Topic Editors
Learning-Enabled Feedback Control of Complex Engineering and Biological Systems
Topic Information
Dear Colleagues,
Recent advances in machine learning, artificial intelligence, and computational modeling are transforming the design and implementation of feedback control systems. By integrating data-driven learning methods with established control-theoretic principles, researchers are developing intelligent controllers capable of adapting to uncertainty, improving performance, and enabling autonomous decision-making in increasingly complex environments.
Learning-enabled feedback control has emerged as a powerful framework for the regulation of complex dynamical systems across a wide range of domains. In engineering, these approaches are being applied to autonomous vehicles, robotics, aerospace systems, manufacturing processes, energy systems, and cyber–physical infrastructures. At the same time, advances in bioengineering and computational medicine are enabling closed-loop regulation of biological systems, including physiological processes, cellular behavior, bioelectronic devices, and precision therapeutic interventions.
This Topic aims to bring together researchers working on the development and application of learning-enabled feedback control methodologies for complex engineering and biological systems. Contributions addressing theoretical foundations, computational frameworks, algorithmic developments, experimental validation, and real-world implementations are welcome. Particular emphasis is placed on approaches that combine machine learning and artificial intelligence with feedback control, systems theory, optimization, and dynamical systems analysis.
Topics of interest include, but are not limited to, reinforcement learning for control, adaptive and intelligent control, neural-network-based control, data-driven and model-free control, safe and robust learning-based control, autonomous systems, multi-agent systems, cyber–physical systems, digital twins, bioelectronic medicine, physiological regulation, precision medicine, cellular control, and human-in-the-loop systems. Studies investigating stability, robustness, explainability, and trustworthiness of learning-enabled control systems are also encouraged.
Research articles, review papers, and case studies that address these challenges from theoretical, computational, experimental, or application-oriented perspectives are all welcome.
Dr. Mohammad Jafari
Dr. Marcella Gomez
Topic Editors
Keywords
- learning-enabled control
- artificial intelligence for control
- machine learning
- reinforcement learning
- data-driven control
- adaptive and robust control
- autonomous systems
- robotics
- cyber-physical systems
- multi-agent systems
- digital twins
- nonlinear dynamical systems
- biomedical control systems
- bioelectronics and precision medicine
- human-in-the-loop systems
Participating Journals
| Journal Name | Impact Factor | CiteScore | Launched Year | First Decision (median) | APC | |
|---|---|---|---|---|---|---|
Applied Sciences
|
2.9 | 6.1 | 2011 | 15 Days | CHF 2400 | Submit |
Automation
|
2.9 | 4.5 | 2020 | 24.8 Days | CHF 1200 | Submit |
Bioengineering
|
4.4 | 7.5 | 2014 | 16.9 Days | CHF 2700 | Submit |
Electronics
|
2.9 | 7.0 | 2012 | 14.8 Days | CHF 2400 | Submit |
Mathematical and Computational Applications
|
2.2 | 2.8 | 1996 | 23.3 Days | CHF 1600 | Submit |
Systems
|
3.8 | 5.4 | 2013 | 19.8 Days | CHF 2400 | Submit |
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