This work proposes adaptive control-based design strategies to control a pressurized water reactor (PWR) nuclear power plant (NPP). An L ₁ -adaptive-based state-feedback control technique is proposed using the linear quadratic Gaussian control and projection-based adaptation laws. The control scheme possesses good robustness capabilities in handling disturbances and uncertainties. A robust L ₁ -adaptive control technique is also proposed by combining the L ₁ -adaptive control with the loop transfer recovery (LTR) technology. The framework hence gives the strengthened robust set-point tracking performance given the matched and unmatched uncertainties and disturbances. The NPP model employed in this article is defined by five inputs, five outputs, and 38 state variables. A linear model for controller design is obtained by linearizing the nonlinear NPP model at operating conditions. Various simulations are carried out on subsystems of the NPP to verify the effectiveness of the proposed scheme. Numerical and statistical measures are computed for quantitative analysis of the controllers' performance. Several classical control design techniques are also implemented, and their performance is compared with the proposed adaptive control techniques.

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.

This paper introduces a methodology of designing subspace predictive reactor core power control during loadfollowing mode of operation. The central idea is to implement predictive control law directly from the pre-processed input-output dataset without using any explicit process model. The controller is designed to include design constraints, feed-forward control, and integral control action effectively. Further, time variations in the process are taken into account by recursively updating control parameters with the arrival of new dataset. The efficacy of the proposed technique is demonstrated for tracking various load rejection as well as load-following transients for a pressurized water nuclear reactor. A detailed parameter sensitivity analysis is carried out to analyze the controller performance.

Signals from sensors placed at different locations are used for control and protection purposes in a nuclear reactor. It also requires in-service Fault Detection and Isolation (FDI) for its safe operation. The sensor signals are generally a superimposition of low-frequency components representative of true values of the variables being monitored; occasional high-frequency periodical oscillations due to disturbances and faults; and sensor faults. Techniques like Principal Component Analysis (PCA) can be used for FDI, however, it would be more meaningful if the FDI technique can also help for predictive maintenance of reactor internals through vibration spectra. To address these issues, a multi-scale PCA, integrating wavelet transform with PCA and aiming to reduce the modeling cost by using only a fewer scales that contribute to the monitoring has been proposed in this paper for online FDI of Advanced Heavy Water Reactor (AHWR). A new mathematical formulation of the Generalized Likelihood Ratio Test for its use with wavelet approximation coefficients has also been proposed for better sensor-FDI outcomes. The proposed approach detects and isolates sensor faults and process faults using the signals from neutron detectors. Efficacy of the proposed technique is established on the simulated ex-core ion chamber data of AHWR considering different scenarios that involve localized frequency contents representative of process faults, slowly developing (incipient) sensor faults, and the simultaneous presence of two or more of these scenarios. Simulation results validate the effectiveness of the proposed scheme for online FDI in the reactor.