Advances in Mathematical Model and Machine Learning-Based Control for Small-Scale Robots
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: 31 July 2024 | Viewed by 114
Special Issue Editor
Interests: microrobotics; control; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Small-scale robotics is attracting increasing attention in both fundamental research and applications. The tiny size of such robots enables them to work in many scenarios beyond the capabilities of traditional robots, e.g., inspection in narrow regions, targeted delivery in the human body, and so on. Through mathematical modeling and control design, the working efficiency and precision of these robots are enhanced. To further elevate the levels of intelligence for small-scale robots, i.e., to be more independent of humans and able to learn advanced behaviors, advanced control methods and machine learning are required to support high-level autonomous task execution. However, the complexities of fluid and mechanism modeling at small scales lead to difficulty in controlling via rigorous mathematical methods. Thus, the design paradigm of controllers prefers model-free and direct learning. Driven by these emerging demands, novel solutions including robust control, disturbance observers, and machine learning are required to impel the level of autonomy of small-scale robots, thus enhancing their intelligence.
The aim of this Special Issue is to provide a forum for researchers to present their original contributions describing their experience and approaches towards a wide range of control and machine learning techniques applied to small-scale robotics. Research and review papers that showcase the latest developments in theoretical analysis, simulations, and experiments are welcome.
Dr. Lidong Yang
Guest Editor
Manuscript Submission Information
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Keywords
- small-scale robots
- microrobots
- control
- machine learning
- deep learning
- model-free control
