Preview-Aware LSTM-Assisted Predictive Control for Turboshaft Engines Under Tiltrotor Conversion-Flight Power Demand

Conversion flight turns the aerodynamic versatility of a tiltrotor into a demanding propulsion-control problem. As the nacelles rotate and vertical load transfers from the proprotors to the wing, the two turboshaft engines must follow a rapidly changing shaft-power demand while respecting fuel command magnitude and rate limits, compressor-pressure limits and turbine temperature limits. A control-oriented conversion model is coupled to a component-level turboshaft engine through a long short-term memory (LSTM) dynamic surrogate embedded in a constrained receding-horizon controller. The aircraft model resolves wing force balance, blade-element/momentum rotor loads, forward acceleration, nacelle actuation and accessory power. The LSTM predicts six engine outputs from flight conditions, fuel command and previous-step spool speeds. Training and evaluation use 537 converged component model cases divided by complete simulation cases into 375 training, 80 validation and 82 held-out test cases. Against parameter-matched multilayer perceptron, temporal convolutional network and gated recurrent unit baselines, the LSTM gives the lowest power root-mean-square error (20.15 kW before online output correction). Its corrected 20–320-step forecasts outperform a linear autoregressive model and zero-order hold prediction, although the linear model remains slightly better at one step. In direct component-level closed-loop simulation, LSTM engine-surrogate nonlinear model predictive control (NMPC) reduces power RMSE from 29.80 to 16.23 kW relative to linear MPC and from 34.94 to 16.23 kW relative to PI control while reducing cumulative fuel command variation by 46.5% relative to linear MPC. Turbine temperature and compressor-pressure margins remain 119.0 K and 85.4 kPa, respectively. Mean optimization time is 29.6 ms for a 1.92 s update interval. The resulting framework connects conversion flight aerodynamic loading, multi-step engine prediction and constrained power control in a reproducible numerical validation chain.