The reliably stable neural network controllers' synthesis with the transient process parameters optimization
The subject of this paper is to develop a method for synthesizing stable neural network controllers with optimization of transient process parameters. The goal is to develop a method for synthesizing a neural network controller for control systems that guarantees the closed-loop system stability thr...
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| Format: | Article |
| Language: | English |
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National Aerospace University «Kharkiv Aviation Institute»
2024-11-01
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| Series: | Радіоелектронні і комп'ютерні системи |
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| Online Access: | http://nti.khai.edu/ojs/index.php/reks/article/view/2659 |
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| author | Serhii Vladov Anatoliy Sachenko Victoria Vysotska Yevhen Volkanin Dmytro Kukharenko Danylo Severynenko |
| author_facet | Serhii Vladov Anatoliy Sachenko Victoria Vysotska Yevhen Volkanin Dmytro Kukharenko Danylo Severynenko |
| author_sort | Serhii Vladov |
| collection | DOAJ |
| description | The subject of this paper is to develop a method for synthesizing stable neural network controllers with optimization of transient process parameters. The goal is to develop a method for synthesizing a neural network controller for control systems that guarantees the closed-loop system stability through automated selection of Lyapunov function with the involvement of an additional neural network trained on the data obtained in the solving process the integer linear programming problem. The tasks to be solved are: study the stability of a closed-loop control system with a neural network controller, train the neurocontroller and Lyapunov neural network function, create an optimization model for the loss function minimization, and conduct a computational experiment as an example of the neural network stabilizing controller synthesis. The methods used are: a neural network-based control object simulator training method described by an equations system taking into account the SmoothReLU activation function, a direct Lyapunov method to the closed-loop system stability guarantee, and a mixed integer programming method that allows minimizing losses and ensuring stability and minimum time regulation for solving the optimization problem. The following results were obtained: the neural network used made it possible to obtain results related to the transient process time reduction to 3.0 s and a 2.33-fold reduction in overregulation compared to the traditional controller (on the example of the TV3-117 turboshaft engine fuel consumption model). The results demonstrate the proposed approach's advantages, remarkably increasing the dynamic stability and parameter maintenance accuracy, and reducing fuel consumption fluctuations. Conclusions. This study is the first to develop a method for synthesizing a stabilizing neural network controller for helicopter turboshaft engines with guaranteed system stability based on Lyapunov theory. The proposed method's novelty lies in its linear approximation of the SmoothReLU activation function using binary variables, which allowed us to reduce the stability problem to an optimization problem using the mixed integer programming method. A system of constraints was developed that considers the control signal and stability conditions to minimize the system stabilization time. The results confirmed the proposed approach's effectiveness in increasing engine adaptability and energy efficiency in various operating modes. |
| format | Article |
| id | doaj-art-a7ac12a20ec44974b6faf65a27601ef7 |
| institution | Kabale University |
| issn | 1814-4225 2663-2012 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | National Aerospace University «Kharkiv Aviation Institute» |
| record_format | Article |
| series | Радіоелектронні і комп'ютерні системи |
| spelling | doaj-art-a7ac12a20ec44974b6faf65a27601ef72025-01-06T10:47:18ZengNational Aerospace University «Kharkiv Aviation Institute»Радіоелектронні і комп'ютерні системи1814-42252663-20122024-11-012024417819110.32620/reks.2024.4.152364The reliably stable neural network controllers' synthesis with the transient process parameters optimizationSerhii Vladov0Anatoliy Sachenko1Victoria Vysotska2Yevhen Volkanin3Dmytro Kukharenko4Danylo Severynenko5Kharkiv National Uni-versity of Internal Affairs, Kremenchuk Flight College, KharkivWest Ukrainian National University, Ternopil, Ukraine; Kazimierz Pulaski University of Radom, RadomLviv Polytechnic National University, LvivKharkiv National University of Internal Affairs, Kremenchuk Flight College, KharkivKremenchuk Mykhailo Ostrohradskyi National University, KremenchukLviv Polytechnic National University, LvivThe subject of this paper is to develop a method for synthesizing stable neural network controllers with optimization of transient process parameters. The goal is to develop a method for synthesizing a neural network controller for control systems that guarantees the closed-loop system stability through automated selection of Lyapunov function with the involvement of an additional neural network trained on the data obtained in the solving process the integer linear programming problem. The tasks to be solved are: study the stability of a closed-loop control system with a neural network controller, train the neurocontroller and Lyapunov neural network function, create an optimization model for the loss function minimization, and conduct a computational experiment as an example of the neural network stabilizing controller synthesis. The methods used are: a neural network-based control object simulator training method described by an equations system taking into account the SmoothReLU activation function, a direct Lyapunov method to the closed-loop system stability guarantee, and a mixed integer programming method that allows minimizing losses and ensuring stability and minimum time regulation for solving the optimization problem. The following results were obtained: the neural network used made it possible to obtain results related to the transient process time reduction to 3.0 s and a 2.33-fold reduction in overregulation compared to the traditional controller (on the example of the TV3-117 turboshaft engine fuel consumption model). The results demonstrate the proposed approach's advantages, remarkably increasing the dynamic stability and parameter maintenance accuracy, and reducing fuel consumption fluctuations. Conclusions. This study is the first to develop a method for synthesizing a stabilizing neural network controller for helicopter turboshaft engines with guaranteed system stability based on Lyapunov theory. The proposed method's novelty lies in its linear approximation of the SmoothReLU activation function using binary variables, which allowed us to reduce the stability problem to an optimization problem using the mixed integer programming method. A system of constraints was developed that considers the control signal and stability conditions to minimize the system stabilization time. The results confirmed the proposed approach's effectiveness in increasing engine adaptability and energy efficiency in various operating modes.http://nti.khai.edu/ojs/index.php/reks/article/view/2659optimizationcontrollerneural networklyapunov functionmixed integer programming |
| spellingShingle | Serhii Vladov Anatoliy Sachenko Victoria Vysotska Yevhen Volkanin Dmytro Kukharenko Danylo Severynenko The reliably stable neural network controllers' synthesis with the transient process parameters optimization Радіоелектронні і комп'ютерні системи optimization controller neural network lyapunov function mixed integer programming |
| title | The reliably stable neural network controllers' synthesis with the transient process parameters optimization |
| title_full | The reliably stable neural network controllers' synthesis with the transient process parameters optimization |
| title_fullStr | The reliably stable neural network controllers' synthesis with the transient process parameters optimization |
| title_full_unstemmed | The reliably stable neural network controllers' synthesis with the transient process parameters optimization |
| title_short | The reliably stable neural network controllers' synthesis with the transient process parameters optimization |
| title_sort | reliably stable neural network controllers synthesis with the transient process parameters optimization |
| topic | optimization controller neural network lyapunov function mixed integer programming |
| url | http://nti.khai.edu/ojs/index.php/reks/article/view/2659 |
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