Abstract
Abstract This paper conducts a study on closed-loop control of engine performance parameters during mode transition process of TBCC engine based on artificial intelligence method. Firstly, a composite modeling method based on stepwise regression analysis and batch normalization-depth neural network is proposed to establish the on-board model during mode transition to estimate the thrust and inlet airflow in real-time. Secondly, based on the hybrid penalty function-particle swarm optimization algorithm, a mode transition control schedule applicable to the closed-loop control of thrust and inlet airflow is developed. Finally, a data processing method based on similarity conversion is proposed to extend the applicable envelope range of the mode transition control system. The transition time is shortened by 33.3 %, and the fluctuations of thrust and inlet airflow are reduced by 1.33 % and 10.77 %, respectively. When the control system is applied to the off-design mode transition process, a satisfactory mode transition performance is also obtained.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
