A Reinforcement-Learning-Based Fuzzy Compensator for a Microcontroller-Based Frequency Synthesizer/Vector Voltmeter

This paper presents the development of an intelligent fuzzy-based compensation scheme, utilizing reinforcement learning methodology, which can be used to compensate the reading of an unknown voltage, in vector form. This compensator is implemented online, in real time, with an indigenously developed microcontroller-based scheme that can be used both as a frequency synthesizer and as a vector voltmeter. This frequency synthesizer/vector voltmeter is developed using a direct digital synthesis method for the frequency synthesizer and a synchronous detection technique for the vector voltmeter. The developed fuzzy compensator has been tested in both offline and online modes, and in both cases, it has been found to significantly improve the accuracy of the measurement compared to those obtained with an uncompensated vector voltmeter. It has been shown that the final compensated measurements are in close agreement with the true unknown voltages under measurement.