Neural network-driven predictive control and fractional-order nonlinear filter optimization for helicopter active vibration control

The vibration problem in helicopters is a critical factor that affects both flight safety and passenger comfort. Active control is essential for vibration reduction, but several challenges are often encountered, such as phase delay in response, low system control accuracy, and slow convergence of control signals. This study explores the use of artificial intelligence (AI) to address the helicopter vibration reduction problem by proposing an approach based on neural network-driven predictive control and fractional-order nonlinear filter (N-PC-FNF) optimization for helicopter active vibration control. The approach first employs a bidirectional long short-term memory (BiLSTM) neural network to predict the time series of helicopter vibration signals, laying the foundation for the subsequent calculation of reverse vibration reduction signals in advance. Next, a fractional-order nonlinear filter combined with variational mode decomposition (VMD) is designed to decompose the complex vibration signals into intrinsic mode functions (IMFs) with distinct frequency domain characteristics. The fractional-order parameters are flexibly adjusted to better adapt to the nonlinear dynamic variations in the vibration signals. Finally, the weights of the filter are transferred to a multilayer perceptron (MLP) network for training, enabling the calculation of the filter’s steady-state weights and the actual actuator output signals, thereby achieving active control of helicopter vibrations. The research demonstrates that the proposed approach, which integrates neural network-driven predictive control with fractional-order nonlinear filter optimization, effectively realizes helicopter active vibration control and meets the requirements of engineering applications.