Reinforcement Learning-Based Control of a Power Electronic Converter

Abstract

This article presents a modern, data-driven, reinforcement learning-based (RL-based), discrete-time control methodology for power electronic converters. Additionally, the key advantages and disadvantages of this novel control method in comparison to classical frequency-domain-derived PID control are examined. One key advantage of this technique is that it obviates the need to derive an accurate system/plant model by utilizing measured data to iteratively solve for an optimal control solution. This optimization algorithm stems from the linear quadratic regulator (LQR) and involves the iterative solution of an algebraic Riccati equation (ARE). Simulation results implemented on a buck converter are provided to verify the effectiveness and examine the limitations of the proposed control strategy. The implementation of a classical Type-III compensator was also simulated to serve as a performance comparison to the proposed controller.

Fuzzy-Augmented Model Reference Adaptive PID Control Law Design for Robust Voltage Regulation in DC–DC Buck Converters

“…To deliver and regulate the desired value of average voltage at the output, the duty-cycle ratio, d, of the switching period of Q 1 's gating signal is suitably adjusted [41]. The relationship between the input voltage v in and the output voltage v o is expressed in (1).…”

“…In this section, an LQR-driven PID controller is developed for the DC-DC buck converter system [41,42]. The LQR is a popular optimal control technique used for regulating the linear systems described by state space equations [1]. It is a state feedback control law, which means that the control input is determined as a linear function of the state variables.…”

“…A buck converter is a DC-DC power converter that steps down the voltage from a higher level to a lower level [1]. It is widely used for efficient power conversion and voltage regulation in electronic systems owing to their compact size, improved conversion efficiency, minimal drop-out voltage, affordable manufacturing expenses, and substantial output power supply [2].…”