Vibration Control in Wind Tunnel by Biologic Neurons Methods

Abstract

This study presents an innovative approach for vibration control in wind tunnels using a combination of Back-Propagation (BP) neural network and Proportional-Integral-Derivative (PID) control. Wind tunnel testing plays a crucial role in aerodynamic research, but it often faces challenges related to vibrations that can affect the accuracy of measurements. Traditional PID controllers are effective but may struggle to adapt to dynamic and complex vibration patterns. In contrast, BP neural networks offer learning capabilities and adaptability, making them suitable for handling such challenges, also examines the impact of active vibration control with feedback control to reduce or eliminate unwanted vibrations in a system actively with development and experimental evaluation controlled separately by both PID and BP neural network to make comparisons by MATLAB - Simulink software's thorough Laboratory with vibrations in the sting can affect the accuracy and repeatability of the test results, so active vibration control can be used to improve the performance of the wind tunnel. The proposed system integrates a BP neural network with a PID controller to create a robust vibration control mechanism. The BP neural network is trained to learn the dynamic behavior of the wind tunnel system and generate control signals based on input parameters such as setpoints, error signals, and their derivatives. The PID controller works in tandem with the neural network to fine-tune the control signals and ensure stability. The experimental results demonstrate the effectiveness of the proposed approach in suppressing vibrations in wind tunnel testing. The system achieves improved accuracy and stability, leading to more reliable aerodynamic measurements. This research to shows BB neural network vibration control impact and analyze the behaver of K_p, K_i, and K_d.