Model Predictive Control Based on Linear Parameter-Varying Models of Active Magnetic Bearing Systems

نویسندگان

چکیده

Active magnetic bearing (AMB) system has been recently employed widely as an ideal equipment for high-speed rotating machines. The inherent challenges to control the include instability, nonlinearity and constricted range of operation. Therefore, advanced technology is essential optimize AMB performance. This paper presents application model predictive (MPC) based on linear parameter-varying (LPV) models subject input state constraints. For this purpose, LPV representation derived from nonlinear dynamic system. In order provide stability guarantees since obtained a large number scheduling parameters, parameter set mapping (PSM) technique used reduce their number. Based reduced model, terminal cost ellipsoidal are determined offline included into MPC optimization problem which ingredients guaranteeing closed-loop asymptotic stability. Moreover, recursive feasibility problem, slack variable its function. goal proposed feedback twofold. First demonstrate high performance by achieving stable levitation rotor shaft well capability reference tracking without violating constraints, increases overall safety under disturbances effects. Second computationally tractable LPVMPC algorithm, substantial requirement in practice operating with over full range. we propose scheme frozen prediction horizon MPC. Furthermore, simulation that such can achieve comparable more sophisticated developed standard NL (NMPC) approach. verify LPVMPC, comparison classical controller, commonly applied practice, provided.

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ژورنال

عنوان ژورنال: IEEE Access

سال: 2021

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2021.3056323