Intelligent Mechatronic Systems: Advances in Sensing, Control, and Integration for Next-Generation Automation

Mechatronic systems have evolved from traditional electromechanical devices into complex, intelligent, and interconnected systems that form the foundation of modern automation and robotics. The convergence of advanced sensing technologies, real-time control algorithms, and artificial intelligence has enabled higher levels of autonomy, adaptability, and performance in diverse applications such as manufacturing, transportation, healthcare, and energy systems. At the same time, the increasing system complexity and coupling among mechanical, electrical, and computational domains pose new challenges for modeling, control, and integration. To address these challenges, researchers are exploring intelligent, data-driven, and hybrid approaches that combine physical modeling with machine learning, aiming to achieve robust, efficient, and adaptive mechatronic systems capable of operating reliably in uncertain and dynamic environments.

The goal of this Research Topic is to advance the understanding, design, and implementation of intelligent mechatronic systems capable of adaptive, robust, and high-performance operation in complex environments. Traditional mechatronic architectures often struggle with nonlinearities, parameter uncertainties, and multi-domain interactions, which limit their efficiency and scalability. To overcome these challenges, this Topic seeks contributions that integrate model-based and data-driven methods to enhance system autonomy, learning capability, and real-time decision-making.

Recent advances in artificial intelligence, digital twins, and edge computing have opened new pathways for intelligent sensing, predictive control, and fault-tolerant operation in mechatronic systems. By combining these emerging technologies with robust and optimal control frameworks, researchers can develop next-generation systems that self-optimize under changing conditions. This Research Topic aims to gather interdisciplinary studies and practical innovations that promote the transition toward intelligent, resilient, and sustainable mechatronic systems for future industrial and societal applications.

This Research Topic welcomes original research and review articles that explore recent advances in intelligent mechatronic systems, with a focus on sensing, control, and system integration. Topics of interest include, but are not limited to: intelligent sensors and actuators; nonlinear, adaptive, and robust control strategies; AI- and learning-based control; digital twin and cyber-physical systems; fault detection and health monitoring; and human–machine collaboration in mechatronic applications. Contributions addressing theoretical developments, simulation analysis, and experimental validation are all encouraged. Manuscripts that bridge the gap between control theory, system design, and practical implementation—particularly those demonstrating industrial relevance or cross-domain innovation—are highly welcome. Interdisciplinary works connecting mechanical engineering, electrical engineering, computer science, and applied AI are especially encouraged.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review

Keywords: Intelligent mechatronic systems, Advanced sensing and actuation, Nonlinear and robust control, AI-assisted control and optimization, Digital twin and cyber-physical systems, Fault diagnosis and adaptive learning, Human–machine collaboration

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.