Robust Nonsingular Terminal Sliding-Mode Control for Nonlinear Magnetic Bearing System
ABSTRACT This study presents a robust nonsingular terminal sliding-mode control (RNTSMC) system to achieve finite time tracking control (FTTC) for the rotor position in the axial direction of a nonlinear thrust active magnetic bearing (TAMB) system. Compared with conventional sliding-mode control (SMC) with linear sliding surface, terminal sliding-mode control (TSMC) with nonlinear terminal sliding surface provides faster, finite time convergence, and higher control precision. In this study, first, the operating principles and dynamic model of the TAMB system using a linearized electromagnetic force model are introduced. Then, the TSMC system is designed for the TAMB to achieve FTTC. Moreover, in order to overcome the singularity problem of the TSMC, a nonsingular terminal sliding-mode control (NTSMC) system is proposed. Furthermore, since the control characteristics of the TAMB are highly nonlinear and time-varying, the RNTSMC system with a recurrent Hermite neural network (RHNN) uncertainty estimator is proposed to improve the control performance and increase the robustness of the TAMB control system. Using the proposed RNTSMC system, the bound of the lumped uncertainty of the TAMB is not required to be known in advance. Finally, some experimental results for the tracking of various reference trajectories demonstrate the validity of the proposed RNTSMC for practical TAMB applications.
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ABSTRACT: Current paper presents a recursive fast terminal sliding mode controller used to regulate blood glucose concentration. The fast terminal sliding mode controller makes the output to converge to the equilibrium point robustly and in finite time. In the controller of the system, the gain will be selected a priori based on the magnitude of uncertainty. The bound of uncertainties usually cannot be calculated in real systems. So, a fuzzy system is used to adjust the controller to encounter well with the uncertainties. Efficiency of the method is finally tested via simulations. The provided structure is then shown to be faster than previous algorithms.2012 4th International Conference on Intelligent & Advanced Systems (ICIAS); 06/2012
Journal of Guidance Control and Dynamics 02/2015; DOI:10.2514/1.G000912 · 1.15 Impact Factor
IEEE Transactions on Industrial Electronics 01/2015; DOI:10.1109/TIE.2015.2399397 · 6.50 Impact Factor