Nonlinear Model Predictive Control Using Feedback Linearization for a Pressurized Water Nuclear Power Plant

نویسندگان

چکیده

The present work aims to introduce a nonlinear control scheme that combines intelligent feedback linearization (FBL) and model predictive (MPC) for pressurized water reactor (PWR). plant is considered in this study described by the first-principles approach, it consists of 38 state variables. First, system identification using dynamic neural network (DNN) structure performed obtain standard affine system. quasi-Newton algorithm employed find best DNN model. Then, an FBL formulated address nonlinearity An MPC controller developed based on improve performance. designed compared with linear state-space models evaluate performance proposed controller. approach improves load-following operation offers better disturbance rejection capability than conventional MPC. In addition, numerical measures are compare analyze performances two strategies.

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ژورنال

عنوان ژورنال: IEEE Access

سال: 2022

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2022.3149790