مهندسی مکانیک مدرس

مهندسی مکانیک مدرس

طراحی و شبیه سازی کنترل کننده مدل پیشبین مبتنی بر سیستم فازی نوع 2 برای کنترل سیستم غیرخطی بویلر توربین

نویسندگان
1 دانشجوی کارشناسیارشد، مهندسی برق، دانشگاه صنعتی نوشیروانی بابل، بابل
2 دانشیار، مهندسی برق، دانشگاه صنعتی نوشیروانی بابل، بابل
چکیده
در این مقاله یک روش کنترل مدل پیشبین فازی جدید به منظور کنترل سیستم بویلر توربین به عنوان یک سیستم غیرخطی نامعین ارائه میگردد. در روش پیشنهادی جهت غلبه بر عواملی که به علت عدم دقت مدل سیستم میتوانند منجر به بروز خطای ماندگار یا بایاس در روش کنترل پیشبین گردند از سیستم فازی استفاده شده است. در این راستا با توجه به مدل تکه ای خطی سیستم و در نظر گرفتن محدودیتهای موجود در حالتهای سیستم و سیگنال کنترلی، یک کنترل کننده پیشبین با هدف بهینه سازی تابع هزینه مقید طراحی میشود. در طرح کنترلی ارائه شده از یک ناظر فازی نوع ۲ برای تعیین سیگنال ورودی مرجع با توجه به شرایط سیستم استفاده میشود. در این مطالعه نشان داده میشود که به کارگیری سیستمهای فازی نوع ۲ در روش کنترل پیشبین به جای سیستمهای فازی نوع ۱ منجر به نتایج رضایت بخشی میگردد. روش پیشنهادی به مدل غیرخطی سیستم بویلر توربین اعمال شود و نتایج حاصل از شبیه سازی، مؤثر بودن این روش در مقایسه با روشهای کنترل پیشبین فازی موجود را به ویژه در شرایطی نشان میدهد که وجود نامعینی در مدل وجود دارد.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Designing and simulating model predictive controller based on type-2 fuzzy system for a nonlinear boiler-turbine system

نویسندگان English

M. Azizi 1
B. Rezaie 2
2 Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
چکیده English

In this paper, a novel model predictive control method is presented for controlling a boiler-turbine system as an uncertain nonlinear system. In the proposed method, type-2 fuzzy system is used to cope with steady state error or bias appeared in the predictive control method due to the effects of model mismatch. For this purpose, using a piece-wise linear model of the system and considering the constraints in the system and the control signal, a predictive controller is designed to solve a constrained optimization problem. . In the presented control scheme, a type-2 fuzzy supervisor is used to adjust the reference input signal according to the system conditions. It has been shown that utilizing type-2 fuzzy system in the predictive control method, instead of type-1 fuzzy system, leads to satisfactory results. The proposed method is applied to the nonlinear model of the boiler-turbine system and the simulation results show the effectiveness of this method compared with the existing fuzzy predictive control methods, especially for the conditions in which the model uncertainty is present.

کلیدواژه‌ها English

Model Predictive Control
Fuzzy Control
Type-1 fuzzy system
Type-2 Fuzzy System
Boiler-turbine system
[1] W. Yang, G. Feng, T. Zhang, Quasi-min-max fuzzy model predictive control of direct methanol fuel cells, Fuzzy Sets and Systems, Vol. 248, pp. 39-60, 2013.
[2] J. Holaza, M. Klauco, J. Drgona, J. Oravec, M. Kvasnica, M. Fikar, MPC-based reference governor control of a continuous stirred-tank reactor, Computers and Chemical Engineering, Vol. 108, pp. 289-299, 2018.
[3] C. Bo, J. Li, L. Yang, H, Yi, J. Tang, X. Qiao, MPC of distillation column with side reactors for methylacetate, Chemical Engineering, Vol. 25, No. 12, pp. 1798-1804, 2017.
[4] V. Kirubakaran, T. K. Radhakrishnan, N. Sivakumaran, Fuzzy aggregation based multiple models explicit multi parametric MPC design for a quadruple tank process, IFAC-PapersOnLine, Vol. 49, No. 1, pp. 555-560, 2016.
[5] M. Sarailoo, Z. Rahmani, B. Rezaie, A novel model predictive control scheme based on bees algorithm in a class of nonlinear systems: Application to three tanks system, Neurocomputing, Vol. 15, pp. 294-304, 2015.
[6] M. Sarailoo, B. Rezaie, Z. Rahmani, Fuzzy predictive control of three-tank system based on a novel modeling framework of hybrid systems, Proceedings of the Institution of Mechanical Engineering Part .1: Journal of System, Control Engineering, Vol. 228, No. 6, pp. 369-384, 2014.
[7] M. Klauco, M. Kvasnica, Control of a boiler-turbine unit using MPC-based reference governors, Applied Merman Engineering, Vol. 110, pp. 1437-1447, 2017.
[8] X. Sun, C, Yuan, Y. Cai, S. Wang, L. Chen, Model predictive control of an air suspension with damping multi-mode switching damper based on hybrid model, Mechanical Systems and Signal Processing, Vol. 94, pp. 94-110, 2017.
[9] K. Worthmann, M. W. Mehrez, M. Zanon, G. K. I. Mann, R. G. Gosine, M. Diehl, Regulation of differential drive robots using continuous time MPC Transactions on Fuzzy Systems, Vol. 7, No. 6, pp. 643-658, 1999.
[10] M. Ghorbani, S. K. Hosseini Sani, Nonlinear model predictive control of Stewart platform 6 dot Modares Mechanical Engineering, Vol. 16, No. 1, pp. 41-50, 2016.
[11] M. Zamanian, A. Keymasi Khalaji, Trajectory tracking and stabilization of a tractor-trailer wheeled robot using model predictive control, Modares Mechanical Engineering, Vol. 17, No. 12, pp. 167-175, 2018. (in Persian
[12] S. Jalili, B. Rezaie and Z. Rahmani, A novel hybrid model predictive control design with application to a quadrotor helicopter, Optimal Control Applications and Methods. 2018. (doi: 10.1002/oca.2411)
[13] M. Zabihi, Z. Rahmani, B. Rezaie, Controlling parabolic trough collector using model predictive control, Energy, Vol. 18, No. 2, pp. 1-14, 2015. (in Persian
[14] G. Prasath, B. Recke, M. Chidambaram, J. B. Jorgensen, Soft Constrained based MPC for Robust Control of a Cement Grinding Circuit, IFAC Proceedings Volumes, Vol. 46, No. 32, pp. 475-480, 2013.
[15] L-D-L. Nguyen, I. Prodan, L. Lefevre, D. Genon-Catalot, Distributed Model Predictive Control of Irrigation System using Cooperative Controllers. IFAC-PapersOnLine, Vol. 50, No. 1, pp.6564-6569, 2017.
[16] L. Yuan, H. Zhao, H. Chen, B. Ren, Nonlinear MPC-based slip control for electric vehicles with vehicle safety constraints, Mechatronics, Vol. 38, pp. 1-15, 2016.
[17] S. Najjaran, Z. Rahmani, M. HassanZadeh, Energy management in order to reduce fuel consumption for parallel Plug-in Hybrid Electric vehicles based on fuzzy predictive control, Modares Mechanical Engineering, Vol. 17, No. 5, pp. 353-362, 2017. (in Persian
[18] S. Najafi, B. Moaveni, Modeling and real-time traffic regulation in metro loop lines using nonlinear model predictive control, Control, Vol. 9, No. 2, pp. 1-12, 2015. (in Persian
[19] M. El-Bardini, A. M. El-Nagar, Interval type-2 fuzzy PID controller for uncertain nolinear inverted pendulum system, ISA Transactions, Vol. 53, No. 3, pp. 732-743, 2014.
[20] A. Sharifian, M. Jabbari Ghadi, S. Ghavidel, L. Li , J. Zhang, A new method based on type-2 fuzzy neural network for accurate wind power forecasting under uncertain data, Renewable Energy, Vol. 120, pp. 220-230, 2018.
[21] F. Zhang, X. Wu, J. Shen, Extended state observer based fuzzy model predictive control for ultra-supercritical boiler-turbine unit, Applied Thermal Engineering, Vol. 118, pp. 90-100, 2017.
[22] X. Wu, J. Shen, Y. Li, K. Y. Lee, Hierarchical optimization of boiler-turbine unit using fuzzy stable model predictive control, Control Engineering Practice, Vol. 30, pp. 112-123, 2014.
[23] M. Sarailoo, Z. Rabmani, B. Rezaie, Fuzzy predictive control of boiler-turbine system based on hybrid model system, Industrial and Engineering Chemical Research, Vol. 53, No. 6, pp. 2362-2381, 2014.
[24] P. Sarhadi, B. Rezaie, Z. Rahmani, Adaptive predictive control based on adaptive neuro-fuzzy inference system for a class of nonlinear industrial processes, the Taiwan Institute of Chemical Engineers, Vol. 61, pp. 132-137, 2016.
[25] K. Sabahi, S. Ghaemi, J. Liu, M. A. Badamchizade, Indirect predictive type-2 fuzzy neural network controller for a class of nonlinear input-delay systems, ISA Transactions, Vol. 71, No. 2, pp. 185-195, 2017.
[26] K. J. Aston', K. Eklund, A simplified non-linear model of a drum-boiler-turbine unit, Control, Vol. 16, No. 1, pp. 145-169, 1972.
[27] A. J. Morton, P. H. Price, The controllability of steam output, pressure and water level in drum boilers, Proceedings of Industrial and Marine Steam Plant: Convention; Institution of Mechanical Engineers, Great Britain, pp. 75-84,1977.
[28] R. D. Bell, K. J. Astram, Dynamic models for boi-ler-turbine alternator units: Data logs and parameter estimation for a 160 MW unit, Report TFRT-3J92, Lund Institute of Technology, Sweden, 1987.
[29] U. C Moon, K.Y. Lee, A boiler-turbine system control using a fuzzy auto-regressive moving average (FARMA) model, IEEE Transactions on Energy Conversion, Vol. 18, No. 1, pp. 142-148, 2003.
[30] J. Wu, J. Shen, M. Krug, S.K. Nguang, Y. Li, GA-based nonlinear predictive switching control for a boilerturbine system, Control Theory and Applications, Vol. 10, No. 1, pp. 100-106, 2012
[31] R. Dimeo, K. Y. Lee, Boiler-turbine control system design using a genetic algorithm, IEEE Transactions on Energy Conversion, Vol. 10, No. 4, pp. 752-759, 1995.
[32] P. Chen, J. S. Shamma, Gain-scheduled C 1-optimal control for boiler-turbine dynamics with actuator saturation, Process Control, Vol. 14, No. 3, pp. 263-277, 2004.
[33] W. Tan, H. J. Marquez, T. Chen, J. Liu, Analysis and control of a nonlinear boiler-turbine unit, Process Control, VoL 15, No. 8, pp. 883-891, 2005.
[34] J. Wu, M. Kurg, S.K. Nguang, J. Shen, Y.G. Li, H. fuzzy tracking control for boiler-turbine systems, Proceedings of IEEE International Conference on Control and Automation, Christchurch, New Zealand, pp.1980-1985, 2009.
[35] J. A. Rossiter, Y. Ding, Interpolation methods in model predictive control: an overview, Control, Vol. 83, No. 2, pp. 297-312, 2010.
[36] J. M. Mendel, Type-2 Fuzzy sets and systems: an overview, IEEE Computational Intelligence Magazine, Vol. 2, No. 1, pp. 20-29, 2007.
[37] J. M. Mendel. Type-2 Fuzzy sets: some questions and answers, IEEE Neural Networks Society Newsletter, Vol. 1, pp. 10-13, 2003.
[38] N. N. Kamilc, J. M. Mendel, L. Qilian, Type2 fuzzy logic systems, IEEE
[39] J. M. Mendel. Advances in type-2 fuzzy sets and systems, Information Sciences, Vol. 177, No. 1, pp. 84-110, 2007.