طراحی کنترل گسسته فازی تطبیقی مقاوم برای ردیابی مجانبی بازوی ربات هنرمند
محورهای موضوعی : مهندسی الکترونیک
مسلم زارعی
1
,
سیامک آذرگشسب
2
,
نجمه چراغی شیرازی
3
1 - دانشگاه ازاد اسلامی ، بوشهر
2 - دانشگاه یاسوج
3 - گروه برق، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر، ایران
کلید واژه: کنترل زمان گسسته, راهبرد کنترل ولتاژ, بازوی ربات, تخمین‌گر فازی تطبیقی, ردگیری مجانبی,
چکیده مقاله :
بازوهای رباتیک سیستمهای غیرخطی چندمتغیره با تزویج بالا و انواع عدم قطعیت‌ها می‌باشند. اگرچه روش‌های کنترل مقاوم و تطبیقی به منظور غلبه بر عدم قطعیت‌ها که شامل عدم قطعیت پارامتری، دینامیک مدل نشده، اغتشاش خارجی و خطای گسسته‌سازی می‌باشند، پیشنهاد شده‌اند ولی به دلیل پیچیدگی دینامیک ربات با مشکل مواجه هستند. یک سیستم فازی می‌تواند به عنوان یک تقریب‌گر عمومی برای تقریب هر تابع غیرخطی استفاده شود. از این ویژگی سیستمهای فازی در طراحی کنترل‌کننده‌های فازی تطبیقی به خوبی استفاده شده است. سیستمهای کنترل فازی تطبیقی بر مبنای تضمین پایداری برای بدست آوردن قوانین تطبیق طراحی می-شوند. از آنجا که در عمل، قوانین کنترل به‌صورت گسسته پیاده‌سازی می-شوند، در این مقاله، طراحی کنترل‌کننده‌های زمان-گسسته فازی تطبیقی ربات با راهبرد کنترل ولتاژ و تحلیل پایداری سیستمهای کنترل پیشنهادی ارائه شده است. در این مقاله، برای جبران خطای تقریب سیستم فازی روش جدیدی ارائه شده است که نیازی به انتگرال‌گیری از خطای ردگیری ندارد. همچنین، قانون کنترل زمان-گسسته فازی تطبیقی با فیدبک موقعیت پیشنهادی، فقط پس خورد موقعیت مفصل را نیاز دارد.از طرف دیگر، خطای تقریب سیستم فازی و خطای گسسته‌سازی برای ردیابی مجانبی مسیر مطلوب به خوبی جبران شده است. قانون کنترل فازی تطبیقی مقاوم پیشنهادی بر روی یک ربات هنرمند شبیه‌سازی شده است. نتایج شبیه‌سازی نشان می‌دهد که خطای ردگیری ناچیز است و مقدار خطای ردگیری مفصل دوم که دارای بیشترین خطا است در نقطه پایان زمان شبیه‌سازی حدود رادیان می‌باشد. تطبیق پارامترها به خوبی نشان داده شده و همچنین موتورها رفتار خوبی تحت حداکثر مقدار مجاز ولتاژ دارند.
Robot manipulators are nonlinear multivariable systems with high couplings and various uncertainties. Although, adaptive and robust control methods are suggested to overcome the uncertainties including parametric uncertainty, un-modeled dynamics, external disturbances and discretization error, they face many challenges because of the complexity in robot dynamics. A fuzzy system can be used as a universal approximator for any nonlinear system. This feature has been efficiently used to design the adaptive fuzzy controllers. Adaptive fuzzy control systems are designed based on guaranteeing stability. Since practical implementation of the control law is carried out using digital processors, designing a discrete-time adaptive fuzzy controller for robot manipulators based on the voltage control strategy and proposed control systems stability analysis is suggested in this paper. In this paper, a new method is developed for compensating the approximation error of the fuzzy system which does not needed integration of tracking error. Moreover, the proposed discrete-time adaptive fuzzy with position feedback control law requires feedbacks of joint positions only. On the other hand, the fuzzy system approximation error and the discretization error are well compensated for asymptotic tracking of the desired path. The proposed robust Adaptive Fuzzy control law is simulated on an articulated robot. The simulation results show that the tracking error is negligible and the value of the second joint tracking error with the highest error at the end point of the simulation time is about radians. The parameters are well matched and the motors behave well under the maximum allowable voltage.
[1] M. W. Spong and M. Vidyasagar, “Robot dynamic and control”, Wiley, New York, 1989.
[2] P. Francesco and G. Oaplo,”An Example o Collaborative Robot for Automotive and General Industry Application ”, Procedia Manufacturing, Vol. 11, pp. 338-345, 2017.
[3] Y. Guotao, Z. Zhenghe, G. Hu, L. Zhenfeng, Y. Huashan and L. Lei,”Flexible Punching System using Industrial Robots for Automotive Panels”,Robotics and Computer-Integrated Manufacturing, Vol. 52, pp. 92-99, 2018.
[4] K. Ogata, “Discrete-Time Control Systems”, Prentice-Hall, NJ, 1987.
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[6] O. Akhrif, F. A. Okou, L. A. Dessaint and R. Champagne, “Application of a multivariable feedback linearization scheme for rotor angle stability and voltage regulation of power systems”, IEEE Transaction on power systems, Vol. 14, No. 2, pp. 620-628, 1999.
[7] G. Zheng, Y. zhou and M. Ju, “Robust control of a silicone soft robot using neural networks”, ISA Transactions, Vol. 100, pp. 38-45, 2020.
[8] Y. Zhou, H. Hu, L. Xia and Y. Chen, “A distributed approach to robust control of multi-robot systems”, Automatica, Vol. 98, pp. 1-13, 2018.
[9] Fateh M.M. and Soltanpour M. R, “Robust task-space control of robot manipulators under imperfect transportation of control space”, International Journal of Innovative Computing, Information and Control,Vol. 5, No. 11, pp. 3949-3960, 2009.
[10] R. Qi and M. A Brdys, “Indirect adaptive fuzzy control for nonlinear systems with online modeling”, in: Proc. Internat. Conf. Control, Glasgow,Scotland, 2006,pp.23-28.
[11] R. Subramaniam, D. Song and Y. H. Joo, ” T-S Fuzzy Based Sliding Mode Controller Design for Discrete Nonlinear Model and its Applications ”, Information Siences, Vol. 519, pp. 183–199, 2020.
[12] T. K. J. Koo, “Model reference adaptive fuzzy control of robot manipulator”, IEEE International Conference on Systems, Man and Cybernetics, Vol. 1, pp. 424-429,1995.
[13] M. Bahita and K. Belarbi, “ Model Reference Neural-Fuzzy Adaptive Control of the Concentration in a Chemical Reactor (CSTR)”, IFAC-Papers Online, Vol. 49, No. 29, pp. 158–162, 2016.
[14] M. M. Fateh, “On the voltage based control of electrical manipulators”, International Journal of Control”, Automation and System, Vol. 6, No.5, pp. 702–712, 2008.
[15] K. Yi, J. Han, X. Liang and Y. He, “Contact Transition Control with Acceleration Feedback Enhancement for a Quadrotor”, ISA Transactions, vol.109,pp.288-294, March 2021.
[16] V. Helma, M. Goubej and O. Jezek, “ Acceleration Feedback in PID Controlled Elastic Drive Systems”, IFAC-Papers Online, Vol. 51, No. 4, pp. 214–219, 2018.
[17] R. Ortega and M. W. Spong, “Adaptive motion control of rigid robots: a tutorial”, Proceedings of the 27th conference on decision and control, 1988, pp. 1575-1584.
[18] M. M. Fateh, “Robust control of electrical manipulators by joint acceleration”, International Journal of Innovative Computing, Information and Control, Vol. 6, No. 12, pp. 5501-5510, 2010.
[19] M. M. Fateh, “Robust control of electrical manipulators by reducing the effects of uncertainties”, World Applied Sciences Journal, Vol. 7, Special Issue, pp.161–167, 2009.
[20] M. M. Fateh, “Robust fuzzy control of electrical manipulators”, Journal of Intelligent and Robotic Systems, Vol. 60, No. 3, pp. 415-434, 2010.
[21] M. R. Soltanpour and M. M. Fateh, “Adaptive robust tracking control of robot manipulators in the task-space under uncertainties”, Australian Journal of Basic and Applied Sciences, Vol. 3, No. 1, pp. 308–322, 2009.
[22] L. X. Wang, “Adaptive fuzzy systems and control”, Prentice Hall, 1994.
[23] Z. Qu and D. M. Dawson, “Robust tracking control of robot manipulators”, IEEE Press, Inc., New York, 1996.
[24] M. M. Fateh, “Robust control of flexible-joint robots using voltage control strategy”, Nonlinear Dynamics, Vol. 67, No. 2, pp.1525–1537, 2012.
[25] M. M. Fateh and S. Khorashadizadeh, “Robust control of electrically driven robots by adaptive fuzzy estimation of uncertainty,” Nonlinear Dynamics (ND), Vol. 63, No. 4, pp. 1465-1477, 2012.
_||_[1] M. W. Spong and M. Vidyasagar, “Robot dynamic and control”, Wiley, New York, 1989.
[2] P. Francesco and G. Oaplo,”An Example o Collaborative Robot for Automotive and General Industry Application ”, Procedia Manufacturing, Vol. 11, pp. 338-345, 2017.
[3] Y. Guotao, Z. Zhenghe, G. Hu, L. Zhenfeng, Y. Huashan and L. Lei,”Flexible Punching System using Industrial Robots for Automotive Panels”,Robotics and Computer-Integrated Manufacturing, Vol. 52, pp. 92-99, 2018.
[4] K. Ogata, “Discrete-Time Control Systems”, Prentice-Hall, NJ, 1987.
[5] J.S.H. Tsai, C.M. Chen and L.S Shieh,”Digital Modelling Ideal State reconstructor and Control for Time-Delay Sampled-Data systems”, Applied Mathematical Modelling, Vol. 15, pp. 576-585,1991.
[6] O. Akhrif, F. A. Okou, L. A. Dessaint and R. Champagne, “Application of a multivariable feedback linearization scheme for rotor angle stability and voltage regulation of power systems”, IEEE Transaction on power systems, Vol. 14, No. 2, pp. 620-628, 1999.
[7] G. Zheng, Y. zhou and M. Ju, “Robust control of a silicone soft robot using neural networks”, ISA Transactions, Vol. 100, pp. 38-45, 2020.
[8] Y. Zhou, H. Hu, L. Xia and Y. Chen, “A distributed approach to robust control of multi-robot systems”, Automatica, Vol. 98, pp. 1-13, 2018.
[9] Fateh M.M. and Soltanpour M. R, “Robust task-space control of robot manipulators under imperfect transportation of control space”, International Journal of Innovative Computing, Information and Control,Vol. 5, No. 11, pp. 3949-3960, 2009.
[10] R. Qi and M. A Brdys, “Indirect adaptive fuzzy control for nonlinear systems with online modeling”, in: Proc. Internat. Conf. Control, Glasgow,Scotland, 2006,pp.23-28.
[11] R. Subramaniam, D. Song and Y. H. Joo, ” T-S Fuzzy Based Sliding Mode Controller Design for Discrete Nonlinear Model and its Applications ”, Information Siences, Vol. 519, pp. 183–199, 2020.
[12] T. K. J. Koo, “Model reference adaptive fuzzy control of robot manipulator”, IEEE International Conference on Systems, Man and Cybernetics, Vol. 1, pp. 424-429,1995.
[13] M. Bahita and K. Belarbi, “ Model Reference Neural-Fuzzy Adaptive Control of the Concentration in a Chemical Reactor (CSTR)”, IFAC-Papers Online, Vol. 49, No. 29, pp. 158–162, 2016.
[14] M. M. Fateh, “On the voltage based control of electrical manipulators”, International Journal of Control”, Automation and System, Vol. 6, No.5, pp. 702–712, 2008.
[15] K. Yi, J. Han, X. Liang and Y. He, “Contact Transition Control with Acceleration Feedback Enhancement for a Quadrotor”, ISA Transactions, vol.109,pp.288-294, March 2021.
[16] V. Helma, M. Goubej and O. Jezek, “ Acceleration Feedback in PID Controlled Elastic Drive Systems”, IFAC-Papers Online, Vol. 51, No. 4, pp. 214–219, 2018.
[17] R. Ortega and M. W. Spong, “Adaptive motion control of rigid robots: a tutorial”, Proceedings of the 27th conference on decision and control, 1988, pp. 1575-1584.
[18] M. M. Fateh, “Robust control of electrical manipulators by joint acceleration”, International Journal of Innovative Computing, Information and Control, Vol. 6, No. 12, pp. 5501-5510, 2010.
[19] M. M. Fateh, “Robust control of electrical manipulators by reducing the effects of uncertainties”, World Applied Sciences Journal, Vol. 7, Special Issue, pp.161–167, 2009.
[20] M. M. Fateh, “Robust fuzzy control of electrical manipulators”, Journal of Intelligent and Robotic Systems, Vol. 60, No. 3, pp. 415-434, 2010.
[21] M. R. Soltanpour and M. M. Fateh, “Adaptive robust tracking control of robot manipulators in the task-space under uncertainties”, Australian Journal of Basic and Applied Sciences, Vol. 3, No. 1, pp. 308–322, 2009.
[22] L. X. Wang, “Adaptive fuzzy systems and control”, Prentice Hall, 1994.
[23] Z. Qu and D. M. Dawson, “Robust tracking control of robot manipulators”, IEEE Press, Inc., New York, 1996.
[24] M. M. Fateh, “Robust control of flexible-joint robots using voltage control strategy”, Nonlinear Dynamics, Vol. 67, No. 2, pp.1525–1537, 2012.
[25] M. M. Fateh and S. Khorashadizadeh, “Robust control of electrically driven robots by adaptive fuzzy estimation of uncertainty,” Nonlinear Dynamics (ND), Vol. 63, No. 4, pp. 1465-1477, 2012.