Data-Driven and Adaptive Learning Control for Heterogeneous Multi-Agent Systems

Dear Colleagues,

The increasing deployment of autonomous and networked systems in smart manufacturing, intelligent transportation, distributed energy resources, and cooperative robotics has intensified the demand for advanced control strategies that can operate effectively under uncertainty, heterogeneity, and limited model knowledge. In such environments, agents often exhibit diverse dynamics, suffer from unknown disturbances or faults, and rely on noisy or incomplete measurements—challenges that traditional model-based control methods struggle to address. To overcome these limitations, data-driven and adaptive learning control has emerged as a transformative approach, enabling multi-agent systems to learn effective coordination policies directly from operational data without requiring explicit system models. By integrating tools from behavioral systems theory, reinforcement learning, online optimization, and adaptive estimation, these methods offer a pathway toward resilient, scalable, and self-tuning collective behavior in complex real-world settings.

This Special Issue, “Data-Driven and Adaptive Learning Control for Heterogeneous Multi-Agent Systems”, aims to gather recent advances in the theory, algorithms, and applications of learning-enabled control for heterogeneous networked systems operating under uncertainty.

Topics of interest include, but are not limited to, the following:

• Data-driven consensus, formation, and containment control for heterogeneous MASs;
• Adaptive learning protocols with unknown leader/follower dynamics;
• Control design based on Willems’ Fundamental Lemma and subspace identification;
• Reinforcement learning and adaptive dynamic programming for multi-agent coordination;
• Robustness to measurement noise, communication delays, and external disturbances;
• Fault-tolerant and resilient learning-based control under actuator/sensor failures;
• Event-triggered or resource-aware learning strategies for networked agents;
• Applications in microgrids, autonomous vehicle fleets, UAV swarms, industrial IoT, and robotic teams.

We invite researchers from control theory, robotics, artificial intelligence, and applied mathematics to contribute original research that bridges theoretical innovation with practical relevance in the era of intelligent, data-enabled multi-agent coordination.

Thank you for your consideration, and we look forward to receiving your contributions.

Sincerely,

Dr. Shicheng Huo
Dr. Feng Li
Guest Editors

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• multi-agent systems
• heterogeneous multi-agent systems
• networked control systems
• distributed intelligent microgrids
• reinforcement learning control
• data-driven control
• robust and fault-tolerant control