Intelligent Control of Electromechanical Complex System
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: 20 December 2024 | Viewed by 674
Special Issue Editors
Interests: precision machinery; precision motion control; learning control; system identification
Interests: adaptive parameter estimation; system identification; intelligent control; adaptive control and application
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Traditional control methods often struggle with the nonlinearity, disturbances, and unmodeled dynamics present in complex electromechanical systems, leading to laborious and complex control strategies. With the advent of advanced computational techniques, machine learning algorithms, and abundant measurement data, control strategies for complex electromechanical systems, used in robotics, aerospace, transportation, and wafer scanners, are undergoing a transformative shift from traditional model-based methods to learning-driven control, with the aim of enhancing performance, reliability, and adaptability.
This Special Issue is dedicated to exploring state-of-the-art advancements and innovative approaches in this critical field, both theoretical and application-oriented. It aims to provide a comprehensive platform for researchers, engineers, and practitioners to present their latest findings, share insights, and foster discussions on the intelligent control of complex electromechanical systems. We encourage the submission of theoretical and experimental studies that would promote further research activities in this area. The key topics covered in this issue include, but are not limited to, the following:
- Theoretical foundations for intelligent control;
- Intelligent modeling and identification in electromechanical systems;
- Intelligentization in feedforward and feedback control;
- Intelligent optimization in precision/ultra-precision motion systems;
- Iterative/deep/adaptive learning control for complex systems;
- Intelligent trajectory planning for complex motion systems;
- Sensor fusion and data analytics in electromechanical systems;
- Human–machine interactions in electromechanical systems;
- Application of machine learning and artificial intelligence in electromechanical systems.
Prof. Dr. Yang Liu
Prof. Dr. Jing Na
Dr. Siyuan Zhan
Guest Editors
Manuscript Submission Information
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Keywords
- learning control
- intelligent optimization
- complex electromechanical system
- modeling and identification
- motion control
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