Application of Real-Coded Genetic Algorithm–PID Cascade Speed Controller to Marine Gas Turbine Engine Based on Sensitivity Function Analysis
Gas turbine engines at sea, characterized by nonlinear behavior and parameter variations due to dynamic marine environments, pose challenges for precise speed control. The focus of this study was a COGAG system with four LM-2500 gas turbines. A third-order model with time delay was derived at three...
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MDPI AG
2025-01-01
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| author | Yunhyung Lee Kitak Ryu Gunbaek So Jaesung Kwon Jongkap Ahn |
| author_facet | Yunhyung Lee Kitak Ryu Gunbaek So Jaesung Kwon Jongkap Ahn |
| author_sort | Yunhyung Lee |
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| description | Gas turbine engines at sea, characterized by nonlinear behavior and parameter variations due to dynamic marine environments, pose challenges for precise speed control. The focus of this study was a COGAG system with four LM-2500 gas turbines. A third-order model with time delay was derived at three operating points using commissioning data to capture the engines’ inherent characteristics. The cascade controller design employs a real-coded genetic algorithm–PID (R-PID) controller, optimizing PID parameters for each model. Simulations revealed that the R-PID controllers, optimized for robustness, show Nyquist path stability, maintaining the furthest distance from the critical point (−1, j0). The smallest sensitivity function <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>s</mi></mrow></semantics></math></inline-formula> (maximum sensitivity) values and minimal changes in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>s</mi></mrow></semantics></math></inline-formula> for uncertain plants confirm robustness against uncertainties. Comparing transient responses, the R-PID controller outperforms traditional methods like IMC and Sadeghi in total variation in control input, settling time, overshoot, and ITAE, despite a slightly slower rise time. However, controllers designed for specific operating points show decreased performance when applied beyond those points, with increased rise time, settling time, and overshoot, highlighting the need for operating-point-specific designs to ensure optimal performance. This research underscores the importance of tailored controller design for effective gas turbine engine management in marine applications. |
| format | Article |
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| spelling | doaj-art-2f01b1049aeb408091fbc9a80af4e48b2025-01-24T13:40:09ZengMDPI AGMathematics2227-73902025-01-0113231410.3390/math13020314Application of Real-Coded Genetic Algorithm–PID Cascade Speed Controller to Marine Gas Turbine Engine Based on Sensitivity Function AnalysisYunhyung Lee0Kitak Ryu1Gunbaek So2Jaesung Kwon3Jongkap Ahn4Ocean Technology Training Team, Korea Institute of Maritime and Fisheries Technology, Busan 49111, Republic of KoreaOcean Technology Training Team, Korea Institute of Maritime and Fisheries Technology, Busan 49111, Republic of KoreaDepartment of Maritime Industry Convergence, Mokpo National Maritime University, Mokpo-si 58628, Republic of KoreaDepartment of Mechanical System Engineering, Gyeongsang National University, Tongyeong-si 53064, Republic of KoreaTraining Ship Operation Center, Gyeongsang National University, Tongyeong-si 53064, Republic of KoreaGas turbine engines at sea, characterized by nonlinear behavior and parameter variations due to dynamic marine environments, pose challenges for precise speed control. The focus of this study was a COGAG system with four LM-2500 gas turbines. A third-order model with time delay was derived at three operating points using commissioning data to capture the engines’ inherent characteristics. The cascade controller design employs a real-coded genetic algorithm–PID (R-PID) controller, optimizing PID parameters for each model. Simulations revealed that the R-PID controllers, optimized for robustness, show Nyquist path stability, maintaining the furthest distance from the critical point (−1, j0). The smallest sensitivity function <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>s</mi></mrow></semantics></math></inline-formula> (maximum sensitivity) values and minimal changes in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>s</mi></mrow></semantics></math></inline-formula> for uncertain plants confirm robustness against uncertainties. Comparing transient responses, the R-PID controller outperforms traditional methods like IMC and Sadeghi in total variation in control input, settling time, overshoot, and ITAE, despite a slightly slower rise time. However, controllers designed for specific operating points show decreased performance when applied beyond those points, with increased rise time, settling time, and overshoot, highlighting the need for operating-point-specific designs to ensure optimal performance. This research underscores the importance of tailored controller design for effective gas turbine engine management in marine applications.https://www.mdpi.com/2227-7390/13/2/314gas turbine engineCOGAGgenetic algorithmmaximum sensitivity |
| spellingShingle | Yunhyung Lee Kitak Ryu Gunbaek So Jaesung Kwon Jongkap Ahn Application of Real-Coded Genetic Algorithm–PID Cascade Speed Controller to Marine Gas Turbine Engine Based on Sensitivity Function Analysis Mathematics gas turbine engine COGAG genetic algorithm maximum sensitivity |
| title | Application of Real-Coded Genetic Algorithm–PID Cascade Speed Controller to Marine Gas Turbine Engine Based on Sensitivity Function Analysis |
| title_full | Application of Real-Coded Genetic Algorithm–PID Cascade Speed Controller to Marine Gas Turbine Engine Based on Sensitivity Function Analysis |
| title_fullStr | Application of Real-Coded Genetic Algorithm–PID Cascade Speed Controller to Marine Gas Turbine Engine Based on Sensitivity Function Analysis |
| title_full_unstemmed | Application of Real-Coded Genetic Algorithm–PID Cascade Speed Controller to Marine Gas Turbine Engine Based on Sensitivity Function Analysis |
| title_short | Application of Real-Coded Genetic Algorithm–PID Cascade Speed Controller to Marine Gas Turbine Engine Based on Sensitivity Function Analysis |
| title_sort | application of real coded genetic algorithm pid cascade speed controller to marine gas turbine engine based on sensitivity function analysis |
| topic | gas turbine engine COGAG genetic algorithm maximum sensitivity |
| url | https://www.mdpi.com/2227-7390/13/2/314 |
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