A methodology for tuning and optimization of a quadruple tank control system using firefly algorithm in wavelet space

• Autorzy: Pires André Henrique Matias , Araújo Fábio Meneghetti Ugulino

Identyfikatory

DOI

10.15199/48.2023.05.15

Warianty tytułu

PL

Metodologia dostrajania i optymalizacji poczwórnego systemu sterowania zbiornikiem za pomocą algorytmu Firefly w przestrzeni falkowej

• Języki publikacji: EN

Abstrakty

EN

Using more efficient tuning techniques becomes imperative, due to the increasing competitiveness in the industry. With this propose, meta-heuristics, such as Firefly Algorithm (FA), can be used to obtain the parameters of the controller according to a cost function, which should encode how good a controller is, adequately expressing the desired specifications, so that the metaheuristic employed can find the desired controller that is able to reach the response wanted. The methods traditionally used for automatic tuning of controlers present difficulties in expressing the desired specifications, being able to mapping the desired search space and allowing that the algorithm finds the proper answer. These difficulties is more evident when more complex controllers are required, as for Multiple Input Multiple Output (MIMO) problems. Aiming to solve these difficulties, a methodology using wavelet transform to describe the behavior of a controller response and its use for obtain better performance of the optimization algorithm. A case study will be done using the quadruple tank system, showing the efficiency of the methodology proposed.

PL

Stosowanie bardziej wydajnych technik strojenia staje się koniecznoscią ze względu na rosnącą konkurencyjnosć w branży. Dzięki tej propozycji meta-heurystyki, takie jak Firefly Algorithm (FA), mogą byc użyte do uzyskania parametrów kontrolera zgodnie z funkcją kosztu, która powinna kodowac, jak dobry jest kontroler, adekwatnie wyrażajac poządane specyfikacje, tak aby zastosowana metaheurystyka moze znaleźć ządany kontroler, który jest w stanie osiągnąć ządaną odpowiedź. Metody tradycyjnie stosowane do automatycznego dostrajania sterowników stwarzają trudnosci w wyrażeniu pożądanych specyfikacji, mozliwości odwzorowania pożądanej przestrzeni wyszukiwania i umozliwienia algorytmowi znalezienia ˙ własciwej odpowiedzi. Trudności te są bardziej widoczne, gdy wymagane są bardziej złozone kontrolery, jak w przypadku problemów z wieloma wejściami i wieloma wyjsciami (MIMO). Mając na celu rozwiązanie tych trudnosci, opracowano metodologię wykorzystującą transformat falkową do opisu zachowania się odpowiedzi sterownika i jej zastosowanie w celu uzyskania lepszej wydajnosci algorytmu optymalizacji. Zostanie przeprowadzone ´ studium przypadku z wykorzystaniem systemu poczwórnego zbiornika, pokazujące skuteczność proponowanej metodologii.

Słowa kluczowe

EN

optimization; optimization algorithm; firefly algorithm; wavelet; MIMO; process control

PL

optymalizacja; algorytm optymalizacji; algorytm Firefly

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2023
• Tom: R. 99, nr 5
• Strony: 83--89
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Pires André Henrique Matias

• Federal University of Rio Grande do Norte

autor

Araújo Fábio Meneghetti Ugulino

• Federal University of Rio Grande do Norte

Bibliografia

• [1] S. Yousaf, A. Mughees, M. G. Khan, A. A. Amin and M. Adnan,"A Comparative Analysis of Various Controller Techniques forOptimal Control of Smart Nano-Grid Using GA and PSO Algorithms," in IEEE Access, vol. 8, pp. 205696-205711, 2020,doi: 10.1109/ACCESS.2020.3038021.
• [2] V. Veerasamy et al., "A Hankel Matrix Based Reduced Order Model for Stability Analysis of Hybrid Power System UsingPSO-GSA Optimized Cascade PI-PD Controller for AutomaticLoad Frequency Control," in IEEE Access, vol. 8, pp. 71422-71446, 2020, doi: 10.1109/ACCESS.2020.2987387.
• [3] Z. Qi, Q. Shi and H. Zhang, "Tuning of Digital PID Controllers Using Particle Swarm Optimization Algorithm for aCAN-Based DC Motor Subject to Stochastic Delays," in IEEETransactions on Industrial Electronics, vol. 67, no. 7, pp. 5637-5646, July 2020, doi: 10.1109/TIE.2019.2934030.
• [4] AGWA, Ahmed Mahmoud. Equilibrium optimization algorithmfor automatic generation control of interconnected power systems. Przeglad Elektrotechniczny 96.9 (2020): 143-148.
• [5] CHEUNPIROM, Montree, and Deacha PUANGDOWNREONG. Optimal Design of PI D A Controller for Wind TurbineSystems by Cuckoo Search. Przeglad Elektrotechniczny 98.7(2022): 110-115.
• [6] KUKHARCHUK, Vasyl V., et al. Discrete wavelet transformation in spectral analysis of vibration processes at hydropowerunits. Przeglad elektrotechniczny 93.5 (2017): 65-68.
• [7] S. Pant, R. K. Nema and S. Gupta, "Detecting Faultsin Power Transformers Using Wavelet Transform," 2021IEEE 2nd International Conference On Electrical Powerand Energy Systems (ICEPES), 2021, pp. 1-5, doi:10.1109/ICEPES52894.2021.9699483.
• [8] L. Ke, "Damage detection method of long-span bridgecable structure based on wavelet packet analysis," 202214th International Conference on Measuring Technology andMechatronics Automation (ICMTMA), 2022, pp. 326-330, doi:10.1109/ICMTMA54903.2022.00069.
• [9] M. M. Zarachoff, A. Sheikh-Akbari and D. Monekosso, "Non-Decimated Wavelet Based Multi-Band Ear Recognition UsingPrincipal Component Analysis," in IEEE Access, vol. 10, pp.3949-3961, 2022, doi: 10.1109/ACCESS.2021.3139684.
• [10] I. Sornsen, C. Suppitaksakul and R. Kitpaiboontawee, "Partial Discharge Signal Detection in Generators Using Wavelet Transforms," 2021 International Conference on Power, Energy and Innovations (ICPEI), 2021, pp. 195-198, doi:10.1109/ICPEI52436.2021.9690682.
• [11] Pires, André Henrique Matias. Análise multinível waveletcomo fitness na sintonia de controladores utilizando metaheurísticas. MS thesis. Brazil, 2017.
• [12] X.-S. Yang, “Firefly algorithms for multimodal optimization,” inInternational symposium on stochastic algorithms. Springer,2009, pp. 169–178.
• [13] E. Gagnon, A. Pomerleau, and A. Desbiens, “Simplified, idealor inverted decoupling?” ISA transactions, vol. 37, no. 4, pp.265–276, 1998.
• [14] F. Shinskey, Process-control systems: application, design, adjustment, ser. Chemical engineering series. McGraw-Hill, 1988. [Online]. Available:https://books.google.com.br/books?id=PE1lAg3ks0YC
• [15] J. Garrido, F. Vazquez, and F. Morilla, “An extended approachof inverted decoupling,” Journal of Process Control, vol. 21,no. 1, pp. 55–68, 2011.
• [16] S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 11, no. 7, pp.674–693, 1989.
• [17] M. N. Do and M. Vetterli, “Wavelet-based texture retrievalusing generalized gaussian density and kullback-leibler distance,” IEEE transactions on image processing, vol. 11, no. 2,pp. 146–158, 2002.
• [18] M. Vetterli and C. Herley, “Wavelets and filter banks: Theoryand design,” IEEE transactions on signal processing, vol. 40,no. ARTICLE, 1992
• [19] I. Darilmaz, “Wavelet based similarity measurement algorithmfor seafloor morphology,” Ph.D. dissertation, MassachusettsInstitute of Technology, 2006.
• [20] R. Kronland-Martinet, J. Morlet, and A. Grossmann, “Analysis of sound patterns through wavelet transforms,”Internationaljournal of pattern recognition and artificial intelligence, vol. 1,no. 02, pp. 273–302, 1987
• [21] L. Zhang, L. Zhang, X. Mou, and D. Zhang, “Fsim: A featuresimilarity index for image quality assessment,” IEEE transactions on Image Processing, vol. 20, no. 8, pp. 2378–2386,2011.
• [22] J. Lin, “Feature extraction of machine sound using wavelet andits application in fault diagnosis,” NDT e International, vol. 34,no. 1, pp. 25–30, 2001.
• [23] B. W. Bequette, Process control: modeling, design, and simulation. Prentice Hall Professional, 2003.