Control Strategies and Health Management for Aircraft Engines

Control strategies and health management for aircraft engines encompass a combination of real-time regulatory techniques and predictive maintenance frameworks to ensure safety, reliability and optimal performance. Control strategies range from classical proportional-integral-derivative schemes and gain-scheduling approaches to advanced model predictive control that anticipates future operating conditions and enforces constraints across the entire flight envelope. Neural adaptive controllers and situational control paradigms further enhance responsiveness to rapid thrust commands and atypical flight scenarios. In parallel, health management systems integrate data-driven and model-based methods—such as analytical redundancy, filtering approaches and machine-learning algorithms—to detect sensor faults, estimate component degradation and predict remaining useful life. The convergence of high-fidelity dynamic modelling, onboard computation and big-data analytics enables continuous condition monitoring, early fault diagnosis and informed maintenance scheduling. Together, these advances reduce unscheduled downtime, lower life-cycle costs and contribute to fuel efficiency and environmental objectives, supporting both commercial and military aviation applications worldwide.

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Control Strategies and Health Management for Aircraft Engines publication trend

The graph below shows the total number of articles in control strategies and health management for aircraft engines across all publications each year (not limited to Nature Index journals).

Technical terms

Model predictive control: An optimisation-based strategy that predicts future engine behaviour over a finite horizon and computes control inputs to satisfy performance and safety constraints.

Neural adaptive PID control: A proportional-integral-derivative scheme whose gains are tuned online by a neural network to handle nonlinearity and time-varying dynamics.

Convolutional neural network (CNN): A deep-learning architecture that applies convolutional filters to extract hierarchical features from time-series or image-like sensor data.

Long short-term memory (LSTM) network: A type of recurrent neural network designed to capture long-range dependencies in sequential data, suited to predicting engine fault progression.

Prognostics and health management (PHM): An integrated framework for monitoring engine condition, diagnosing faults and forecasting remaining useful life to optimise maintenance decisions.

Remaining useful life (RUL): The estimated operational time or usage left before a component or system reaches a predefined failure threshold.

References

1. Advanced Constraints Management Strategy for Real-Time Optimization of Gas Turbine Engine Transient Performance. Applied Sciences (2019).
2. Single Neural Adaptive PID Control for Small UAV Micro-Turbojet Engine. Sensors (2020).
3. Fault Detection of Aero-Engine Sensor Based on Inception-CNN. Aerospace (2022).
4. A Prognostic and Health Management Framework for Aero-Engines Based on a Dynamic Probability Model and LSTM Network. Aerospace (2022).