Modeling Control and Forecasting Nonlinear Systems Based on Grey Signal Theory

Abstract

Based on this article, a fuzzy NN (neural network) based on the EBA (evolved bat algorithm) was developed to devise adaptive control with gray signal prediction to provide asymptomatic stability and increased driving comfort. The method is used to assess plant nonlinearity and to perform structural tracking of the signal. The set of Gray’s differential equations is applied to Gray’s model (GM) (n, h), which has been an active system model. In the model, n is the order of the Gray’s differential equation and h is the number of variables considered. In this paper, a GM(2.1) has been utilised to achieve advanced nonlinear motion of a system, allowing the controller to demonstrate the efficiency and stability of the whole system in a Lyapunov-like expression. The controller design standard for a MEW (mechanical elastic wheel) is presented, creating a realistic framework in mathematical for practical engineering applications.