Sitong Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Sitong Liu has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Masoud Davari
Department of Electrical and Computer Engineering, Georgia Southern University (Statesboro Campus), Statesboro, GA, USA
1 shared publication - Omar Qasem
Department of Mechanical and Civil Engineering, College of Engineering and Science, Florida Institute of Technology, Melbourne, FL, USA
1 shared publication - Tianyou Chai
Northeastern University
1 shared publication - Weinan Gao
Northeastern University
1 shared publication
Latest Publications
- 1 October 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Industrial Electronics
- Vol. 73 (10) , 15154-15168
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
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.
- 1 October 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Industrial Electronics
- Vol. 73 (10) , 15154-15168
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
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.