# An Innovative Applied Control System of Helicopter Turboshaft Engines Based on Neuro-Fuzzy Networks
## 4 Discussion  
This study focuses on developing an innovative helicopter TE FTCS (see Figure 4). The proposed helicopter TE FTCS (see Figure 4) includes a fuzzy regulator, an adaptation and reconfiguration unit, a control channel selector, an actuator block, a helicopter TE model, a fuel metering needle model, and a sensor block. The fuzzy regulator consists of a fuzzifier, inference mechanism, defuzzifier, fuzzy rule base, and monitoring block. The key distinction lies in separating the helicopter TE and FMU models, improving control accuracy and adaptability to changing conditions. To achieve this, the helicopter TE operation innovative control law (23) has been proposed, differing from the traditional approach by using an adaptive control system instead of static settings. This system dynamically adjusts fuel supply and the blade pitch angle. The model is based on maintaining free turbine rotor speed, which is crucial for safe piloting while accounting for changing flight conditions such as altitude, temperature, and power. The helicopter TE chosen for the computational experiment was the TV3-117, installed on an Mi-SMTV helicopter. Engine parameters (\(n_{TC}\), \(n_{FT}\), \(T_{G}^{*}\), etc.) were collected exclusively from flight data recorded during helicopter trials. The data were logged using D-2M and D-1M sensors and 14 pairs of T-101 thermocouples over 320 s of actual flight time at a frequency of one per second (see Figure 1). A simulation test stand (see Figure 8) was developed, representing an improved version of the stand described earlier in [57]. The test stand simulates helicopter TE parameters in real time and reproduces operating modes across a range of altitudes and airspeeds. Moreover, it supports interactions with higher-level systems through data exchange channels, allowing for control unit testing and performing other tasks [57]. The research explored failure modeling related to the control loss in the fuel supply actuator within the gas generator rotor r.p.m. channel. A failure model (see Figure 9) was created as a production rule comprising conditions and actions describing system behavior during failure scenarios. This model accounts for dynamic changes in system parameters and probabilistic failure characteristics, enabling the prediction of potential malfunctions and their impact on gas generator operation. Production rules (43) are used for the failure scenarios' automatic generation and their effects on subsequent system performance analysis, which is essential for developing reliable diagnostic and control methods in natural operational environments. The failure model transient characteristic analysis, associated with control loss in the fuel supply actuator within the turbo-compressor rotor speed channel, showed that sudden failure (surge and decay in Figure 10) causes a sharp drop in fuel supply. This results in reduced rotor speed and impaired engine dynamics. For comparison, the standard engine trajectory is shown as a dashed red line in Figure 10, highlighting the failure's impact on system performance. The developed helicopter TE FTCS fault tolerance assessment (see Figure 11) revealed that the cost function's maximum value during the studied time interval (from 0 to 320 s) is 0.137, significantly below the allowable value of 1, which is set for normalized parameters. This indicates a high degree of fault tolerance in the system, effectively controlling deviations from target values while minimizing control efforts and the effects of external disturbances during the specified period. The neuro-fuzzy network's (see Figure 5) quality, forming the developed helicopter TE FTCS's (see Figure 4) foundation, was evaluated using traditional metrics such as accuracy, loss, precision, recall, F1-score, and AUC-ROC. Figures 12 and 13 present accuracy and loss diagrams. The developed neuro-fuzzy network's (Figure 5) accuracy reaches 99.455% after 250 training epochs in the helicopter engine control task, demonstrating the model's high efficiency under various conditions.