Intelligent Control and Robotics
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: closed (10 November 2021) | Viewed by 5247
Special Issue Editors
Interests: control; robotics; autonomous systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Intelligent control considers the use of a wide variety of artificial intelligence techniques, such as neural networks, fuzzy logic, and machine learning, to name a few. The complexity of modern engineering plants is growing in order to fulfil strict requirements and performance specifications. Therefore, obtaining accurate and suitable mathematical models is one of the most challenging tasks in control design. The above problem has led to considering intelligent control tools in order to provide accurate identification methods, as well as flexible and robust controllers.
Robots are designed to be able to solve new and different tasks in complex and cluttered environments. Thus, flexible structures are of great interest during the control design of an uncertain robotic system. In recent decades, various approaches based on intelligent control have been considered for some robotic applications, such as navigation, trajectory tracking, rehabilitation, force control, and cooperative or coordinated motion in multirobotic systems.
Therefore, the purpose of this Special Issue is to present the latest developments and outstanding applications in intelligent control, such as neural networks, fuzzy logics, and machine learning; and in robotics, such as navigation, force control, rehabilitation, and coordination and consensus of multirobotic systems. Researchers in these fields are invited to contribute with their original and unpublished works. Both research and review papers are welcome.
Dr. Aldo Jonathan Muñoz-VázquezDr. Juan Diego Sánchez Torres
Guest Editors
Manuscript Submission Information
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Keywords
- Fuzzy logic
- Neural networks
- Machine learning
- Data-driven control and identification
- Discrete-time adaptive controllers
- Robot navigation
- Force robot control
- Rehabilitation robots
- Multirobotic systems
- Consensus
- Unmanned aerial, ground, and underwater vehicles
