Conference Paper

Robust Nonlinear Control of a Magnetic Levitation System via Backstepping Design Approach

Fac. of Comput. Eng. & Syst. Sci., Kyushu Inst. of Technol., Fukuoka
DOI: 10.1109/SICE.1998.742978 Conference: SICE '98. Proceedings of the 37th SICE Annual Conference. International Session Papers
Source: IEEE Xplore


Proposes a robust nonlinear controller for the position tracking
problem of a magnetic levitation system, which is governed by a SISO
second-order nonlinear differential equation. The controller is designed
in a backstepping manner, based on the nonlinear system model in the
presence of parameter uncertainties. The effects of the parameter
uncertainties are reduced by a nonlinear damping term and the effects of
position error are removed by a PI controller. Input-to-state stability
of the control system is analyzed and experimental results are included
to show the excellent position tracking performance of the designed
control system

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    • "Therefore, it is an interesting and impressive system for engineers and researchers. There are various methods for nonlinear system control, such as backstepping control [6] [7] [8] [9] [10], sliding mode control [3] [4] [5], neural network control [5] [8] and adaptive control [8] [9] [10] etc. The backstepping control is a systematic, methodical recursion nonlinear feedback control method which has presented by Kanellakopoulos et al. [11]. "
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    ABSTRACT: This paper solves the uncertainty of nonlinear system using the backstepping control with sliding function in magnetic ball suspension system. The classical backstepping control is a systematic, methodical recursive nonlinear feedback control method which is fit to design in high order complicated system. A backstepping control and a sliding function are added in design process. The magnetic ball suspension system is employed to validate the proposed controller. The experimental results show that the integral backstepping sliding mode controller is successful solving both the uncertainty of nonlinear system and the steady error of classical backstepping control method.
    Power Electronics and Drive Systems (PEDS), 2013 IEEE 10th International Conference on; 01/2013
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    • "The problems arise from variations of the parameters due to environmental conditions or thermal drifts. Nonlinear controllers [7] [8] , robust linear controllers such as H∞, optimal control and µ-synthesis [9] [10] , control based on phase space [11] , neural network methods [12] [13] and fuzzy control [14] are other proposed approaches to control magnetic levitation systems. The proposed dynamic models for the magnetic levitation system usually have some uncertainties due to simplified dynamic equation and its parameters. "
    11/2011; 2(2):35. DOI:10.3968/j.est.1923847920110202.114
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    • "Other types of nonlinear controllers based on nonlinear methods have been reported in the literature [6] [7] [8]. "
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    ABSTRACT: Neural network Based controller is used for controlling a magnetic levitation system. Feedback error learning (FEL) can be regarded as a hybrid control to guarantee stability of control approach. This paper presents simulation of a magnetic levitation system controlled by a FEL neural network and PID controllers. The simulation results demonstrate that this method is more feasible and effective for magnetic levitation system control.
    2th IEEE International Conference on Power and Energy (PECon08); 01/2008
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