This article presents a dynamic modeling and control strategy for a non-ideal buck DC–DC (direct current) converter using a PID neural controller. Unlike conventional approaches that rely on fixed-gain PID (Proportional Integral Derivative) controllers, the proposed method dynamically updates the controller’s gain constants to enhance robustness against parametric variations caused by tolerances, wear, or other practical discrepancies. To ensure the neural network’s weight convergence, a Lyapunov-based algorithm is employed, enabling optimal weight adjustments in conjunction with the PID control strategy. The study validates the ANN-based (Artificial Neuronal Network) PID controller under diverse dynamic conditions (input voltage variations, disturbances in voltage sensors, etc.) through numerical simulations, incorporating theoretical derivations and circuit dynamics modeling. The main contribution of this work lies in demonstrating the convergence of the system under the proposed control law, substantiated by Lyapunov stability analysis and comparative simulations against traditional methods in the literature.