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Robotics
Special Issues
Adaptive and Learning Control for Complex Dynamical Systems and Robotics
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Adaptive and Learning Control for Complex Dynamical Systems and Robotics

A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "AI in Robotics".

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 14250

Special Issue Editors

Dr. Bin Wei

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Algoma University, 1520 Queen St E, Sault Ste, Marie, ON P6A 2G4, Canada
Interests: robotics; control theory; dynamical systems; human–robot interaction; AI
Special Issues, Collections and Topics in MDPI journals
Dr. Tansel Yucelen

E-Mail Website
Guest Editor
Department of Mechanical Engineering at the University of South Florida, Tampa, FL, USA
Interests: dynamical systems and control; adaptive control and learning algorithms; analysis and synthesis of control algorithms for complex dynamical systems; robotics

Special Issue Information

Dear Colleagues,

Adaptive control for robotics has been developed in the last decade, and learning control design is still in its early stages. The development of control system design is a critical step in the development of complex dynamical systems and robotics. This Special Issue aims to bring researchers together to present the latest advances and technologies in the field of adaptive and learning control for complex dynamical systems and robotics in order to further summarise and improve the methodologies in this field. Suitable topics include but are not limited to the following:

  • Adaptive control for robotics;
  • Learning control for robotics;
  • Intelligent control system for human–robot interactions;
  • Nonlinear control of complex dynamical systems;
  • Control stability.

This call invites both theoretical and empirical studies on these topics.

Dr. Bin Wei
Dr. Tansel Yucelen
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. Robotics 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 1800 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
  • Robotics
  • Human–robot interactions
  • Complex dynamical systems

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

13 pages, 3104 KiB  
Article
Kinematic-Model-Free Orientation Control for Robot Manipulation Using Locally Weighted Dual Quaternions
by Ahmad AlAttar and Petar Kormushev
Robotics 2020, 9(4), 76; https://doi.org/10.3390/robotics9040076 - 24 Sep 2020
Cited by 16 | Viewed by 4286
Abstract
Conventional control of robotic manipulators requires prior knowledge of their kinematic structure. Model-learning controllers have the advantage of being able to control robots without requiring a complete kinematic model and work well in less structured environments. Our recently proposed Encoderless controller has shown [...] Read more.
Conventional control of robotic manipulators requires prior knowledge of their kinematic structure. Model-learning controllers have the advantage of being able to control robots without requiring a complete kinematic model and work well in less structured environments. Our recently proposed Encoderless controller has shown promising ability to control a manipulator without requiring any prior kinematic model whatsoever. However, this controller is only limited to position control, leaving orientation control unsolved. The research presented in this paper extends the state-of-the-art kinematic-model-free controller to handle orientation control to manipulate a robotic arm without requiring any prior model of the robot or any joint angle information during control. This paper presents a novel method to simultaneously control the position and orientation of a robot’s end effector using locally weighted dual quaternions. The proposed novel controller is also scaled up to control three-degrees-of-freedom robots. Full article
(This article belongs to the Special Issue Adaptive and Learning Control for Complex Dynamical Systems and Robotics)
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17 pages, 2311 KiB  
Article
Adaptive Kinematic Modelling for Multiobjective Control of a Redundant Surgical Robotic Tool
by Francesco Cursi, George P. Mylonas and Petar Kormushev
Robotics 2020, 9(3), 68; https://doi.org/10.3390/robotics9030068 - 31 Aug 2020
Cited by 11 | Viewed by 4973
Abstract
Accurate kinematic models are essential for effective control of surgical robots. For tendon driven robots, which are common for minimally invasive surgery, the high nonlinearities in the transmission make modelling complex. Machine learning techniques are a preferred approach to tackle this problem. However, [...] Read more.
Accurate kinematic models are essential for effective control of surgical robots. For tendon driven robots, which are common for minimally invasive surgery, the high nonlinearities in the transmission make modelling complex. Machine learning techniques are a preferred approach to tackle this problem. However, surgical environments are rarely structured, due to organs being very soft and deformable, and unpredictable, for instance, because of fluids in the system, wear and break of the tendons that lead to changes of the system’s behaviour. Therefore, the model needs to quickly adapt. In this work, we propose a method to learn the kinematic model of a redundant surgical robot and control it to perform surgical tasks both autonomously and in teleoperation. The approach employs Feedforward Artificial Neural Networks (ANN) for building the kinematic model of the robot offline, and an online adaptive strategy in order to allow the system to conform to the changing environment. To prove the capabilities of the method, a comparison with a simple feedback controller for autonomous tracking is carried out. Simulation results show that the proposed method is capable of achieving very small tracking errors, even when unpredicted changes in the system occur, such as broken joints. The method proved effective also in guaranteeing accurate tracking in teleoperation. Full article
(This article belongs to the Special Issue Adaptive and Learning Control for Complex Dynamical Systems and Robotics)
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21 pages, 12107 KiB  
Article
Experimental Testing of Bandstop Wave Filter to Mitigate Wave Reflections in Bilateral Teleoperation
by Isaac O. Ogunrinde, Collins F. Adetu, Carl A. Moore, Jr., Rodney G. Roberts and Keimargeo McQueen
Robotics 2020, 9(2), 24; https://doi.org/10.3390/robotics9020024 - 11 Apr 2020
Cited by 4 | Viewed by 4203
Abstract
A bilateral teleoperation system can become unstable in the presence of a modest time delay. However, the wave variable algorithm provides stable operation for any fixed time delay using passivity arguments. Unfortunately, the wave variable method produces wave reflection that can degrade teleoperation [...] Read more.
A bilateral teleoperation system can become unstable in the presence of a modest time delay. However, the wave variable algorithm provides stable operation for any fixed time delay using passivity arguments. Unfortunately, the wave variable method produces wave reflection that can degrade teleoperation performance when a mismatched impedance exists between the master and slave robot. In this work, we develop a novel bandstop wave filter and experimentally verify that the technique can mitigate the effects of wave reflections in bilaterally teleoperated systems. We apply the bandstop wave filter in the wave domain and filtered the wave signal along the communication channel. We placed the bandstop wave filter in the master-to-slave robot path to alleviate lower frequency components of the reflected signal. With the lower frequency components reduced, wave reflections that degrade teleoperation performance were mitigated and we obtained a better transient response from the system. Results from our experiment show that the bandstop wave filter performed better by 67% when compared to the shaping wave filter respectively. Full article
(This article belongs to the Special Issue Adaptive and Learning Control for Complex Dynamical Systems and Robotics)
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