A pressurized water reactor (PWR) is a system of several integrated components such as the core, steam generator, hot leg, cold leg, and plenums. The subsystems consist of critical parameters and malfunctions that cause potential accidents. Therefore, a PWR requires a control system for safe and stable operation over its lifetime. In this study, the state-space model of the PWR core is established and validated with published work. Then, a beetle antenna search (BAS) algorithm–optimized radial basis function (RBF) neural network proportional-integral-derivative (PID) control (BAS-RBF-PID) strategy is proposed to regulate the core power. The BAS-RBF-PID control approach computes the control input to optimize the PWR core output power to track the reference command. The integral absolute error and integral time absolute error criterion functions are used to measure the control performance. The sensitivity of the control input on the PWR output is examined through the Jacobian, and the stability is analyzed by using the Lyapunov approach and Nichols chart. The simulation results verified that the PWR core output power chased the reference command smoothly as compared with the BAS-PID and PID strategies with good performance. This confirms that the control signal optimizes the core power effectively. This study gives the benefit to apply the BAS-RBF-PID algorithm in other nuclear engineering fields for control purposes.