کنترل مقاوم بازوی ربات با بهره گیری از روش بهینه‌سازی ازدحام ذرات
محورهای موضوعی : مهندسی الکترونیک
فضل اله رجایی
1
,
سید محمد علی ریاضی
2
,
سیامک آذرگشسب
3
1 - گروه مهندسی برق، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر، ایران
2 - گروه مهندسی برق، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر، ایران
3 - گروه مهندسی برق، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر، ایران
کلید واژه: بازوی ماهر رباتیک, عدم قطعیت, بهینه سازی ازدحام ذرات, کنترل مقاوم,
چکیده مقاله :
در این مقاله، روشی جدید برای کنترل مقاوم موقعیت بازوهای رباتیک بـا اسـتفاده از بهینه سازی ازدحام ذرات ارائه شده است. کل سیستم رباتیک شامل بازوی ربات و موتورها در مسئله کنترلی در نظر گرفته شده است. هدف اصلی این مقاله بدست آوردن برآورد بهینه از پارامتر‌های قانون کنترل به منظور رسیدن به حداقل خطای ردگیری است که از بهینه سازی ازدحام استفاده میشود همچنین، طراحی قانون کنترل بر مبنای مدل نامی انجام میشود و برای جبـران عـدم قطعیـت ناشی از عدم تطابق مدل نامی و مدل واقعی از سیستمهای هوشمند استفاده میشود. کنترل بهینه مقاوم با تجزیه و تحلیل همگرایی تأیید می شود. اثبات پایداری سیستم با اسـتفاده از روش مستقیم لیاپانوف انجام میشود و نتایج شبیه سازی، اثربخشی روشهای پیشنهادی اعمال شده بر روی یک ربات کروی را که توسط موتورهای dc مغناطیس دائمی رانده می شود، ارائه می دهد. با استفاده از نتایج شبیه سازی، مقادیر بهینه پارامترها در کنترل کننده های گشتاور به دلیل وجود یک دینامیک بزرگ بدون مدل به مقادیر واقعی خود همگرا نشده اند در حالی که آنها به مقادیر واقعی خود در کنترل ولتاژ همگرا شده اند زیرا فقط عدم قطعیت پارامتری دارد. همچنین، قانون کنترل گشتاور به فیدبک‌های بردار موقعیت، بردار سرعت و بردار شتاب نیاز دارد. این بازخوردها را نمی توان به راحتی به دست آورد. در مقابل، قانون کنترل ولتاژ به بازخوردهای بردار موقعیت، بردار سرعت، بردار جریان و مشتق زمانی آن نیاز دارد. این بازخوردها می توانند به سادگی در دسترس باشند.
In this paper, a new method for robust control is used. The whole robotic system, including the robot arm and motors in control, is considered. The main purpose of this article is to obtain the best results of the control law in order to achieve the minimum tracking error, which uses congestion optimization. Also, the designers of the control law are based on the nominal model . The real model uses intelligent systems. Control to resistance is evaluated by analysis and analysis.The stability of the system is demonstrated using Lyapunov's direct method, and the simulation results show the effectiveness of the proposed methods applied to a spherical robot driven by permanent magnet dc motors. Using the simulation results, the optimal values of the parameters in the torque controllers have not converged to their true values due to the large modelless dynamics, while they have converged to their true values in the voltage control because it has only parametric uncertainty. . Also, the torque control law requires position vector, velocity vector and acceleration vector feedback.These feedback can not be easily obtained. In contrast, the law of voltage control requires feedback from the position vector, velocity vector, current vector, and time derivative. These feedback can be easily accessed.
[1] M. W. Spong, S. Hutchinson and M. Vidyasagar, " Robot Modelling and Control ", Wiley, New York, 2006, ISBN: 978-1-119-52404-5.
[2] M.M. Fateh, “On the Voltage-Based Control of Robot Manipulators", Int. J. Control. Autom. Syst., Vol. 6, no. 5, pp. 702–712, 2008, doi:10.1007/s12555-017-0035-0.
[3] J. C. Fernandez, L. Penalver, V. Hernandez and J. Tornero, "High Performance Algorithm to Obtain Johansson Adaptive Control in Robot Manipulators", Commun. Nonlinear Sci. Numer. Simul. Vol. 9, no. 2, pp. 167–176, 2003, doi:10.1016/S1007-5704(03)00105-9.
[4] Y. Siyang, H. Jiang, X. Lian, and CH. Ye-Hwa, "An Optimal Fuzzy-Theoretic Setting of Adaptive Robust Control Design for a Lower Limb Exoskeleton Robot System", Mechanical Systems and Signal Processing, Vol. 141, pp. 415–434, 2020, doi:10.1016/j.ymssp.2020.106706.
[5] S. Yuxin, Z. Chunhong, and M. Paolo, "Robust Approximate Fixed-Time Tracking Control for Uncertain Robot Manipulators", Mechanical Systems and Signal Processing, Vol. 135, pp. 1-12, 2020, doi:10.1016/j.ymssp.2019.106379.
[6] Z. Qu and D.M. Dawson, " Robust Tracking Control of Robot Manipulators", IEEE Press, New York, 1996.
[7] M. W. Spong, " On the Robust Control of Robot Manipulators", IEEE Transactionson Automatic Control, Vol. 37, pp. 1782-1786, 1992, doi: 10.1109/9.173151.
[8] E. Camci, D. R. Kripalani, L. Ma, and E. Kayacan, "An Aerial Robot for Rice Farm Quality Inspection with Type-2 Fuzzy Neural Networks Tuned by Particle Swarm Optimization-Sliding Mode Control Hybrid Algorithm", Swarm and Evolutionary Computation, Vol. 41, pp. 1–8, 2018, doi:10.1016/j.swevo.2017.10.003.
[9] M.M. Fateh, “Robust voltage control of electrical manipulators in task-space", Int. J. Innov. Comput. Inf. Control, Vol. 6, no. 6, pp. 2691-2700, 2010, doi:10.1007/s10846-010-9430.
[10] M.M. Fateh, “Nonlinear Control of Electrical Flexible-Joint Robots", Nonlinear Dynamics, Vol. 67, no. 4, pp. 2549-2559, 2011, doi:10.1007/s11071-011-0167-3.
[11] M.M. Fateh, “Robust Control of Flexible-Joint Robots Using Voltage Control Strategy", Nonlinear Dynamics, Vol. 67, pp. 1525-1537, 2012, doi:10.1007/s11071-011-0086-3.
[12] M. W. Spong, "Modeling and Control of Elastic Joint Robots", J. Dyn. Syst. Meas. Control Vol. 109, pp. 310–319, 1987, doi:10.1115/1.3143860 .
[13] J.P. Hwang, and E. Kim, "Robust Tracking Control of an Electrically Driven Robot: Adaptive Fuzzy Logic Approach", IEEE Trans. Fuzzy Syst. Vol. 14, pp. 232–247, 2006, doi:10.1109/TFUZZ.2005.864082.
[14] T.J. Tarn, A.K. Bejczy, X. Yun and Z. Li, "Effect of Motor Dynamics on Nonlinear Feedback Robot Arm Control", IEEE Trans. Robot. Autom. Vol.7, no. 1, pp. 114–122, 1991, doi: 10.1109/70.68075.
[15] Y.C. Chang, H. M. Yen, and M.F. Wu, "An Intelligent Robust Tracking Control for Electrically-Driven Robot Systems", Int. J. Syst. Sci. Vol. 39, pp. 497–511, 2008, doi:10.1080/00207720701832747.
[16] M.M. Fateh, “Robust Fuzzy Control of Electrical Manipulators”, Journal of Intelligent and Robotic Systems, Vol. 60, pp. 415-434, 2010, doi:10.1007/s10846-010-9430-y.
[17] M.M. Fateh and S. Khorashadizadeh, “Robust Control of Electrically Driven Robots by Adaptive Fuzzy Estimation of Uncertainty", Nonlinear Dyn. Vol. 69, no. 3, pp. 1465–1477, 2012, doi:10.1007/s11071-012-0362-x.
[18] J. Kennedy, R. Eberhart, "Particle Swarm Optimization", In: Proc. IEEE Int. Conf. Neural Networks, Piscataway, NJ, vol. 4, pp. 1942–1948, 1995, doi:10.1109/ICNN.1995.488968.
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[20] M.M. Fateh and M. Moradi Zirkohi, "Adaptive Impedance Control of a Hydraulic Spension System Using Particle Swarm Optimization", Veh. Syst. Dyn. Vol. 49, no. 12, pp. 1951–1965, 2011, doi:10.1080/00423114.2011.564289.
[21] A. M., Abdelbar, S., Abdelshahid, and D. C., Wunsch, “Fuzzy PSO A Generalization of Particle Swarm Optimization”, Proceeding of International Joint Conference on Neural Networks, Montreal, Canada, pp 1086-1091, 2005, doi:10.1109/IJCNN.2005.1556004.
[22] R. Zarin and S. Azargoshasb, " Model-Free Discrete Time Control for Scara Robot Manipulators Using Descending G-radient Algorithm," Journal of Communication Engineering., vol. 11,no.41, pp. 59-76, 2021 (in persian).
[23] M. Zarei , S. Azargoshasb and najmeh cheraghi shirazi, " Design Discrete Robust Adaptive Fuzzy Control for Asymptotic Tracking of Articulated Robot Manipulator," Journal of Communication Engineering., vol. 10,no.40, pp. 37-50, 2021(in persian).
[24] C.C. Wong, H.Y. Wang, S.A. Li, "PSO-based motion fuzzy controller design for mobile robots", Int. J. Fuzzy Syst. Vol.10, no. 1, pp. 284–292, 2008.
[25] R. Xiaolin, Y. Yi, G. Long, CH. Jianbo, M. Tianxiang, Y. Jingya, and H. Qinqin, "Research on Robot Tracking of Books Returning to Bookshelf Based on Particle Swarm Optimization Fuzzy PID Control", IEEE Chinese Control and Decision Conference, 2020, doi:10.1109/CCDC49329.2020.9163983.
[26] S. D. Harshal, K. M. Prases, and K. Shubhasri, "A Robust Path Planning for Mobile Robot using Smart Particle Swarm Optimization", International Conference on Robotics and Smart Manufacturing, Vol. 133, pp. 290-297, 2018, doi:10.1016/j.procs.2018.07.036.
[27] M.M. Fateh and R. Babaghasabha, " Impedance Control of Robots Using Voltage Control Strategy", Nonlinear Dynamics, 19 May 2013, doi:10.1007/s11071-013-0964-y.
[28] H. V. H. Ayala and L. D. S. Coelho, " Tuning of PID controller based on a multiobjective genetic algorithm applied to a robotic manipulator", Expert Systems with plications, Vol. 39, pp. 8968-8974, 2012, doi:10.1016/j.eswa.2012.02.027.
_||_[1] M. W. Spong, S. Hutchinson and M. Vidyasagar, " Robot Modelling and Control ", Wiley, New York, 2006, ISBN: 978-1-119-52404-5.
[2] M.M. Fateh, “On the Voltage-Based Control of Robot Manipulators", Int. J. Control. Autom. Syst., Vol. 6, no. 5, pp. 702–712, 2008, doi:10.1007/s12555-017-0035-0.
[3] J. C. Fernandez, L. Penalver, V. Hernandez and J. Tornero, "High Performance Algorithm to Obtain Johansson Adaptive Control in Robot Manipulators", Commun. Nonlinear Sci. Numer. Simul. Vol. 9, no. 2, pp. 167–176, 2003, doi:10.1016/S1007-5704(03)00105-9.
[4] Y. Siyang, H. Jiang, X. Lian, and CH. Ye-Hwa, "An Optimal Fuzzy-Theoretic Setting of Adaptive Robust Control Design for a Lower Limb Exoskeleton Robot System", Mechanical Systems and Signal Processing, Vol. 141, pp. 415–434, 2020, doi:10.1016/j.ymssp.2020.106706.
[5] S. Yuxin, Z. Chunhong, and M. Paolo, "Robust Approximate Fixed-Time Tracking Control for Uncertain Robot Manipulators", Mechanical Systems and Signal Processing, Vol. 135, pp. 1-12, 2020, doi:10.1016/j.ymssp.2019.106379.
[6] Z. Qu and D.M. Dawson, " Robust Tracking Control of Robot Manipulators", IEEE Press, New York, 1996.
[7] M. W. Spong, " On the Robust Control of Robot Manipulators", IEEE Transactionson Automatic Control, Vol. 37, pp. 1782-1786, 1992, doi: 10.1109/9.173151.
[8] E. Camci, D. R. Kripalani, L. Ma, and E. Kayacan, "An Aerial Robot for Rice Farm Quality Inspection with Type-2 Fuzzy Neural Networks Tuned by Particle Swarm Optimization-Sliding Mode Control Hybrid Algorithm", Swarm and Evolutionary Computation, Vol. 41, pp. 1–8, 2018, doi:10.1016/j.swevo.2017.10.003.
[9] M.M. Fateh, “Robust voltage control of electrical manipulators in task-space", Int. J. Innov. Comput. Inf. Control, Vol. 6, no. 6, pp. 2691-2700, 2010, doi:10.1007/s10846-010-9430.
[10] M.M. Fateh, “Nonlinear Control of Electrical Flexible-Joint Robots", Nonlinear Dynamics, Vol. 67, no. 4, pp. 2549-2559, 2011, doi:10.1007/s11071-011-0167-3.
[11] M.M. Fateh, “Robust Control of Flexible-Joint Robots Using Voltage Control Strategy", Nonlinear Dynamics, Vol. 67, pp. 1525-1537, 2012, doi:10.1007/s11071-011-0086-3.
[12] M. W. Spong, "Modeling and Control of Elastic Joint Robots", J. Dyn. Syst. Meas. Control Vol. 109, pp. 310–319, 1987, doi:10.1115/1.3143860 .
[13] J.P. Hwang, and E. Kim, "Robust Tracking Control of an Electrically Driven Robot: Adaptive Fuzzy Logic Approach", IEEE Trans. Fuzzy Syst. Vol. 14, pp. 232–247, 2006, doi:10.1109/TFUZZ.2005.864082.
[14] T.J. Tarn, A.K. Bejczy, X. Yun and Z. Li, "Effect of Motor Dynamics on Nonlinear Feedback Robot Arm Control", IEEE Trans. Robot. Autom. Vol.7, no. 1, pp. 114–122, 1991, doi: 10.1109/70.68075.
[15] Y.C. Chang, H. M. Yen, and M.F. Wu, "An Intelligent Robust Tracking Control for Electrically-Driven Robot Systems", Int. J. Syst. Sci. Vol. 39, pp. 497–511, 2008, doi:10.1080/00207720701832747.
[16] M.M. Fateh, “Robust Fuzzy Control of Electrical Manipulators”, Journal of Intelligent and Robotic Systems, Vol. 60, pp. 415-434, 2010, doi:10.1007/s10846-010-9430-y.
[17] M.M. Fateh and S. Khorashadizadeh, “Robust Control of Electrically Driven Robots by Adaptive Fuzzy Estimation of Uncertainty", Nonlinear Dyn. Vol. 69, no. 3, pp. 1465–1477, 2012, doi:10.1007/s11071-012-0362-x.
[18] J. Kennedy, R. Eberhart, "Particle Swarm Optimization", In: Proc. IEEE Int. Conf. Neural Networks, Piscataway, NJ, vol. 4, pp. 1942–1948, 1995, doi:10.1109/ICNN.1995.488968.
[19] H. Modares, A. Alfi, M.M. Fateh, "Parameter Identification of Chaotic Dynamic Systems Through an Improved Particle Swarm Optimization", Expert Syst. Appl. Vol. 37, pp. 3714–3720, 2010, doi:10.1016/j.eswa.2009.11.054.
[20] M.M. Fateh and M. Moradi Zirkohi, "Adaptive Impedance Control of a Hydraulic Spension System Using Particle Swarm Optimization", Veh. Syst. Dyn. Vol. 49, no. 12, pp. 1951–1965, 2011, doi:10.1080/00423114.2011.564289.
[21] A. M., Abdelbar, S., Abdelshahid, and D. C., Wunsch, “Fuzzy PSO A Generalization of Particle Swarm Optimization”, Proceeding of International Joint Conference on Neural Networks, Montreal, Canada, pp 1086-1091, 2005, doi:10.1109/IJCNN.2005.1556004.
[22] R. Zarin and S. Azargoshasb, " Model-Free Discrete Time Control for Scara Robot Manipulators Using Descending G-radient Algorithm," Journal of Communication Engineering., vol. 11,no.41, pp. 59-76, 2021 (in persian).
[23] M. Zarei , S. Azargoshasb and najmeh cheraghi shirazi, " Design Discrete Robust Adaptive Fuzzy Control for Asymptotic Tracking of Articulated Robot Manipulator," Journal of Communication Engineering., vol. 10,no.40, pp. 37-50, 2021(in persian).
[24] C.C. Wong, H.Y. Wang, S.A. Li, "PSO-based motion fuzzy controller design for mobile robots", Int. J. Fuzzy Syst. Vol.10, no. 1, pp. 284–292, 2008.
[25] R. Xiaolin, Y. Yi, G. Long, CH. Jianbo, M. Tianxiang, Y. Jingya, and H. Qinqin, "Research on Robot Tracking of Books Returning to Bookshelf Based on Particle Swarm Optimization Fuzzy PID Control", IEEE Chinese Control and Decision Conference, 2020, doi:10.1109/CCDC49329.2020.9163983.
[26] S. D. Harshal, K. M. Prases, and K. Shubhasri, "A Robust Path Planning for Mobile Robot using Smart Particle Swarm Optimization", International Conference on Robotics and Smart Manufacturing, Vol. 133, pp. 290-297, 2018, doi:10.1016/j.procs.2018.07.036.
[27] M.M. Fateh and R. Babaghasabha, " Impedance Control of Robots Using Voltage Control Strategy", Nonlinear Dynamics, 19 May 2013, doi:10.1007/s11071-013-0964-y.
[28] H. V. H. Ayala and L. D. S. Coelho, " Tuning of PID controller based on a multiobjective genetic algorithm applied to a robotic manipulator", Expert Systems with plications, Vol. 39, pp. 8968-8974, 2012, doi:10.1016/j.eswa.2012.02.027.