# An Innovative Applied Control System of Helicopter Turboshaft Engines Based on Neuro-Fuzzy Networks
## 4 Discussion
The loss function decreases from 2.5 to 0.5% over the same training period. These results suggest good model optimization and reliability for practical applications, with the potential for further improvements in adaptability and performance under dynamic operational conditions. The helicopter TE control task comparative analysis (see Tables 3 and 4) using the developed neuro-fuzzy control system (see Figure 4) was conducted against four alternative approaches: 1 is the neural closed-loop triple-channel onboard control system, 2 is the neuro-fuzzy onboard system based on ANFIS, 3 is the neuro-fuzzy onboard system based on AFNN, and 4 is the traditional control system. The comparative analysis revealed that the proposed method demonstrates a high accuracy (99.5%) and F1-score (0.990), confirming its effectiveness and reliability. In contrast, the alternative approaches showed less satisfactory results, particularly Approach 4, with an accuracy of 88.2%. Approaches 1 and 2 provided acceptable results but were outperformed by the proposed solution, which also features perfect recall (recall is 1.0), indicating the full recognition of all present positive cases. As shown in Table 4 and Figure 14, the proposed method surpasses the others in terms of fault detection, with the highest true positive rate (0.828) and the lowest false alarm rate (0.0101), supported by an AUC-ROC of 0.831. Thus, the developed FTCS (see Figure 4) application on board helicopters improves control efficiency and reduces accident probability [87; 88; 89; 90; 91; 92], providing better integration of expert knowledge and enhancing resilience to uncertainties and external factors. However, this study's one limitation is the exclusive use of flight data from a specific engine type (TV3-117), which may restrict the results' generalizability to other engine types or operating conditions. Additionally, the proposed control model requires precise tuning and calibration of system parameters for different flight modes, which may complicate its implementation on real helicopters, especially for significant changes faced in operating conditions or system configuration. Furthermore, variations in flight conditions, such as altitude, temperature, or load fluctuations, may lead to discrepancies in the model's performance if not accounted for during the calibration process. The model's reliance on specific engine characteristics also limits its adaptability, potentially requiring significant reconfiguration or retraining when applied to engines with differing dynamic behaviors or performance parameters. Future research directions include extending the model to other engine types [93; 94; 95; 96] and aircrafts [97; 98] and integrating more advanced machine learning [99; 100] and predictive methods, such as deep neural networks [101; 102] and reinforcement learning techniques [103; 104; 105], to enhance the system's adaptability to dynamic flight conditions. Furthermore, optimizing computational costs and developing self-correction algorithms [106; 107] could improve system reliability and fault tolerance in real time. In future directions of research, models will be developed in which clear systems and machine learning methods will be integrated in the current model to improve context. Mentioning specific approaches would provide more effective guidance and better support the future relevance of the research. In addition, future directions of research will include the development of a potential extension of the model to engines and aircrafts of other types [108; 109; 110; 111]. Also, discussing the possible problems of more advanced machine learning methods and how they could contribute to the system's adaptability will provide more context and justify the proposed future directions. It is also planned to calculate the added value for demonstration in experimental research.