarak elde edilmiştir. Sonuçta iki tekerlekli denge robotun üzerindeki kütlenin ölçüm hata oranı %1 olarak elde edilmiştir.

Anahtar Kelimeler

• İki tekerlekli denge robotu (İTDR)
• Yük hücresi
• Elektrikli araç
• PID kontrolör
• Mikrodenetleyici

Kaynakça

1. [1] Kamen DL, Ambrogi RR, Duggan RJ, Field JD, Heinzmann RK, Amesbury B, Langenfeld CC. “U.S. Patent No: 6, 302,230”. US. Patent and Trademark Office, Washington, DC, 2001.
2. [2] Chiu CH, Peng YF. “Design and ımplement of the selfdynamic controller for two-wheel transporter”. 2006 IEEE International Conference on Fuzzy Systems, Vancouver, BC, Canada, 16-21 July 2006.
3. [3] Charles E, Forrest Jr. A Neural Network Control System for the Segway Robotic Mobility Platform. PhD Thesis, North Carolina State University, Raleigh, USA, 2006.
4. [4] Jeong S, Takahashi T. “Wheeled ınverted pendulum type assistant robot: Inverted mobile, standing and sitting motions”. 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego CA, USA, 29 October-2 November 2007.
5. [5] Butler LJ, Bright G. “Feedback control of a self-balancing materials handling robot”. 10th International Conference on Control, Automation, Robotics and Vision, Hanoi, Vietnam, 17-20 December 2008.
6. [6] Grepl R. “Balancing wheeled robot: Effective modelling sensory processing and simplified control”. Engineering Mechanics, 16(2), 141-154, 2009.
7. [7] Küçük D. Design of Two Wheeled Twin Rotored Hybrid Robotic Platform. MSc Thesis, Atilim University, İstanbul, Turkey, 2010.
8. [8] Pinto LJ, Kim DH, Lee JY, Han CS. “Development of a Segway robot for an intelligent transport system”. In 2012 IEEE/SICE International Symposium on System Integration (SII), Fukuoka, Japan, 16-18 December 2012.

9. [9] Draz MU, Ali MS, Majeed M, Ejaz U, Izhar U. “Segway electric vehicle”. 2012 International Conference of Robotics and Artificial Intelligence, Rawalpindi, Pakistan, 22-23 October 2012.
10. [10] Han M, Kim K, Lee J. “Implementation of unicycle segway using unscented Kalman filter in LQR control”. In 2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Jeju, Korea, 30 October-2 November 2013.
11. [11] Umay Y. İki Tekerlekli Kendini Dengeleyen Mobil Bir Aracın Kontrolü. Yüksek Lisans Tezi, Fırat Üniversitesi, Elâzığ, Türkiye, 2018.
12. [12] Pham DB, Kim H, Kim J, Lee S G. “Balancing and transferring control of a ball segway using a double-loop approach [applications of control]”. IEEE Control Systems Magazine, 38(2), 15-37, 2018.
13. [13] Steiner M. ROS Navigation Stack On A Loomo Segway Robot. BSc Thesis, Vienna University, Munich, Vienna, 2018.
14. [14] Çelik Y. Nesne Takibi Yapan Denge Robot Tasarımı. Yüksek Lisans Tezi, Kahramanmaraş Sütçü İmam Üniversitesi, Kahramanmaraş, Türkiye, 2018.
15. [15] Morantes J, Espitia D, Morales O, Jiménez R, Aviles O. “Control system for a segway”. International Journal of Applied Engineering Research, 13(18), 13-767, 2018.
16. [16] Yedamale P. “Brushless DC (BLDC) motor Fundamentals”. Microchip Technology Inc, 20(1), 3-15, 2003.
17. [17] Get Your Guide Web Interface. “Segway Tour Organizations”. https://www.getyourguide.com.tr/s/?q=Segway&custo merSearch=1&p=1, (18.08.2020).
18. [18] Önkol M. İki Tekerlekli Hareketli Platform Üzerindeki Bir Robot Kolun Modellenmesi ve Kontrolü. Yüksek Lisans Tezi, TOBB Ekonomi ve Teknoloji Üniversitesi, Ankara, Türkiye, 2018.
19. [19] Polat B. Denge Robotu Tasarımı ve Modellenmesi/Balancing Robot Design and Modeling. Yüksek Lisans Tezi, Fırat Üniversitesi, Elâzığ, Türkiye, 2018.
20. [20] Sasaki M, Yanagihara N, Matsumoto O, Komoriya K. “Steering control of the personal riding-type wheeled mobile platform (PMP)”. 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, AB, Canada, 2-6 August 2005.
21. [21] Rechkemmer SK, Zang X, Zhang W, Sawodny O. “Lifetime optimized charging strategy of Li-ion cells based on daily driving cycle of electric two-wheelers”. Applied Energy, 251(1), 1-13, 2019.
22. [22] Prabhakar G, Selvaperumal S, Pugazhenthi P N, Umamaheswari K, Elamurugan P. “Online optimization based model predictive control on two wheel Segway system”. Materials Today: Proceedings, 33(7), 3846-3853, 2020.
23. [23] Sevekari P, Tamhane B, Kurode S. “Robust control for stable and safe performance of a two wheeled human transporter”. IFAC-PapersOnLine, 53(1), 616-621, 2020.
24. [24] Mohammed IK, Abdulla AI. “Balancing a segway robot using LQR controller based on genetic and bacteria foraging optimization algorithms”. Telkomnika, 18(5), 2642-2653, 2020.
25. [25] Yun H, Zhang H, Lee J. “stability ımprovement of segway based on tire model using the SEA”. Robotica, 1, 1-13, 2020.
26. [26] Ahmed AA, Saleh Alshandoli AF. "On replacing a PID controller with neural network controller for segway". 2020 International Conference on Electrical Engineering (ICEE), Istanbul, Turkey, 25-27 September 2020
27. [27] Kelek MM, Çelik İ, Fidan U, Oğuz Y. “The Simulation of mathematical model of outer rotor BLDC motor”. 4th International Symposium on Innovative Approaches in Engineering and Natural Sciences, Samsun, Turkey, 22-24 November 2019.