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The aim of the study is to compare Ziegler-Nichols (Z-N) and Particle Swarm Optimization (PSO) based tuning methods for controller tuning in the driving mechanism of prosthetic limbs. By adopting suitable control strategies like P, PI and PID in the driving system, the positioning of knee and hip joints can be attained in the ideal time of 1.4s for completing one locomotion cycle. The gain constants (KP , KI , and KD) of the controllers were tuned manually and also using Z-N and PSO; thereby appropriate constants were determined so that the joints could be moved to the desired position. The performance of P, PI, and PID controllers were compared and PID was identified as the ideal control strategy which exhibited least error and good stability. It was observed that the conventional Z-N method produced a big overshoot, and so a modern approach called PSO was employed to enhance its capability. The PSO based PID controller optimization resulted in less overshoot as well as it helped in optimizing the gain constants so as to improve the stability of the system when compared to the classical method.
Słowa kluczowe
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Rocznik
Tom
Strony
841--851
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
autor
- Electrical and Electronics Department, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamilnadu, India
autor
- Electronics and Instrumentation Department, Federal Institute of Science And Technology, Ernakulam, Kerala, India
autor
- Biomedical Engineering Department, Manipal Institute of Technology, Karnataka, India
autor
- Mechanical Engineering Department, National Institute of Technology Calicut, Kerala, India
Bibliografia
- 1. Al-Saedi M.I., Wu H., Handroos H., 2013, ANFIS and fuzzy tuning of PID controller for trajectory tracking of a flexible hydraulically driven parallel robot machine, Journal of Automation and Control Engineering, 1, 2, 70-77.
- 2. Ashmi M., Anila M., Jayaraj S., Sivanandan K.S., 2016a, Identification of the best control strategy for the application of prosthetic limbs, Journal of Mechanics in Medicine and Biology, 16, 6, 1-17.
- 3. Ashmi M., Tintu George T., Jayaraj S., Sivanandan K.S., 2016b, A comparative study on neural network, fuzzy logic and neuro-fuzzy technique for the human locomotion angle prediction, Journal of Medical Imaging and Health Informatics, 6, 1-7.
- 4. Dubey S., Srivastava S.K., 2013, A PID controlled real time analysis of DC motor, International Journal of Innovative Research in Computer and Communication Engineering, 1, 8, 1965-1973.
- 5. Dutta R., Kumar N., Pankaj D., 2014, PID control for ambulatory gait orthosis: application of different tuning methods, Advances in Biomedical Engineering Research, 2, 44-49.
- 6. Giriraj Kumar S.M., Jayaraj D., Kishan A.R., 2010, PSO based tuning of a PID controller for a high performance drilling machine, International Journal of Computer Applications, 1, 19, 12-18.
- 7. Irby S.E., 1994, A digital logic controlled electromechanical free knee brace, MSI Thesis, San Diego State University.
- 8. Johnson C.D., 1993, Process Control Instrumentation Technology, Prentice Hall PTR.
- 9. Kaufman K.R., Irby S.E., Mathewson J.W., Wirta B.W., Sutherland D.H., 1996, Energy-efficient knee-ankle-foot orthosis: A case study, Journal of Prosthetics and Orthotics, 8, 3, 79-85.
- 10. Kutilek P., Viteckova S., Svoboda Z., Smrcka P., 2013, The use of artificial neural networks to predict the muscle behavior, Open Engineering (formerly Central European Journal of Engineering), 3, 3, 410-418.
- 11. Lee C.S., Gonzalez R.V., 2008, Fuzzy logic versus a PID controller for position control of a muscle-like actuated arm, Journal of Mechanical Science and Technology, 22, 8, 1475-1482.
- 12. Madić M., Radovanović M., 2014, Possibilities of using the Monte Carlo method for solving machining optimization problems, Facta Universitatis – Series: Mechanical Engineering, 12, 1, 27-36.
- 13. Malcolm L., Sutherland D.H., Cooper L., Wyatt M., 1980, A digital logic-controlled electromechanical orthosis for free-knee gait in muscular dystrophy children, Orthopedic Transactions, 5, 90, b12.
- 14. Mančić M., Petrović E., Nikolić V., Jovanović M., [Rajković P., Simonović M.], 2016, Particle swarm optimization of a heat pump photovoltaic energy system, Facta Universitatis – Series: Working and Living Environmental Protection, 13, 3, 165-176.
- 15. Meshram P.M., Kanojiya R.G., 2012, Tuning of PID controller using Ziegler-Nichols method for speed control of DC motor, IEEE International Conference on Advances in Engineering, Science and Management, 117-122.
- 16. Moosavi M., Eram M., Khajeh A., Mahmoudi O., Piltan F., 2013, Design new artificial intelligence base modified PID hybrid controller for highly nonlinear system, International Journal of Advanced Science and Technology , 57, 5, 45-62.
- 17. Neogi B., Darbar R., Mondal S., Gorai B., 2011, Study of proper tuning of prosthetic limb control system with paraplegia and fatigue condition, IEEE International Conference on Emerging Applications of Information Technology, 79-82.
- 18. Rajanwal K., Shakya R., Patel S., Maurya R.K., 2014, Comparative analysis of PI, PID and fuzzy logic controllers for speed control of DC motor, International Journal of Engineering Research and Technology, 3, 1, 1319-1324.
- 19. Schröder J., Kawamura K., Gockel T., Dillmann R., 2003, Improved control of a humanoid arm driven by pneumatic actuators, Industrial Applications of Informatics and Microsystems, 1-20.
- 20. Solihin M.I., Tack L.F., Kean M.L., 2011, Tuning of PID controller using particle swarm optimization (PSO), International Journal of Advanced Science, Engineering and Information Technology, 1, 4, 458-461.
- 21. Yakimovich T., Lemaire E.D., Kofman J., 2009, Engineering design review of stance-control knee-ankle-foot orthosis, Journal of Rehabilitation Research and Development, 46, 2, 257-267.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
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