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Machines
Special Issues
New Adaptive and Learning Control System Design for Robotic Manipulators
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New Adaptive and Learning Control System Design for Robotic Manipulators

A special issue of Machines (ISSN 2075-1702).

Deadline for manuscript submissions: closed (30 April 2018) | Viewed by 5350

Special Issue Editors

Prof. Dr. Dan Zhang

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Guest Editor
Department of Mechanical Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada
Interests: robotics and mechatronics; high-performance parallel robotic machine development; sustainable/green manufacturing systems; micro/nanomanipulation and MEMS devices (sensors); micro mobile robots and control of multi-robot cooperation; intelligent servo control system for the MEMS-based high-performance micro-robot; web-based remote manipulation; rehabilitation robot and rescue robot
Special Issues, Collections and Topics in MDPI journals
Dr. Bin Wei

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Guest Editor
Department of Mechanical Engineering, Lassonde School of Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
Interests: robotics and mechatronics; adaptive control and learning control; advanced manufacturing and automaton; rehabilitation robots and rescue robots; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Adaptive control for robotic manipulators have been developed in the last decade, and learning control design is still in its early development stages. Control system design is a critical step for robotic manipulator systems and their later development and applications. This Special Issue aims to bring researchers together to present the recent and latest advances and technologies in the field of adaptive and learning control system design for robotic manipulators in order to further summarize and improve the methodologies on this topic. Suitable topics include, but are not limited to, the following:

  • Adaptive control design for robotics
  • Model reference adaptive control design
  • Learning control design for robotics
  • Intelligent control system development for robotics
  • Advanced control system design for manufacturing
  • This call invites both theoretical and empirical studies on this topic.

Prof. Dr. Dan Zhang
Dr. Bin  Wei
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Adaptive control
  • Learning control
  • Robotic manipulators
  • Stability
  • Intelligent control
  • Mechatronics.

Published Papers (1 paper)

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Research

18 pages, 1566 KiB  
Article
Adaptive Synchronization for Heterogeneous Multi-Agent Systems with Switching Topologies
by Muhammad Ridho Rosa
Machines 2018, 6(1), 7; https://doi.org/10.3390/machines6010007 - 22 Feb 2018
Cited by 9 | Viewed by 3861
Abstract
This work provides a multi-agent extension of output-feedback model reference adaptive control (MRAC), designed to synchronize a network of heterogeneous uncertain agents. The implementation of this scheme is based on multi-agent matching conditions. The practical advantage of the proposed MRAC is the possibility [...] Read more.
This work provides a multi-agent extension of output-feedback model reference adaptive control (MRAC), designed to synchronize a network of heterogeneous uncertain agents. The implementation of this scheme is based on multi-agent matching conditions. The practical advantage of the proposed MRAC is the possibility of handling the case of the unknown dynamics of the agents only by using the output and the control input of its neighbors. In addition, it is reasonable to consider the case when the communication topology is time-varying. In this work, the time-varying communication leads to a switching control structure that depends on the number of the predecessor of the agents. By using the switching control structure to handle the time-varying topologies, we show that synchronization can be achieved. The multi-agent adaptive switching controller is first analyzed, and numerical simulations based on formation control of simplifier quadcopter dynamics are provided. Full article
(This article belongs to the Special Issue New Adaptive and Learning Control System Design for Robotic Manipulators)
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