A proportional controller based on clustering theory: an academic example of a machine learning discipline

Abstract

The main objective of this paper is to present a controller design based on the K-means clustering theory. The controller is realized in such way that when the plant output is located outside of the designed clustering set, the controller forces it to be in it. Moreover, and according to our real experiment applied to stabilize an unstable integrator plant, our controller approach design is also robust against un-vanishing perturbations and nonlinearity effects on the overall closed-loop system such as saturation, slew-rate limit, and limit bandwidth frequency operation.