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  • Open Access


    Dynamic Sliding Mode Backstepping Control for Vertical Magnetic Bearing System

    Wei-Lung Mao1,*, Yu-Ying Chiu1, Chao-Ting Chu2, Bing-Hong Lin1, Jian-Jie Hung3

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 923-936, 2022, DOI:10.32604/iasc.2022.019555

    Abstract Electromagnets are commonly used as support for machine components and parts in magnetic bearing systems (MBSs). Compared with conventional mechanical bearings, the magnetic bearings have less noise, friction, and vibration, but the magnetic force has a highly nonlinear relationship with the control current and the air gap. This research presents a dynamic sliding mode backstepping control (DSMBC) designed to track the height position of modeless vertical MBS. Because MBS is nonlinear with model uncertainty, the design of estimator should be able to solve the lumped uncertainty. The proposed DSMBC controller can not only stabilize the nonlinear system under mismatched uncertainties,… More >

  • Open Access


    An Adaptive Extended Kalman Filter Incorporating State Model Uncertainty for Localizing a High Heat Flux Spot Source Using an Ultrasonic Sensor Array

    M.R. Myers1, A.B. Jorge2, D.E. Yuhas3, D.G. Walker1

    CMES-Computer Modeling in Engineering & Sciences, Vol.83, No.3, pp. 221-248, 2012, DOI:10.3970/cmes.2012.083.221

    Abstract An adaptive extended Kalman filter is developed and investigated for a transient heat transfer problem in which a high heat flux spot source is applied on one side of a thin plate and ultrasonic pulse time of flight is measured between spatially separated transducers on the opposite side of the plate. The novel approach is based on the uncertainty in the state model covariance and leverages trends in the extended Kalman filter covariance to drive changes to the state model covariance during convergence. This work is an integral part of an effort to develop a system capable of locating the… More >

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