Nonlinear Model Predictive Control Using Feedback Linearization for a Pressurized Water Nuclear Power Plant
Nonlinear Model Predictive Control Using Feedback Linearization for a Pressurized Water Nuclear Power Plant
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DOI：
10.1109/access.2022.3149790
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发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Amine Naimi;Jiamei Deng;Vineet Vajpayee;V. Becerra;S. Shimjith;A. Arul
中科院分区：
文献类型：
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作者：
Amine Naimi;Jiamei Deng;Vineet Vajpayee;V. Becerra;S. Shimjith;A. Arul
目前的工作旨在引入一种结合智能反馈线性化（FBL）和压水堆（PWR）模型预测控制（MPC）的非线性控制方案。本研究中考虑的非线性被控对象模型采用第一性原理方法描述，由 38 个状态变量组成。首先，使用动态神经网络（DNN）结构进行系统辨识，以获得标准仿射非线性系统。采用拟牛顿算法来寻找最佳的 DNN 模型。然后，制定 FBL 来解决 DNN 模型的非线性问题。为了提高系统性能，在FBL系统的基础上开发了MPC控制器。将设计的控制器与基于状态空间模型的线性 MPC 控制器进行比较，以评估所提出的控制器的性能。所提出的方法改进了负载跟踪操作，并提供比传统 MPC 更好的抗扰能力。此外，还采用数值测量来比较和分析两种控制策略的性能。
The present work aims to introduce a nonlinear control scheme that combines intelligent feedback linearization (FBL) and a model predictive control (MPC) for a pressurized water reactor (PWR). The nonlinear plant model that is considered in this study is described by the first-principles approach, and it consists of 38 state variables. First, system identification using a dynamic neural network (DNN) structure is performed to obtain a standard affine nonlinear system. The quasi-Newton algorithm is employed to find the best DNN model. Then, an FBL is formulated to address the nonlinearity of the DNN model. An MPC controller is developed based on the FBL system to improve the system performance. The designed controller is compared with a linear MPC controller that is based on state-space models to evaluate the performance of the proposed controller. The proposed approach improves the load-following operation and offers better disturbance rejection capability than the conventional MPC. In addition, numerical measures are employed to compare and analyze the performances of the two control strategies.