Intelligent feedforward gust alleviation based on neural network

This paper proposes a neural network-based intelligent feedforward gust alleviation framework, which includes a neural network identification model and a neural network controller. A neural network training dataset is formed by collecting flight data and the gust data encountered during the aircraft flight. A neural network identification model is first trained to accurately predict the aircraft’s output. Then, based on the output of the identification model and the collected flight data, the parameters of the time-delay neural network controller are obtained through a learning process. The simulation results show that the designed intelligent controller has good gust alleviation effects for both continuous turbulence excitation and discrete gust excitation. For example, when the aircraft is 40000 kg and the flight speed is 0.81Ma, the controller achieves a 67.82% reduction in wingtip acceleration and a 35.90% reduction in center of mass acceleration under continuous turbulence excitation. When considering the measurement uncertainties, such as noise existing in the collected data, the trained controller can still achieve an acceptable gust alleviation effect. Finally, considering a flight in which the aircraft mass is constantly changing, the intelligent controller, which continuously learns from new flight data, maintains a good gust alleviation effect throughout the flight.