Magnetorheological damper current estimation based on k-nearest neighbor algorithm

Abstract

Magnetorheological dampers are highly nonlinear damping elements which exhibit multi-dimensional data correspondence problems in the current input and damping force output data sets. Therefore, it is inconvenient to use the traditional modeling method to predict the output damping force of the magnetorheological damper accurately. Although the neural network can predict data-driven output results to a high precision, more models, parameters, and complex structures are required to predict the expected current from the magnetorheological test data set. In this study, a prediction model of the expected current in a magnetorheological damping system was proposed by using the k-nearest neighbor algorithm based on the classification algorithm in machine learning. Then, the k-nearest neighbor prediction model was trained and tested with data obtained experimentally. In order to verify the performance of the k-nearest neighbor algorithm, a comparison was made with a prediction model based on the BP neural network, and the damping force output of the predicted current was simulated. The study results show that the k-nearest neighbor prediction model established has a more straightforward working principle, fewer input variables, and a higher approximation accuracy compared with the BP neural network control system.