Abstract

There is significant interest in the development of intelligent control systems. The design of efficient control strategies requires the integration of multiple artificial intelligence principles. This study presents the development and hardware-in-the-loop (HIL) implementation of proportional-integral-derivative (PID) and artificial neural network (ANN) controllers for synchronous generator (SG) excitation systems, using Xilinx blocks in Simulink for field-programmable gate array (FPGA) deployment. The controllers were evaluated in real time through co-simulation with an FPGA-based SG simulator. The PID controller served as a simple and reliable benchmark for voltage regulation. In contrast, the ANN controller explored the application of machine learning for adaptive control, offering enhanced flexibility under system variations. Performance was assessed in terms of stability, latency, and voltage regulation using both simulation and co-simulation approaches. Both controllers demonstrated effective operation, with results visualized through voltage–time graphs under varying load conditions. Comparative analysis indicated that, although the ANN controller exhibited a slightly slower response, it achieved higher accuracy, reduced deviation, and lower steady-state error compared to the PID controller. These findings highlight the effectiveness of FPGA-based HIL testing in advancing modern power system control techniques.

Keywords

Intelligent control systems, Hardware-in-the-loop (HIL), Proportional–integral–derivative (PID) controller, Artificial neural network (ANN), Field-programmable gate array (FPGA).

Cite this article

Jasim HN, Abdalla KK. FPGA-based implementation of PID and artificial neural network controllers for the excitation system of synchronous generators. International Journal of Advanced Technology and Engineering Exploration. 2026;13(135):280-302. DOI : 10.19101/IJATEE.2025.121220907

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