Rehabilitasyon Robotlarının Kontrolü için Bulanık Mantık ve PID Denetleyicinin Karşılaştırılması

Öz

Doğrusal olmayan bir sistemin yanıtı genellikle bir doğrusal denetleyici kullanılarak istenen bir modele göre şekillendirilemez. PID denetleyiciler gibi geleneksel model tabanlı doğrusal denetleyicilerle doğrusal olmayan durumların gerçekleştirilmesi zordur ve denetleyicinin düzgün çalışması için sıfırlama önleyici sarma, geciktirilmiş integral eylem vb. gibi birçok ek önlem dahil edilmelidir. Bu nedenle doğrusal olmayan sistemler için genellikle Bulanık Mantık Kontrol gibi kontrol yöntemleri kullanılır. Bulanık Mantık, gömülü kontrol için hem doğrusal hem de doğrusal olmayan sistemlerin geliştirilmesinde uygulanabilen alternatif bir tasarım metodolojisidir. Tasarımcılar, bulanık mantık kullanarak daha düşük geliştirme maliyetleri, üstün özellikler ve daha iyi son ürün performansı sağlayabilirler. Bu sebeple bu çalışmada rehabilitasyon robotlarının kontrolü için MATLAB/Simulink ortamında bir Bulanık Kontrol denetleyici tasarlanmıştır. Daha sonra kontrol etkisi analiz edilip PID denetleyicinin etkisiyle karşılaştırılmıştır. Karşılaştırma sonucunda bulanık mantık denetleyici, PID kontrolünden özellikle yanıt süresi, kararlı durumdaki hata ve aşım gibi çeşitli parametrelerde daha üstün performans sergilemiştir.

Anahtar Kelimeler

• PID
• Bulanık Mantık
• Doğrusal Kontrol
• Doğrusal Olmayan Kontrol
• Rehabilitasyon Robotu.
• Fuzzy Logic
• Linear Control
• Non-Linear Control
• Rehabilitation Robot

Comparison of Fuzzy Logic and PID Controller for Control of Rehabilitation Robots

Öz

The response of a nonlinear system cannot usually be shaped into a desired model using a linear controller. Non-linear situations are difficult to realize with traditional model-based linear controllers such as PID controllers, and anti-reset winding, delayed integral action, etc., are required for the controller to work properly. Many additional measures should be included, such as for this reason, control methods such as Fuzzy Logic Control are often used for nonlinear systems. Fuzzy Logic is an alternative design methodology that can be applied to the development of both linear and nonlinear systems for embedded control. By using fuzzy logic, designers can achieve lower development costs, superior features, and better end-product performance. For this reason, in this study, a Fuzzy Control controller was designed in MATLAB/Simulink environment for the control of rehabilitation robots. Then the control effect was analyzed and compared with the effect of the PID controller. As a result of the comparison, the fuzzy logic controller outperformed the PID control in various parameters such as response time, steady state error and overshoot.

Anahtar Kelimeler

• Fuzzy Logic
• Linear Control
• Non-Linear Control
• Rehabilitation Robot

Kaynakça

1. Achkoski, J., Temelkovski, B., & Stainov, R. (2016). Fuzzy logic controller development for classification of patient status based on physiological parameters. 2016 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), 1-5.
2. Ali, A., Ahmed, S.F., Kadir, K.A., Joyo, M.K., & Yarooq, R.N. (2018). Fuzzy PID controller for upper limb rehabilitation robotic system. 2018 IEEE International Conference on Innovative Research and Development (ICIRD), 1-5.
3. Bharti, R., Trivedi, R., & Padhy, P.K. (2018). Design of Optimized PID Type Fuzzy Logic Controller for Higher Order System. 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), 760-764.
4. Bhimte, R., Bhole, K., & Shah, P. (2018). Fractional Order Fuzzy PID Controller for a Rotary Servo System. 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), 538-542.
5. Chaib, S., Netto, M.S., & Mammar, S. (2004). H/sub /spl infin//, adaptive, PID and fuzzy control: a comparison of controllers for vehicle lane keeping. IEEE Intelligent Vehicles Symposium, 2004, 139-144.
6. Jianhua, Y., Wenqi, L., & Wei, L. (2006). Fuzzy Predictive Control of Steam Dryness for Steam-injection Boiler. 2007 Chinese Control Conference, 395-398.
7. Kadirkamanathan, V. (1999). Fuzzy Logic and Control: Software and Hardware Applications. Mohammad Jamshidi, Nader Vadiee and Timothy J. Ross (eds.). Artificial Intelligence Review, 13, 337-339.
8. Kumar, R., & Kumar, M. (2015). Improvement power system stability using Unified Power Flow Controller based on hybrid Fuzzy Logic-PID tuning In SMIB system. 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), 815-819.

9. Liu, R., Hu, E., Zhu, Z., Mao, L., & Ma, Z. (2020). Study on temperature control system of ceramic kiln based on fuzzy PID cascade. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 1, 1769-1772.
10. Mamdani, E.H. (1974). Applications of fuzzy algorithms for control of a simple dynamic plant. Proceedings of the IEEE.
11. Namazov, M. (2018). Fuzzy Logic Control Design for 2-Link Robot Manipulator in MATLAB/Simulink via Robotics Toolbox. 2018 Global Smart Industry Conference (GloSIC), 1-5.
12. Obaid, Z.A., Sulaiman, N.B., Marhaban, M.H., & Hamidon, M.N. (2010). Analysis and Performance Evaluation of PD-like Fuzzy Logic Controller Design Based on Matlab and FPGA.
13. Ozkaya, U. and Seyfi, L., (2016), A novel fuzzy logic model for intelligent traffic systems, Electronics World, 122(1960), 36-39.
14. Raghappriya, M., Devadharshini, K.M., & Karrishma, S. (2022). Fuzzy Logic Based Maximum Power Point Tracking of Photovoltaic System. Journal of Innovative Image Processing.
15. Sagdatullin, A.M. (2021). Application of Fuzzy Logic and Neural Networks Methods for Industry Automation of Technological Processes in Oil and Gas Engineering. 2021 3rd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA), 715-718.
16. Santos, M., Dormido, S., & de la Cruz, J. (1996). Fuzzy-PID controllers vs. fuzzy-PI controllers. Proceedings of IEEE 5th International Fuzzy Systems, 3, 1598-1604 vol.3.
17. Satoh, H., Kawabata, T., & Sankai, Y. (2009). Bathing care assistance with robot suit HAL. 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO), 498-503.
18. Sugeno, M., & Murakami, K. (1984). Fuzzy parking control of model car. The 23rd IEEE Conference on Decision and Control, 902-903.
19. Suma, V. (2020). A Novel Information retrieval system for distributed cloud using Hybrid Deep Fuzzy Hashing Algorithm. September 2020.
20. Wang, L. (1992). Stable adaptive fuzzy control of nonlinear systems. [1992] Proceedings of the 31st IEEE Conference on Decision and Control, 2511-2516 vol.3.
21. Wang, L. (1994). Adaptive fuzzy systems and control- design and stability analysis.
22. Wei, Z., Doctor, F., Liu, Y., Fan, S., & Shieh, J.S. (2020). An Optimized Type-2 Self-Organizing Fuzzy Logic Controller Applied in Anesthesia for Propofol Dosing to Regulate BIS. IEEE Transactions on Fuzzy Systems, 28, 1062-1072.
23. Zhao, Y., Chen, L., & Yang, Q. (2008). The research on the fault diagnosis for boiler system based on fuzzy neural network. 2008 7th World Congress on Intelligent Control and Automation, 8552-8556.