Adaptive learning observer based fixed-time stable controller for load following of a Pressurized Water Reactor

Abstract

Enhancing the load following capacity of Nuclear Power Plants (NPPs) remains a challenge. This paper focuses on designing a fixed-time stable controller for load following operation of a Pressurized Water Reactor (PWR) with compound disturbances. To compensate the limitation of current measuring techniques, an adaptive learning observer is firstly proposed to estimate the hard-to-measure states like Xenon concentration, Iodine concentration, average reactor fuel temperature and the compound disturbances of the PWR system. Based on these important information, in order to ensure that the output power of PWR can rapidly and accurately follow the prescribed idea power, a fixed-time stable controller is presented. Sequentially, Lyapunov method is employed to verify that the control system can be stable within a fixed-time, which upper bound can be accurately calculated by some parameters. Finally, simulation results are provided to illustrate the effectiveness of the designed controller in the load following operation and the performance of the adaptive learning observer.