Advanced Implementation of the Asymmetric Distribution Expectation-Maximum Algorithm in Fault-Tolerant Control for Turbofan Acceleration

For the safety and performance of turbofan engines, the fault-tolerant control of acceleration schedules is becoming increasingly necessary. However, traditional probabilistic approaches struggle to satisfy the single-side surge boundary limits and control asymmetry. Moreover, the baseline fault-tolerance requirement of the acceleration schedule cannot depend on whether fault detection exists, and model-dependent data approaches inherently limit their generalizability. To address all these challenges, this paper proposes a probabilistic viewpoint of non-frequency and non-Bayesian schools, and the asymmetric distribution expectation-maximum algorithm (ADEMA) based on this viewpoint, along with their detailed theoretical derivations. The surge boundary enhances safety requirements for the acceleration control; therefore, simulations and verifications consider the disturbance combinations involving a single significant fault alongside normal deviations from other factors, including minor faults. In the event of such disturbances, ADEMA can effectively prevent the acceleration process from approaching the surge boundary, both at sea level and within the flight envelope. It demonstrates the smallest median estimation error (0.27% at sea level and 0.96% within the flight envelope) compared to other methods, such as the Bayesian weighted average method. Although its maintenance of performance is not exceptionally strong, its independence from model-data makes it a valuable reference.