A new model-free adaptive controller versus non-linear H_∞ controller for levitation of an electromagnetic system

Abstract

In this paper we aim to survey the performance of a non-linear H∞ method and a new proposed controller on a magnetic levitation model (maglev). The proposed controller is a new model-free adaptive design approach using an adaptive-fuzzy procedure based on feedback linearization. The main idea of the new controller comprises two steps: first, by means of the feedback linearization method, a measured signal is taken to a specific level with an error less than a defined value and second, proposed rules are applied to the system to keep the error near zero. The major advantage of new controller is that there is no need for identification of the system dynamics and only the output error is required.

Active Vibration Isolation of a Maglev Inertially Stabilized Platform Based on an Improved Linear Extended State Observer

“…Fuzzy control has been adopted for the stability of ISP control systems, and research [10] has concentrated on improving the transient process and tracking accuracy of rotating shafts. Many efforts have been made to counter magnetic bearing control [11][12][13][14][15][16][17], which has a significant impact on the performance of MISPs. In [11], a PD controller and PID controller were adopted in a magnetic bearing control to obtain an ideal result.…”

“…In [11], a PD controller and PID controller were adopted in a magnetic bearing control to obtain an ideal result. The H∞ method [13] and a quantitative feedback principle [14] were employed in many studies due to the boundedness of linear models.…”

Design and implementation of an adaptive backstepping controller for a magnetic levitation system

“…In this manner, many different controller structures have been designed and realized for magnetic levitation models in order to deal with uncertainties. Fuzzy logic (Golob and Tovornik, 2003) and adaptive fuzzy logic (Javadi and Pezeshki, 2013) controllers are proposed to stabilize the position of the ball. In addition, Javadi and Pezeshki (2013) compare the performances of adaptive fuzzy logic controllers and nonlinear H ∞ controllers.…”

“…Fuzzy logic (Golob and Tovornik, 2003) and adaptive fuzzy logic (Javadi and Pezeshki, 2013) controllers are proposed to stabilize the position of the ball. In addition, Javadi and Pezeshki (2013) compare the performances of adaptive fuzzy logic controllers and nonlinear H ∞ controllers. Model based robust controller structures dealing with parametric uncertainties are also designed for the magnetic levitation system using non-linear damping (Yang and Minashima, 2001) and internal model-based design (Bonivento et al, 2005) techniques.…”

Cognitive non-linear controller design for magnetic levitation system

“…The basic prediction problems in the position tracking for a magnetic ball levitation system are still there, waiting to be addressed; therefore, the fundamental essences of the motivation for this work are (Javadi and Pezeshki, 2013;Jing et al, 2013;Morales et al 2011;Qin et al, 2014): to generate an optimal control action for the electromagnetic system; to track the desired steel ball position with minimum tracking error; to overcome the effect of disturbance/noise signals and to save energy of the magnetic levitation system. The contributions of this paper are summarized by the following points: 1) Developing the analytical derivation for the non-linear control law with high computational accuracy, which is based on the Lyapunov criterion stability, backstepping method and cognitive methodology.…”