An Intelligent Optimized Gain Scheduling for Reaktor Bolshoy Moshchnosti Kanalny's Nuclear Reactor System

Nuclear energy-based electricity generation has increased in recent times due to the vast expanse of carbon-free electricity demand. However, reaktor bolshoy moshchnosti kanalny's (RBMK)-based power generation method is mainly utilized despite complex temperature and concentration control problems. Therefore, in this paper, the novel adaptive heap coupled African buffalo optimization (AHABO)-based gain scheduling proportional–integral–derivative control technique is developed to control the parameters of the linear parameter-varying (LPV)-based RBMK nuclear reactor system. The major role of this work is to control the gain parameters of the introduced controller without errors and enhance the accuracy of the proposed system optimally. Moreover, the proposed controller regulates the state feedback gain using linear Lagrange polynomial (LLP) interpolation technique. Furthermore, the stability of the developed controller performance is synthesized by optimal semidefinite programming along with the approach. The implementation of this research is done on the MATLAB/Simulink platform. The reduction of error percentage and enhancement of proposed system accuracy can be achieved by the proposed AHABO method. Besides, the comparative assessment of the proposed model with other existing models is done using different key metrics. Hence, this proved the effectiveness of the proposed method in RBMK nuclear reactor control. The computational intelligence algorithm provides better tuning results with the AHABO controller during rapid dynamic conditions. So, in future design, the deep neural model with the AHABO algorithm will provide better results.