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Robotics
Special Issues
Robotics: Intelligent Control Theory
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Robotics: Intelligent Control Theory

A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "Industrial Robots and Automation".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 19433

Special Issue Editor

Dr. Hicham Chaoui

E-Mail Website
Guest Editor
1. Department of Electronics, Carleton University, Ottawa, ON K1S 5B6, Canada
2. Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA
Interests: multi-objective optimization; energy/health-conscious mechatronics; robotics; autonomous systems; diagnosis/prognosis; electrified transportation; motor drives; energy storage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of robotics is changing our daily life with its applications ranging from robotic manipulators in factory assembly lines to unmanned systems in transportation. Due to the inherent complexity in robotics, an advanced learning methodology is a key to self-learning and fast adaptation to disturbances.

Intelligent control theory is an active field of research that brings artificial intelligence and automatic control together to solve complex control problems, such as robotics. This class of control techniques is composed of neural network control, fuzzy logic control, neurofuzzy control, evolutionary computation, swarm algorithms, self-organizing systems, soft computing, machine learning, and intelligent agents-based control, to name a few. These strategies are very useful when no a priori mathematical model is available for the system to be controlled.

This Special Issue focuses on the latest developments in intelligent control theory and its application to robotics. Potential topics include but are not limited to the following:

  • Intelligent mobile robots;
  • Intelligent control of robotic manipulators;
  • Intelligent autonomous systems (unmanned surface/underwater/aerial vehicles);
  • Intelligent control of swarm robots;
  • Reinforcement learning-based control of robots;
  • Deep learning-based intelligent control of robots;
  • Intelligent optimization and applications to robotics;
  • Robot machine learning;
  • Intelligent multiagent control systems in robotics;
  • Intelligent modeling and identification in robotics;
  • Stability and robustness analysis of intelligent control systems;
  • Hybridization techniques in intelligent control for robotics;
  • New trends in intelligent control of robotics.

Dr. Hicham Chaoui
Guest Editor

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

  • Neural network control
  • Fuzzy logic control
  • Neurofuzzy control
  • Autonomous systems
  • Unmanned vehicles
  • Mobile robots
  • Swarm robots
  • Reinforcement learning
  • Deep learning
  • Intelligent optimization
  • Evolutionary computation
  • Swarm algorithms
  • Multiagent systems
  • Machine learning
  • Intelligent modeling
  • Stability analysis

Published Papers (2 papers)

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Research

13 pages, 1513 KiB  
Article
Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review
by Rongrong Liu, Florent Nageotte, Philippe Zanne, Michel de Mathelin and Birgitta Dresp-Langley
Robotics 2021, 10(1), 22; https://doi.org/10.3390/robotics10010022 - 24 Jan 2021
Cited by 78 | Viewed by 13478
Abstract
Deep learning has provided new ways of manipulating, processing and analyzing data. It sometimes may achieve results comparable to, or surpassing human expert performance, and has become a source of inspiration in the era of artificial intelligence. Another subfield of machine learning named [...] Read more.
Deep learning has provided new ways of manipulating, processing and analyzing data. It sometimes may achieve results comparable to, or surpassing human expert performance, and has become a source of inspiration in the era of artificial intelligence. Another subfield of machine learning named reinforcement learning, tries to find an optimal behavior strategy through interactions with the environment. Combining deep learning and reinforcement learning permits resolving critical issues relative to the dimensionality and scalability of data in tasks with sparse reward signals, such as robotic manipulation and control tasks, that neither method permits resolving when applied on its own. In this paper, we present recent significant progress of deep reinforcement learning algorithms, which try to tackle the problems for the application in the domain of robotic manipulation control, such as sample efficiency and generalization. Despite these continuous improvements, currently, the challenges of learning robust and versatile manipulation skills for robots with deep reinforcement learning are still far from being resolved for real-world applications. Full article
(This article belongs to the Special Issue Robotics: Intelligent Control Theory)
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15 pages, 1232 KiB  
Article
Adaptive Interval Type-2 Fuzzy Logic Control of a Three Degree-of-Freedom Helicopter
by Hicham Chaoui, Sumit Yadav, Rosita Sharif Ahmadi and Allal El Moubarek Bouzid
Robotics 2020, 9(3), 59; https://doi.org/10.3390/robotics9030059 - 30 Jul 2020
Cited by 4 | Viewed by 4893
Abstract
This paper combines interval type-2 fuzzy logic with adaptive control theory for the control of a three degree-of-freedom (DOF) helicopter. This strategy yields robustness to various kinds of uncertainties and guaranteed stability of the closed-loop control system. Thus, precise trajectory tracking is maintained [...] Read more.
This paper combines interval type-2 fuzzy logic with adaptive control theory for the control of a three degree-of-freedom (DOF) helicopter. This strategy yields robustness to various kinds of uncertainties and guaranteed stability of the closed-loop control system. Thus, precise trajectory tracking is maintained under various operational conditions with the presence of various types of uncertainties. Unlike other controllers, the proposed controller approximates the helicopter’s inverse dynamic model and assumes no a priori knowledge of the helicopter’s dynamics or parameters. The proposed controller is applied to a 3-DOF helicopter model and compared against three other controllers, i.e., PID control, adaptive control, and adaptive sliding-mode control. Numerical results show its high performance and robustness under the presence of uncertainties. To better assess the performance of the control system, two quantitative tracking performance metrics are introduced, i.e., the integral of the tracking errors and the integral of the control signals. Comparative numerical results reveal the superiority of the proposed method by achieving the highest tracking accuracy with the lowest control effort. Full article
(This article belongs to the Special Issue Robotics: Intelligent Control Theory)
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