کنترل تطبیقی سیستم های غیرخطی تاخیردار با در نظر گرفتن محدودیت های خروجی
محورهای موضوعی : انرژی های تجدیدپذیر
فاطمه محمدزمانی
1
(دانشکده مهندسی برق، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران)
مهناز هاشمی
2
(دانشکده مهندسی برق، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران)
غضنفر شاهقلیان
3
(دانشکده مهندسی برق، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران)
کلید واژه: کنترل تطبیقی, قیود خروجی, سیستمهای غیرخطی تاخیردار, پارامترهای نامعین, عیوب عملگر,
چکیده مقاله :
کنترل سیستمها در فرآیندهای صنعتی در معرض مشکلاتی مثل وجود محدودیت بر سیگنالهای سیستم، نامعینی پارامترها، تأخیر زمانی و عیب عملگرها هستند. طراحی کنترل کنندهای که بتواند درضمن ارضای قیود، سیستم را کنترل، با این اثرات مقابله و آنها را جبران کند، توجه زیادی را به خود جلب کرده است. از سوی دیگر، مسئله تأخیر زمانی تا حدی جدی و تاثیرگذار است، که قادر اسهت سیستم را ناپایدار کرده و فرآیند را دچار اختلال کند. بسیاری از ادوات موجود در سیستمها همچون حسگرها و عملگرها ممکن است دچار عیب شوند که در این میان، عیوب عملگر از اهمیت ویژهای برخوردار است. نکته ی حائز اهمیت این است که، هر کدام از موارد فوق و یا حتی پارامترهای سیستم ممکن است نامعین باشند. شناسایی، تخمین و رفع اثرات مخرب مشکلات ذکر شده بر عهده کنترل کننده سیستم میباشد.روش کنترلی پیشنهادی برای سیستمهای غیرخطی در حضور نامعینی پارامتری، تاخیر و عیوب نامعین در عملگرها است و هیچ نیازی به کران پارامترها، تاخیرها و عیوب عملگر ندارد. این روش تطبیقی قادر است کرانداری کلی تمام سیگنالهای سیستم حلقه بسته و همگرایی خطای ردیابی به یک همسایگی کوچک حول مبدا را تضمین کند. در انتها نتایج شبیه سازی کارایی روش کنترلی پیشنهادی را نشان میدهند.
Controlling systems in industrial processes are subject to problems such as the limitation of system signals, the uncertainties of parameters, the time delay and the failure of actuators. The design of the controller, which can satisfy the constraints, counteract and omit these effects, has attracted much attention.On the other hand, the issue of time delay is so serious and effective, which can make the system unstable and disrupt the process. Many of the devices in the systems, such as sensors and actuators, may be defective. The important thing is that any of the above or even system parameters may be uncertain. Identifying, estimating and fixing the destructive effects of the problems mentioned by the controller of the system.The proposed method of control for nonlinear systems in the presence of an uncertain parameters, delay and faults in actuators. There is no need to limit the parameters, delays, and fault of the actuators. This comparative method is capable of guaranteeing the overall boundary of all closed-loop system signals and the convergence of tracking errors to a small neighborhood around the origin. At the end, the simulation results show the effectiveness of the proposed control method
[1] E. Aghadavoodi, G. Shahgholian, "A new practical feed-forward cascade analyze for close loop identification of combustion control loop system through RANFIS and NARX", Applied Thermal Engineering, Vol. 133, pp. 381-395, March 2018 (doi: 10.1016/j.applthermaleng.2018.01.075).
[2] S. E. Razavi, P. Poursoltani, N. Pariz, "Optimal observer path planning in tracking two targets using side angle measurements", Journal of Intelligent Procedures in Electrical Technology, Vol. 10, No. 38, pp. 33-42, Summer 2019.
[3] B. Ahmadzade, G. Shahgholian, F. Mogharrab-Tehrani, M. Mahdavian, "Model predictive control to improve power system oscillations of SMIB with fuzzy logic controller", Proceeding of the IEEE/ICEMS, pp. 1-5, Beijing, China, Aug. 2011 (doi: 10.1109/ICEMS.2011.6073337).
[4] M. Hashemi, G. Shahgholian, "Distributed robust adaptive control of high order nonlinear multi agent systems", ISA Trans., Vol. 74, pp. 14-27, March 2018 (doi:10.1016/j.isatra.2018.01.023).
[5] A. Casavola, E. Mosca, D. Angeli, "Robust command governors for constrained linear systems", IEEE Trans. on Automatic Control, Vol. 45, No. 11, pp. 2071-2077, Nov. 2000 (doi:10.1109/9.887628).
[6] M. A. Mohammadkhani, F. Bayat, A. A. Jalali, "Design of explicit model predictive control for constrained linear systems with disturbances," International Journal of Control, Automation and Systems, Vol. 12, No. 2, pp. 294-301, April 2014 (doi:10.1007/s12555-013-0058-0).
[7] E. G. Gilbert, K. T. Tan, "Linear systems with state and control constraints: The theory and application of maximal output admissible sets", IEEE Trans. on Automatic Control, Vol. 36, No. 9, pp. 1008-1020, Sep. 1991 (doi:10.1109/9.83532).
[8] A. Bemporad, M. Morari, V. Dua, E. N. Pistikopoulos, "The explicit linear quadratic regulator for constrained systems", Automatica, Vol. 38, No. 1, pp. 3-20, Jan. 2002 (doi:10.1016/S0005-1098(01)00174-1).
[9] J. A. Primbs, C. H. Sung, "Stochastic receding horizon control of constrained linear systems with state and control multiplicative noise", IEEE Trans. on Automatic Control, Vol. 54, No. 2, pp. 221-230, Feb. 2009 (doi:10.1109/TAC.2008.2010886).
[10] D. Q. Mayne, J. B. Rawlings, C. V. Rao, P. O. Scokaert, "Constrained model predictive control: Stability and optimality" Automatica, Vol. 36, No. 6, pp. 789-814, June 2000 (doi:10.1016/S0005-1098(99)00214-9).
[11] K. D. Do, "Control of nonlinear systems with output tracking error constraints and its application to magnetic bearings," International Journal of Control, Vol. 83, pp. 1199-1216, May 2010 (doi: 10.1080/00207171003664828).
[12] W. Meng, Q. Yang, J. Si, Y. Sun, "Consensus control of nonlinear multiagent systems with time-varying state constraints," IEEE Trans. on cybernetics, Vol. 47, No. 8, pp. 2110-2120, Aug. 2017 (doi:10.1109/TCYB. 2016.2629268).
[13] D. Liu, X. Yang, D. Wang, Q. Wei, "Reinforcement-learning-based robust controller design for continuous-time uncertain nonlinear systems subject to input constraints", IEEE Trans. on Cybernetics, Vol. 45, No. 7, pp. 1372-1385, July 2015 (doi:10.1109/TCYB.2015.2417170).
[14] L. Zhang, C. Hua, H. Yu, X. Guan, "Distributed adaptive fuzzy containment control of stochastic pure-feedback nonlinear multiagent systems with local quantized controller and tracking constraint", IEEE Trans. on Systems, Man, and Cybernetics: Systems, Vol. 40, No. 4, April 2019 (doi:10.1109/TSMC.2017.2701344 ).
[15] W. He, H. Huang, S. S. Ge, "Adaptive neural network control of a robotic manipulator with time-varying output constraints," IEEE Trans. on Cybernetics, Vol. 47, No. 10, pp. 3136-3147, Oct. 2017 (doi:10.1109/TCYB. 2017.2711961).
[16] Y.-J. Liu, S. Lu, S. Tong, "Neural network controller design for an uncertain robot with time-varying output constraint," IEEE Trans. on Systems, Man, and Cybernetics: Systems, Vol. 47, No. 8, Aug. 2017 (doi:10.1109/ TSMC.2016.2606159).
[17] K. P. Tee, S. S. Ge, E. H. Tay, "Barrier Lyapunov functions for the control of output-constrained nonlinear systems", Automatica, Vol. 45, No. 4, pp. 918-927, April 2009 (doi:10.1016/j.automatica.2008.11.017).
[18] K. P. Tee, B. Ren, S. S. Ge, "Control of nonlinear systems with time-varying output constraints", Automatica, Vol. 47, No. 11, pp. 2511-2516, Nov. 2011 (doi:10.1016/j.automatica.2011.08.044).
[19] G. Shahgholian, A. Movahedi, "Modeling and controller design using ANFIS method for non-linear liquid level system", International Journal of Information and Electronics Engineering, Vol. 1, No. 3, pp. 271-275, Nov. 2011 (doi:10.7763/IJIEE.2011.V1.43).
[20] E. Fridman, U. Shaked, "A descriptor system approach to H/sub/spl infin//control of linear time-delay systems", IEEE Trans. on Automatic Control, Vol. 47, No. 2, pp. 253-270, Feb. 2002 (doi:10.1109/9.983353).
[21] L. Yu, J. Chu, "An LMI approach to guaranteed cost control of linear uncertain time-delay systems", Automatica, Vol. 35, No. 6, pp. 1155-1159, June 1999 (doi:10.1016/S0005-1098(99)00007-2).
[22] D.-H. Zhai, Y. Xia, "Adaptive control for teleoperation system with varying time delays and input saturation constraints", IEEE Trans. on Industrial Electronics, Vol. 63, No. 11, pp. 6921-6929, 2016 (doi:10.1109/TIE. 2016.2583199).
[23] Y.-Y. Cao, P. M. Frank, "Analysis and synthesis of nonlinear time-delay systems via fuzzy control approach", IEEE Trans. on Fuzzy Systems, Vol. 8, No. 2, pp. 200-211, April 2000 (doi:10.1109/91.842153).
[24] Li, Da-Peng, et al. "Approximation-based adaptive neural tracking control of nonlinear MIMO unknown time-varying delay systems with full state constraints", IEEE Trans. on Cybernetics, Vol. 47, No. 10, pp. 3100-3109, Oct. 2017 (doi:10.1109/TCYB.2017.2707178).
[25] K. Gu, "An integral inequality in the stability problem of time-delay systems", Proceeding of the IEEE/CDC, pp. 2805-2810, Sydney, NSW, Australia, Dec. 2000 (doi: 10.1109/CDC.2000.914233).
[26] L. Xie, E. Fridman, U. Shaked, "Robust H/sub/spl infin//control of distributed delay systems with application to combustion control", IEEE Trans. on Automatic Control, Vol. 46, No. 12, pp. 1930-1935, Dec. 2001 (doi:10.1109/9 .975483).
[27] S.-I. Niculescu, "H/sub/spl infin//memoryless control with an/spl alpha/-stability constraint for time-delay systems: an LMI approach," IEEE Trans. on Automatic Control, vol. 43, No. 5, pp. 739-743, May 1998 (doi:10.1109/ 9.668850).
[28] M. Ataei, H. Ghotb, G. Shahgholian, A. Kiyoumarsi. "Modeling of the electric arc furnaces using chaos theory and control of power quality parameters", Journal of Control. Vol. 7, No. 2, pp. 33-42, 2013.
[29] M. Taslimi, A. Chatraei, M. Hosseini, "A robust neuro-adaptive control of three link scara robot with mass uncertainty", Journal of Intelligent Procedures in Electrical Technology, Vol. 4, No. 15, pp. 11-18, Sep. 2013.
[30] R. Aarthi, R. A. Natarajan, "An integrated fault detection and diagnosis using kaman filter and eigen structure assignment-application to three tank system", Applied Mechanics and Materials, Vol. 704, pp. 252-256, 2015 (doi: 10.4028/www.scientific.net/AMM.704.252).
[31] J. Chen, W. Zhang, Y.-Y. Cao, "Robust reliable feedback controller design against actuator faults for linear parameter-varying systems in finite-frequency domain", IET Control Theory and Applications, Vol. 9, No. 10, pp. 1595-1607, June 2015 (doi:10.1049/iet-cta.2014.1308).
[32] J. Chen, W. Zhang, Y.-Y. Cao, H. Chu, "Observer-based consensus control against actuator faults for linear parameter-varying multiagent systems", IEEE Trans. on Systems, Man, and Cybernetics: Systems, Vol. 47, No. 7, pp. 1336-1347, July 2017 (doi:10.1109/TSMC.2016.2587300).
[33] C. Deng, G.-H. Yang, "Cooperative adaptive output regulation for linear multi-agent systems with actuator faults", IET Control Theory and Applications, Vol. 11, No. 14, pp. 2396-2402, Sep. 2017 (doi:10.1049/iet-cta.2016.1571).
[34] M. Hashemi, "Adaptive control of nonlinear systems in the presence of actuator failures", Journal of Intelligent Procedures in Electrical Technology, Vol. 8, No. 32, pp. 51-58, March 2018.
[35] K. Shojaei, A. Chatraei, S. Nakhkoob, "Fuzzy adaptive control for trajectory tracking of autonomous underwater vehicle", Journal of Intelligent Procedures in Electrical Technology, Vol. 4, No. 16, pp. 51-58, Dec. 2014.
[36] M. Blanke, M. Kinnaert, J. Lunze, M. Staroswiecki, J. Schröder, Diagnosis and fault-tolerant control vol. 691: Springer, 2006.
[37] I. Sadeghzadeh, A. Mehta, Y. Zhang, C.-A. Rabbath, "Fault-tolerant trajectory tracking control of a quadrotor helicopter using gain-scheduled PID and model reference adaptive control", Proceeding of the ACPHMS, Aug. 2011.
[38] M. Hashemi, "Adaptive neural dynamic surface control of MIMO nonlinear time delay systems with time‐varying actuator faults", International Journal of Adaptive Control and Signal Processing, Vol. 31, No. 2, pp. 275-296, 2017 (doi:10.1002/acs.2715).
[39] C. Wen, Y. Zhang, Y. C. Soh, "Robustness of an adaptive backstepping controller without modification", Systems and Control Letters, Vol. 36, No. 2, pp. 87-100, Feb. 1999 (doi:10.1016/S0167-6911(98)00081-4).
[40] M. Hashemi, J. Askari, J. Ghaisari, "Adaptive actuator fault compensation for a class of MIMO nonlinear time delay systems," Nonlinear Dynamics, Vol. 79, pp. 865-883, 2015 (doi: 10.1007/s11071-014-1708-3).
[41] M. Hashemi, J. Askari, J. Ghaisari, "Adaptive control of uncertain nonlinear time delay systems in the presence of actuator faults and applications to chemical reactor systems", European Journal of Control, Vol. 29, pp. 62-73, May 2016 (doi:10.1016/j.ejcon.2016.03.002).
[42] S. Yin, H. Yang, H. Gao, J. Qiu, O. Kaynak, "An adaptive NN-based approach for fault-tolerant control of nonlinear time-varying delay systems with unmodeled dynamics", IEEE Trans. on Neural Networks and Learning Systems, Vol. 28, No. 8, pp. 1902-1913, Aug. 2017 (doi:10.1109/TNNLS.2016.2558195).
[43] H. Li, Y. Gao, L. Wu, H.-K. Lam, "Fault detection for TS fuzzy time-delay systems: delta operator and input-output methods", IEEE Trans. on Cybernetics, Vol. 45, No. 2, pp. 229-241, Feb. 2015 (doi:10.1109/TCYB.2014. 2323994).
[44] S.-J. Huang, G.-H. Yang, "Fault tolerant controller design for t–s fuzzy systems with time-varying delay and actuator faults: a k-step fault-estimation approach", IEEE Trans. on Fuzzy Systems, Vol. 22, No. 6, pp. 1526-1540, Dec. 2014 (doi:10.1109/TFUZZ.2014.2298053).
[45] E. Hosseini, E. Aghadavoodi, G. Shahgholian, H. Mahdavi-Nasab, "Intelligent pitch angle control based on gain-scheduled recurrent ANFIS", Journal of Renewable Energy and Environment, Vol. 6, No. 1, pp. 36-45, 2019.
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