# An Innovative Applied Control System of Helicopter Turboshaft Engines Based on Neuro-Fuzzy Networks
## 5 Conclusions  
This study focuses on the development of a helicopter turboshaft engine innovative fault-tolerant fuzzy automatic control system, significantly enhancing management efficiency in various flight modes. The research presents an innovative structure of a fuzzy control system, including a fuzzy controller, adaptation and reconfiguration modules, and a control channel selector. The key achievement is the helicopter turboshaft engines model's separation and the fuel metering unit; the system's responsiveness to changing conditions increases while achieving a control accuracy of more than 99%. The conducted computational experiment is focused on the TV3-117 turboshaft engine installed on the Mi-8 MTV helicopter, with engine data collected using high-precision sensors during flight tests (D2-M, D-1M, etc.). A special test stand was developed to reproduce working conditions, which allowed us to evaluate the system's dynamics and detect potential malfunctions.  
The results showed a high degree of fault tolerance for the developed system, with a cost function limit significantly below the acceptable level. The quality of the neuro-fuzzy network as the foundation for the automatic control system confirmed its effectiveness, achieving an accuracy of 99.455%. A comparative analysis with alternative approaches indicated that the proposed methodology significantly outperforms traditional solutions, providing higher fault recognition and minimizing false alarms.  
Despite the fact that notable successes were achieved, the research is limited by relying on data from flight tests of one type of engine, which potentially limits the wider applicability of its results. Moreover, the system's requirement for careful adjustment of control parameters in various flight modes creates problems for practical deployment. Prospects for further research are focused on expanding the model to cover a wider range of engines and aircrafts with the inclusion of advanced machine learning methods [108; 109; 110; 111]. The proposed fault-tolerant fuzzy control system demonstrates significant prospects for controlling an aircraft's flight, in particular, helicopters [87; 88]. By combining expert knowledge with adaptive control strategies, the developed fault-tolerant fuzzy control system effectively eliminates uncertainties and external failures (achieved accuracy of more than 99%), positioning itself as a valuable tool for use in civil and military aviation.  
Conceptualization, S.V.; methodology, S.V.; software, V.V. ([PERSON]); validation, V.V. ([PERSON]), P.P. and M.V.; formal analysis, S.V.; investigation, P.P. and M.V.; resources, O.L., V.V. ([PERSON]), V.V. ([PERSON]), P.P. and M.V.; data curation, S.V., V.V. ([PERSON]), P.P. and M.V.; writing--original draft preparation, S.V.; writing--review and editing, V.V. ([PERSON]), P.P. and M.V.; visualization, V.V. ([PERSON]); supervision, O.L. and V.V. ([PERSON]); project administration, O.L. and V.V. ([PERSON]); funding acquisition, V.V. ([PERSON]) and P.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data are contained within the article.  
This research was supported by the Ministry of Internal Affairs of Ukraine as part of \"Theoretical and applied aspects of the aviation sphere development\" under Project No. 0123U104884.  
The authors declare no conflicts of interest.