Improving the Intelligent Control of Magnetic Levitiation Ball Using Artificial Neural Network (International Journal of Engineering and Computer Science, 2019)
Forecasting the time series behavior of magnetic levitation system has been a major research objective for the last five decades. This is due to the challenges presented as a result of its dynamic nature in motion. Initially this problem was supposed to be solves by control engineering researchers using predictive control modeling. However, due to the robust nature of the technological application (high speed train, maglev ball, e.t.c), precision in the control system has been a major challenge according to Earnshaw’s theorem. This paper improves intelligent control of a magnetic levitation (ball) system using artificial neural network. First the maglev system plant is identified and then a Neuro controller is designed to predict the dynamic behavior of this system using feedback linearization process. The system will be design and simulated using neural network tool and Mathlab.