Bridging the Control Theory, Optimization, and Learning: Application in Robotics
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machine Design and Theory".
Deadline for manuscript submissions: 31 October 2024 | Viewed by 291
Special Issue Editor
Interests: robotics; control theory; nonlinear dynamics; human-robot interaction; advanced manufacturing; synchronization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Control practitioners are trying to understand how optimization and reinforcement learning enable the application of control theory to achieve better control performance in the presence of uncertainty and high dimensionality. Reinforcement learning is trying to find a path to improve the control field. In light of this context, this Special Issue focuses on building a connection between control theory, optimization, and reinforcement learning, before finally applying it to robotics. Papers in the fields of Lyapunov function and value function, stability and optimality, and embedding optimization into control and its application in robotics are particularly welcome, though papers in other fields will also be accepted.
Dr. Bin Wei
Guest Editor
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- Lyapunov function
- value function
- robotics
- optimization
- nonlinear control
- MPC
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