An article recently published in the journal Energies introduced a novel method for controlling the energy characteristics of helicopter turboshaft engines (TEs). This approach utilizes neural networks to regulate free turbine rotor speed and fuel consumption, thereby improving efficiency and reducing fuel consumption.
Study: Method for Helicopter Turboshaft Engines Controlling Energy Characteristics Through Regulating Free Turbine Rotor Speed and Fuel Consumption Based on Neural Networks. Image Credit: Abs0lute/Shutterstock.com
The goal was to enhance engine performance, reliability, and adaptability across different flight modes by developing a mathematical model that establishes a relationship between rotor speed and engine output power. The researchers designed a fuel controller using a neuro-fuzzy network to analyze input data and make real-time adjustments for improved operational efficiency.
Helicopter Turboshaft Engine Control
Helicopter TEs are crucial in ensuring efficient rotor operation and providing the necessary lift and maneuverability for flight. The automatic control system (ACS) of these engines focuses on maintaining stable main rotor speed, which heavily depends on precise control of power characteristics, particularly fuel consumption and free turbine rotor speed.
Traditional methods, such as proportional-integral-derivative (PID) controllers, face significant challenges in handling rapidly changing external loads and complex dynamic conditions. While adaptive control systems and neural network-based models have shown potential to improve the prediction and adjustment of engine parameters, they often encounter limitations, including slow data processing, high computational demands, and the necessity for large, high-quality training datasets. Additionally, existing research frequently lacks dynamic regulation using predictive neural network models, particularly for real-time fuel optimization and adaptation to sudden external changes.
A Novel Neuro-Fuzzy Control Approach for Helicopter TEs
In this paper, the authors developed a method for controlling helicopter TE power characteristics by regulating free turbine rotor speed and fuel consumption. They created a mathematical model that linked the main rotor speed, free turbine rotor speed, gas-generator rotor speed, and fuel consumption. This model effectively describes the dynamic relationships among these parameters and their impact on engine power.
The study presented a fuel consumption controller based on a neuro-fuzzy network. This network processed input data, including desired and current rotor speeds, their derivatives, and feedback from the gas-generator rotor speed derivative. The neuro-fuzzy framework used fuzzy logic to convert precise input data into fuzzy values, allowing decisions based on fuzzy rules.
Additionally, the researchers integrated a long-short-term memory (LSTM) recurrent structure, a type of deep neural network, within the network to facilitate learning from past states and adapt controller settings in real-time. To enhance signal quality and ensure data consistency, the training and testing datasets were prepared using time sampling and adaptive quantization methods.
Performance Evaluation and Key Findings
The study compared the performance of the developed controller with that of a traditional linear PID controller using flight data from a Mi-8MTV helicopter equipped with a TV3-117 turboshaft engine. The outcomes showed that the neuro-fuzzy controller significantly enhanced control performance, reducing the transient fuel consumption process time by 8.92%. Specifically, it improved accuracy by 18.28% and increased the F1 score, which measures the harmonic mean of precision and recall, by 21.32%. The overshoot remained below 1.302%, and the control accuracy of engine power dynamics reached 99.3%.
These results highlighted substantial improvements in transient processes, power distribution, accuracy metrics, and loss metrics, underscoring the superiority of the neuro-fuzzy controller. Furthermore, cluster analysis of the training and testing datasets confirmed that the data used for model training and validation were homogeneous and representative.
The authors emphasized the importance of adaptive control in addressing the challenges posed by sudden external load changes and dynamic operating conditions. By incorporating feedback mechanisms and employing advanced data processing techniques, the neuro-fuzzy network demonstrated superior responsiveness compared to conventional PID controllers. This adaptability is important for ensuring the reliability and safety of helicopter operations, particularly in unpredictable environments.
Applications
This research has significant implications for the aerospace industry. Integrating neuro-fuzzy networks into engine control systems could enhance fuel efficiency, reduce operational costs, and improve flight safety. The proposed method can help design and implement more efficient control systems for helicopter turboshaft engines, increasing adaptability across various flight modes. Real-time fuel optimization and adaptive responses to changing external loads and operating conditions are also valuable.
The improved accuracy and faster response times boost engine performance and enhance fuel economy. Additionally, these methods could apply to other aircraft engines, potentially transforming engine management across the aviation sector.
Conclusion and Future Directions
In summary, the novel methodologies proved effective for controlling the energy characteristics of helicopter turboshaft engines by regulating free turbine rotor speed and fuel consumption. This approach can potentially revolutionize the aerospace industry by improving helicopter reliability, operational efficiency, and safety.
The authors also acknowledged limitations, including the dependency on high-quality and extensive datasets for training neural networks, which could limit performance in extreme operating conditions. Additionally, the algorithm's complexity may hinder the interpretation of control processes, making it challenging to identify clear cause-and-effect relationships.
Future work should validate the proposed method in real-world scenarios to ensure its practicality. Refining modern control strategies and enhancing comparison methods could further improve the accuracy and reliability of monitoring energy characteristics in turboshaft engines. Exploring the adaptive component’s role in reducing oscillations and improving stability under varying loads will also be crucial for optimizing control performance.
Journal Reference
Vladov, S.; & et al. Method for Helicopter Turboshaft Engines Controlling Energy Characteristics Through Regulating Free Turbine Rotor Speed and Fuel Consumption Based on Neural Networks. Energies 2024, 17, 5755. DOI: 10.3390/en17225755, https://www.mdpi.com/1996-1073/17/22/5755
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