Robust Control and Dynamics Modeling Methodologies for Intelligent Industrial Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: closed (10 July 2022) | Viewed by 2103
Special Issue Editors
Interests: artificial Intelligence; prognostics and health management; AI-PHM; diagnostics; fault detection; degradation; reliability
Special Issues, Collections and Topics in MDPI journals
Interests: nonlinear system; nonlinear control; nonlinear observers; industrial applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Industry 4.0 is the current trend for automation and data exchange in manufacturing technologies and includes cyber-physical systems, the Internet of Things, cloud computing, and cognitive computing. It is commonly referred to as the fourth industrial revolution, and industrial systems, including smart factories, transportation systems, robotic systems, and power systems, are becoming more complex as they are generally composed of a number of interconnected systems and/or have highly nonlinear dynamics. Furthermore, various disturbances such as external disturbance, modeling uncertainties, and parameter uncertainties may cause the degradation of the system and control performance. Thus, it is challenging to develop effective techniques in control and modeling for intelligent industrial systems. This Special Issue aims to collect original research and review articles that report on results focused on robust control and dynamics modeling methodologies for intelligent industrial systems. Potential topics include, but are not limited to, the following:
- Novel modeling and validation techniques for intelligent industrial systems
- Advanced modeling techniques for evolution and degradation of dynamic systems
- Prognosis and health management for complex dynamic systems
- Estimation for industrial systems
- Advanced control for industrial systems
- Real-time implementation for modeling and control approaches
- Application and integration of various techniques such as linear and nonlinear system approaches, hybrid system and switching system approaches, networked system approaches, game theory, and knowledge-based methods
Prof. Ki-Yong Oh
Prof. Wonhee Kim
Guest Editors
Manuscript Submission Information
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Keywords
- robust control
- dynamics modeling
- intelligent industrial systems
