APPLICATION OF H_∞ CONTROL AND CLOSED LOOP IDENTIFICATION TO A MAGNETIC LEVITATION SYSTEM

Abstract

A systematic procedure for modeling and robust control of a multivariable magnetic levitation system is described. Our previous study revealed that an observer‐based LQ controller can stabilize the system, but generates spillovers in the presence of an impulse disturbance. To solve this problem, we apply an H∞ control to suppress the spillovers caused by unmodeled dynamics which we estimate using closed loop identification. First, an exactly linearized model is obtained to compensate for nonlinearities in the system, followed by estimation of the unmodeled dynamics using closed loop identification. Second, this information is used to design a two degree of freedom H∞ servo system for suppressing the spillover while tracking a step reference input. Finally, the desired robust performance of the resulting servo system is confirmed theoretically by γ‐analysis and also experimentally.

Control of a MIMO Magnetic Levitation System using Exponential Control Barrier Function

“…This system is nonlinear, open loop unstable and Single-Input/Single-Output (SISO) ( Barie and Chiasson (1996)). The magnetic levitation system scheme analyzed in this work is based on the experimental apparatus described in Fujii et al (1994) and Tsujino et al (1999). The system is constituted by a Y shape metal plate that must be levitated by electromagnetic attractive forces.…”

“…The works related to control of magnetic levitation systems typically are proposed to satisfy performance/stability objectives, i.e., tracking a desired reference input. Several control techniques are proposed and applied in the literature, such as sliding mode (Al-Muthairi and Zribi (2004)), fuzzy logic (Benomair and Tokhi (2015)), model predictive control (Karampoorian and Mohseni (2010)), backstepping (Liu and Zhou (2013)), neural network (M. Aliasghary and Teshnehlab (2008)) and H ∞ control (Tsujino et al (1999)). However, this work uses a control structure that simultaneously satisfy performance/stability objectives and safety constraints.…”

Modeling of Maglev Yaw System of Wind Turbines and its Robust Trajectory Tracking Control in the Levitating and Landing Process Based on Ndob

“…Due to processing the higher order nonlinear terms of the system as disturbances, and the vibration problems intrinsic to the sliding mode, its control effect is not accurate. The H∞ control method was applied to the maglev system [16], and the system robustness on the change of air gap or the mass of suspension electromagnet was improved, however, its problem was the higher order of controller. Linear state feedback control was used widely in most suspension controls, and a large number of works adopted Taylor series expansion to linearize the system model at the equilibrium position, which could result in the poor robustness performance due to ignoring the higher order terms [17,18].…”

Measurement and Control of Magnetic Flux Signal in a Maglev System

“…Wang and Hill put forward a deterministic learning (DL) theory for identification of nonlinear system [3] dynamics under full-state measurements. A systematic procedure for modeling and robust control of a multivariable magnetic levitation system is described in [4] by Tsujino et al The discrete-time model of the magnetic levitation system [5] is derived and the stability is guaranteed by the root locus methodology. Subrata Banerjee [6] designs a control philosophy for simultaneous stabilization and performance improvement of an electromagnetic levitation system.…”