Abstract:
This study investigates the application of neural network architectures to predict control inputs required to replicate rotorcraft responses under vertical gust disturbances. Two modeling approaches are developed: the Control Equivalent Gust Input (CEGI) model, using body-axis inputs and the Rotor Control Equivalent Gust Input (RCEGI) model using rotor-specific inputs. Initial models employed single-input single-output (SISO) LSTM networks, which demonstrated limitations in capturing transient behavior and exhibited delay in predicted control inputs. By incorporating multiple vehicle response features and increasing the number of hidden neurons, multiple-input single-output (MISO) architectures significantly improved accuracy and reduced Root Mean Square Error (RMSE). Further enhancement was achieved by implementing bidirectional LSTM (BiLSTM) layers, which reduced both delay and transient error. Comparisons with inverted linear time-invariant (LTI) approximations showed that neural networks provided superior performance, particularly in modeling nonlinear dynamics. The results highlight the potential of deep learning approaches to improve the accuracy of control input mapping and inform real-time control strategies in unsteady flight environments.