A proposed algorithm based on long short-term memory network and gradient boosting for aeroengine thrust estimation on transition state

Aeroengine thrust estimation is an important problem for direct thrust control since it is unmeasurable. Many methods and algorithms have been proposed to solve this problem. Unfortunately, almost all these methods can only estimate aeroengine thrust when the engine is in steady state. Hence, this study proposes an algorithm based on long short-term memory networks and gradient boosting for aeroengine thrust estimation in transition state. The newly proposed algorithm can estimate thrust of an aeroengine when its working state is changed from one steady state to another. The experimental results demonstrated that the proposed algorithm can be well applied to estimate aeroengine thrust in transition state and the estimated precision can meet the requirements of thrust estimation.