Neural network control and performance simulation of an active control mount with an oscillating coil actuator Online publication date: Thu, 03-Sep-2020
by Rang-Lin Fan; Jia-Ao Chen; Zhen-Nan Fei; Chu-Yuan Zhang; Fang-Hua Yao; Quan-Fa Wu
International Journal of Computer Applications in Technology (IJCAT), Vol. 63, No. 3, 2020
Abstract: Active Control Mounts (ACMs) are an effective solution to improve the comfort of passenger cars. This paper aims to apply neural networks to ACMs and explores the general process of neural network ACMs. A three-layer BP Neural Network Model (NNM) is established with an Oscillating Coil Actuator (OCA) as the controlled object. The actuator output force is collected as training samples when it is excited under different types of input current signals. Learning is performed, and the result shows the identified NNM based on random signals has good accuracy. Based on this well-identified NNM, two control methods - neural network direct self-tuning control and NNM reference control are discussed. The simulation results for typical low, medium and high frequencies show both control methods achieve good vibration isolation effects. This research shows the strong adaptability of neural networks, which lays a good foundation for subsequent control system development.
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