Design of backstepping control with CNN-based compensator for active magnetic bearing system subjected to input voltage saturation

Abstract

Purpose The stability and input voltage saturation is a common problem associated with an active magnetic bearing (AMB) system. The purpose of this paper is to design a control scheme that stabilizes the single degree of freedom AMB system and also tackle the problem of input voltage saturation in the AMB system. Design/methodology/approach The proposed control technique is a combination of two separate control schemes. First, the Backstepping control scheme is designed to stabilize and control the AMB system and then Chebyshev neural network (CNN)-based compensator is designed to tackle the input voltage saturation when the system control action is saturated. Findings The mathematical and simulation results are presented to validate the effectiveness of proposed methodology for single-degree freedom AMB system. Originality/value This paper introduces a CNN-based compensator with Backstepping control strategy to stabilize and tackle the problem of input voltage saturation in the 1-DOF AMB systems.

The Chebyshev Difference Equation

“…These polynomials obey the recurrence (7). Naively replacing t 2 with t 2 , we obtain the following possible discrete analogues:…”

“…We shall solve Equation (10) and (11) in terms of solutions of Equation (12), and we will develop some of their properties that justify calling these Chebyshev difference equations. The phrase "Chebyshev difference equation" sometimes appears in the literature, e.g., in the recent article [7] (40), in reference to the Equation (7) and in [8] (5.2) which refers to a scaled version of Equation (7) for monic Chebyshev polynomials. We do not use the terminology in this way.…”