Adaptive Optimal Output Regulation Based on Adaptive Dynamic Programming and Output-Feedback Hybrid Iteration: Theory and Applications Across Diverse Time Scales From PMDC Motor Drives to IBR-Based Microgrids

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Abstract
This article investigates the adaptive optimal output-feedback control problem for discrete-time multiagent linear systems. An adaptive dynamic programming (ADP) algorithm, hybrid iteration (HI), is proposed to solve the adaptive optimal output-feedback control problem. The primary contribution of this work is the advancement of an approach to addressing adaptive optimal output regulation problems for multiagent systems via output-feedback control. Unlike existing ADP-based strategies, such as policy iteration (PI) and value iteration (VI), the proposed output-feedback HI strategy learns the optimal output-feedback control policy over two phases. In the first phase, a data-driven scheme is deployed to learn an admissible output-feedback control policy based on online data. In the second phase, given the admissible control policy, the optimal output-feedback control policy is learned via PI with an accelerated convergence rate. Illustrative examples demonstrating the effectiveness of the proposed output-feedback HI approach to controlling systems with slow and fast dynamics, including long and short time scales (or time constants), respectively, are provided and studied. In this regard, experimental results from rapid control prototyping of a permanent-magnet DC motor drive utilizing a DC–DC buck converter (as an example of a system with slow dynamics, or equivalently, with long time scales) are presented. Additionally, testing results from power hardware-in-the-loop simulations of a modern microgrid utilizing inverter-based resources (as an example of a system with fast dynamics, or equivalently, with short time scales) reveal the efficacy and practicality of the proposed ADP-based adaptive optimal output regulation employing HI.

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