Advanced heavy water reactor control with the aid of adaptive second-order sliding mode controller

Purpose Advanced heavy water reactor (AHWR) is a pressure tube type of heavy water reactor. It eliminates high-pressure heavy water coolant resulting in a reduction of heavy water leakage losses and eliminating heavy water recovery system. It recovers the heat generated in the moderator for feed water heating. However, it requires a satisfactory technological response to develop an effective controller that attains the challenges of the very high-level safety system. Hence, they require application-specific improvement for better controlling performance. Design/methodology/approach The purpose of this study intends to propose a system for controlling state vectors v1 and v2and in AHWR using Grey Wolf second-order sliding mode control (GW-SoSMC) technique. The main aim of the paper is to minimize the errors between the predicted and desired azimuthal angles of the system. With this proposed method, it is possible to mitigate both the chattering phenomenon and controlling performance of AHWR system. It implements a SoSMC controller based on GWO algorithm for the purpose of controlling the state vectors in the AHWR system. It aims to accomplish a controller for improving the performance of the AHWR system. Findings Through the performance analysis, the efficiency of the proposed GW-SoSMC technique was verified by comparing it with various conventional algorithms, such as GW-SMC, FF-SoSMC, ABC-SoSMC, GS-SoSMC and GA-SoSMC. From the analysis, it was obtained that the implemented GW-SoSMC technique was 65.3 per cent superior to GW-SMC, 65.32 per cent superior to both FF-SoSMC and 65 per cent superior to ABC-SoSMC, 65.8 per cent superior to the GS-SoSMC and 58 per cent superior to the GA-SoSMC methods. Thus, the effectiveness of the proposed method in controlling the state vectors in AHWR was obtained. Originality/value This paper presents a technique for controlling the state vectors in the AHWR system using GWO algorithm. This is the first work that uses GWO-based optimization for controlling state vectors in the AHWR system.