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

Abstract

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 analyse the performances of the two control strategies.

An overview of power reactor kinetics and control in load-following operation modes

“…Most publications on PWR control consider the isolated problem of the reactor core thermal power (and possibly axial power distribution) control; some also address the heat exchanger and the steam turbine (Park and Cho, 1992;Naimi et al, 2022b).…”

“…The constraints of the minimum and maximum overlap between the CRGs are enforced. • Naimi et al (2022b) presented a controller based on a recurrent ANN model combining feedback linearisation and MPC. The process, originally represented with a 1P PWR reactor core model without considering the 135 Xe dynamics, is identified to an recurrent ANN, trained using a quasi-Newton optimisation algorithm.…”

“…A wide variety of control methods is used, including linear methods like proportional-integral-derivative (PID) control (Mousakazemi, 2019), linear-quadratic-Gaussian control (Edwards et al, 1990;Ben-Abdennour et al, 1992;Li et al, 2014a), minimum-variance control (Na et al, 1998b), model predictive control (receding horizon control) (Na, 2001;Wang et al, 2017), robust control (Torabi et al, 2011), non-linear methods like global linearising control (Park and Cho, 1992), feedback linearization control (Zaidabadi nejad and Ansarifar, 2018;Naimi et al, 2022b), non-linear dynamic inversion (Yadav et al, 2018), non-linear model predictive control (Eliasi et al, 2011;Liu et al, 2016;Ejigu and Liu, 2022), sliding mode control (variable structure control) (Ansarifar and Saadatzi, 2015;Wang et al, 2019;Elsisi and Abdelfattah, 2020;Hui and Yuan, 2022b), control based on artificial neural networks (Na et al, 1998a;Lin and Shen, 2000;Khajavi et al, 2002;Boroushaki et al, 2004;Arab-Alibeik and Setayeshi, 2005;Khorramabadi et al, 2008;Naimi et al, 2022b;Hui and Yuan, 2022d) and/or fuzzy logic (Na et al, 1998a;Khorramabadi et al, 2008;Liu et al, 2016;Zeng et al, 2021), deep learning and artificial intelligence (Lee et al, 2020;Ejigu and Liu, 2022;Park et al, 2022), fault-tolerant control (Hui and Yuan, 2022d), control based on models with distribut...…”

Continuous-Time Nonlinear Model Predictive Tracking Control with Input Constraints Using Feedback Linearization

“…Furthermore, a disturbance observer was designed to estimate wind disturbances [17]. Naimi et al applied the combination to a pressurized water reactor, where a dynamic neural network model of the reactor was identified by using the quasi-Newton algorithm, and a discrete-time MPC cascaded with an input-output FL was applied, based on the identified neural network model [18].…”

Input-Output Feedback Linearization Control for a PWR Nuclear Power Plant

“…Conversely, dynamic neural networks (DNNs) have proved to be more effective than static NNs due to their structures [19]. The simplicity of their architectures makes them suitable together with nonlinear control techniques [21]- [24]. This paper proposes a DNN-based FBL approach for the effective control of an entire PWR.…”