Research on the optimal selection method of sensors/actuators in active structural acoustic control for helicopter based on machine learning
Year: 2024
Type: article
Abstract: The reasonable placement of sensors/actuators is crucial in helicopter cabin noise control technology based on smart active panels. Confronting limitations in traditional methods, this paper proposes a control strategy based on radiation panel elements, aiming to identify and prioritize the control of the dominant acoustic radiation panel elements contributing significantly to cabin noise. To achieve this goal, in combination with machine learning technology, an intelligent optimal selection algorithm is proposed. The algorithm leverages a neural network model to accurately fit the relationship between panel vibration and radiation noise, guiding the optimal placement of sensors. Genetic algorithms are employed to optimize the selection of actuators . Simulations on a helicopter cabin model demonstrate the effectiveness of this approach, achieving significant noise reductions and decreased vibrations. Compared to empirical and conventional genetic methods, this algorithm provides enhanced control while reducing computation time by half, thereby improving overall efficiency.
Source: Measurement
Authors: Yang Yuan, Yang Lu, Jiaming Sun, Changtian Wang
Institution: Nanjing University of Aeronautics and Astronautics
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,650
Award: G5298326564
OpenAlex ID: W4405925696