Designing LQG controller based on neural network estimator for boiler system

الموضوعات : فصلنامه شبیه سازی و تحلیل تکنولوژی های نوین در مهندسی مکانیک

Hamed Khodadadi ¹ (Department of Electrical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran)
HAMID Ghadiri ² (Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran)

تاريخ الإرسال : 27 السبت , رجب, 1444 تاريخ التأكيد : 10 الأحد , شوال, 1444 تاريخ الإصدار : 12 الخميس , ذو القعدة, 1444

الکلمات المفتاحية: Neural network, Kalman Filter, Boiler system, LQG controller,

ملخص المقالة :

Due to demands for the economical operations of power plants, performance control of a boiler-turbine unit has great importance. Besides, the multi-input multi-output (MIMO) structure of boiler systems has some challenges that make their control some problems. In this research, the control of boiler systems is performed based on the neural network algorithm. In the boiler system, the liquid (usually water) reaches its desired temperature and its output steam is employed for making electricity, locomotive movement, environmental heating, health domain and etc. Since the water level in the tank has a great effect on the stability of the boiler, controlling dram water level significantly affects the system's performance. In this paper, controlling the water level of the boiler system is applied utilizing LQG and a neural network algorithm. For the system variables estimation, neural networks are employed instead of system conditions, due to their high ability in system identification in various conditions. The simulation results of the proposed method are compared with the Kalman-filter-based LQG controller.

المصادر:

• Wang X, Sun Y, Ding D (2022) Adaptive dynamic programming for networked control systems under communication constraints: a survey of trends and techniques. International Journal of Network Dynamics Intelligence:85-98


• Eslami, M., Mokhtarian, A., Pirmoradian, M., Seifzadeh, S. A., & Rafiaei, S. M. (2020). Designing and creating a virtual reality environment and a wearable glove with control and evaluation capability to rehabilitate patients. Journal of Health and Biomedical Informatics, 7(2), 161-170.


• Zeini, M., & Pirmoradian, M. (2021). Design and construction of a unicycle robot controlled by its center of gravity. Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 13(2), 59-73.


• Zeinaddini, M., Pirmoradian, M., & Azimifar, F. (2018). Optimizing the Torque of Knee Movements of a Rehabilitation Robot. Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 11(1), 5-14.


• Rahmanipour P, Ghadiri H (2020) Stability analysis for a class of fractional-order nonlinear systems with time-varying delays. Soft Computing 24 (22):17445-17453


• Mohammadhosseini M, Ghadiri H (2019) A combination of genetic algorithm and particle swarm optimization for power systems planning subject to energy storage. Journal of Computer & Robotics 12 (1):65-76


• Coutinho JP, Santos LO, Reis MS (2023) Bayesian Optimization for automatic tuning of digital multi-loop PID controllers. Computers and Chemical Engineering 173:108211


• Khodadadi H, Ghadiri H (2019) Fuzzy logic self-tuning PID controller design for ball mill grinding circuits using an improved disturbance observer. Mining, Metallurgy & Exploration 36 (6):1075-1090


• Yeh T-J, Huang C-C, Toh C-S (2023) Closed-loop identification of multi-axis active magnetic bearing systems based on decentralized and decoupling control. Mechatronics 90:102933


• Wang C, Qiao Y, Liu M, Zhao Y, Yan J (2020) Enhancing peak shaving capability by optimizing reheat-steam temperature control of a double-reheat boiler. Applied Energy 260:114341


• Shen B, Han Y, Price L, Lu H, Liu M (2017) Techno-economic evaluation of strategies for addressing energy and environmental challenges of industrial boilers in China. Energy 118:526-533


• Jha R, Jha NN, Lele MM (2022) Stochastic gradient descent algorithm for the predictive modelling of grate combustion and boiler dynamics. ISA transactions


• Polton C, Boje E (2020) Quantitative feedback theory design of valve position control for co-ordinated superheater control of main steam temperatures of power plant boilers. IFAC-PapersOnLine 53 (2):13070-13075


• Jan AZ, Kedzia K, Abbass MJ (2023) Fractional-Order PID Controller (FOPID)-Based Iterative Learning Control for a Nonlinear Boiler System. Energies 16 (3):1045


• Tavoosi J (2021) Intelligent Model Predictive Control for Boiler Temperature. Automatic Control and Computer Sciences 55:16-25


• Siddiqui I, Ingole D, Sonawane D, Agashe S (2020) Offset-free Nonlinear Model Predictive Control of A Drum-boiler Pilot Plant. IFAC-PapersOnLine 53 (1):506-511


• Tavoosi J, Azarakhsh Y, Mohammadzadeh A, Mobayen S, Asad JH, Safdar R (2021) Multivariable model reference adaptive control of an industrial power boiler using recurrent RBFN. Complexity 2021:1-12


• Fang F, Wei L (2011) Backstepping-based nonlinear adaptive control for coal-fired utility boiler–turbine units. Applied Energy 88 (3):814-824


• Bucolo M, Buscarino A, Fortuna L, Frasca M (2022) LQG control of linear lossless positive-real systems: the continuous-time and discrete-time cases. International Journal of Dynamics and Control Engineering Practice:1-9


• Muravyova E, Uspenskaya NJ (2018) Development of a neural network for a boiler unit generating water vapour control. Optical Memory and Neural Networks 27:297-307


• Yao Z, Romero C, Baltrusaitis J (2023) Combustion optimization of a coal-fired power plant boiler using artificial intelligence neural networks. Fuel 344:128145


• Jiang J-Y, Wu B, Zhou T (2022) Fractional-Order Active Disturbance Rejection Controller Design for Large Radio Telescope Antenna. International Journal of Antennas and Propagation 2022.


• Arab Zade, M., & Khodadadi, H. (2019). Fuzzy controller design for breast cancer treatment based on fractal dimension using breast thermograms. IET systems biology, 13(1), 1-7.


• Khodadadi, H., Sedigh, A. K., Ataei, M., Motlagh, M. R. J., & Hekmatnia, A. (2017). Nonlinear analysis of the contour boundary irregularity of skin lesion using Lyapunov exponent and KS entropy. Journal of Medical and Biological Engineering, 37, 409-419.


• Khodadadi, H., Khaki-Sedigh, A., Ataei, M., & Jahed-Motlagh, M. R. (2018). Applying a modified version of Lyapunov exponent for cancer diagnosis in biomedical images: the case of breast mammograms. Multidimensional Systems and Signal Processing, 29(1), 19-33.


• Saibi A, Belaidi H, Boushaki R, Eddine RZ, Hafid A (2022) Enhanced backstepping control for disturbances rejection in quadrotors. Bulletin of Electrical Engineering and Informatics 11 (6):3201-3216


• Abbasi A, Moshayedi AJ (2018) Trajectory Tracking of Two-Wheeled Mobile Robots, Using LQR Optimal Control Method, Based On Computational Model of KHEPERA IV. Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering 10 (3):41-50


• Oualid L, Hacene H Multivariable control of a drum-type boiler system through LQG fuzzy emulation. In: 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT), 2015. IEEE, pp 1-6


• Ghadiri H (2020) Real time stability assessment of power system using ANN without requiring expert experience. Majlesi Journal of Electrical Engineering 14 (2):43-49