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 6163

Special Issue Editors


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Guest Editor
Department of Multidisciplinay Engineering, Texas A&M University, 6200 Tres Lagos Blvd, Higher Education Center at McAllen, McAllen, TX 78504, USA
Interests: fractional calculus; nonlinear systems; robotics; fuzzy logics; neural networks; control theory; integral equations
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Co-Guest Editor
ITESO, Universidad Jesuita de Guadalajara, San Pedro Tlaquepaque 45604, Mexico
Interests: nonlinear control; sliding mode methods; state observers; robust control

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 Researchers in these fields are invited to contribute with their original and unpublished works.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-Vázquez
Dr. Juan Diego Sánchez Torres
Guest Editors

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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

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Published Papers (2 papers)

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Research

18 pages, 1136 KiB  
Article
Passive Fault-Tolerant Control of a 2-DOF Robotic Helicopter
by Manuel A. Zuñiga, Luis A. Ramírez, Gerardo Romero, Efraín Alcorta-García and Alejandro Arceo
Information 2021, 12(11), 445; https://doi.org/10.3390/info12110445 - 27 Oct 2021
Cited by 2 | Viewed by 2282
Abstract
The presence of faults in dynamic systems causes the potential loss of some of the control objectives. For that reason, a fault-tolerant controller is required to ensure a proper operation, as well as to reduce the risk of accidents. The present work proposes [...] Read more.
The presence of faults in dynamic systems causes the potential loss of some of the control objectives. For that reason, a fault-tolerant controller is required to ensure a proper operation, as well as to reduce the risk of accidents. The present work proposes a passive fault-tolerant controller that is based on robust techniques, which are utilized to adjust a proportional-derivative scheme through a linear matrix inequality. In addition, a nonlinear term is included to improve the accuracy of the control task. The proposed methodology is implemented in the control of a two degrees of a freedom robotic helicopter in a simulation environment, where abrupt faults in the actuators are considered. Finally, the proposed scheme is also tested experimentally in the Quanser® 2-DOF Helicopter, highlighting the effectiveness of the proposed controller. Full article
(This article belongs to the Special Issue Intelligent Control and Robotics)
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14 pages, 20714 KiB  
Article
Research on Generation Method of Grasp Strategy Based on DeepLab V3+ for Three-Finger Gripper
by Sanlong Jiang, Shaobo Li, Qiang Bai, Jing Yang, Yanming Miao and Leiyu Chen
Information 2021, 12(7), 278; https://doi.org/10.3390/info12070278 - 8 Jul 2021
Cited by 3 | Viewed by 2485
Abstract
A reasonable grasping strategy is a prerequisite for the successful grasping of a target, and it is also a basic condition for the wide application of robots. Presently, mainstream grippers on the market are divided into two-finger grippers and three-finger grippers. According to [...] Read more.
A reasonable grasping strategy is a prerequisite for the successful grasping of a target, and it is also a basic condition for the wide application of robots. Presently, mainstream grippers on the market are divided into two-finger grippers and three-finger grippers. According to human grasping experience, the stability of three-finger grippers is much better than that of two-finger grippers. Therefore, this paper’s focus is on the three-finger grasping strategy generation method based on the DeepLab V3+ algorithm. DeepLab V3+ uses the atrous convolution kernel and the atrous spatial pyramid pooling (ASPP) architecture based on atrous convolution. The atrous convolution kernel can adjust the field-of-view of the filter layer by changing the convolution rate. In addition, ASPP can effectively capture multi-scale information, based on the parallel connection of multiple convolution rates of atrous convolutional layers, so that the model performs better on multi-scale objects. The article innovatively uses the DeepLab V3+ algorithm to generate the grasp strategy of a target and optimizes the atrous convolution parameter values of ASPP. This study used the Cornell Grasp dataset to train and verify the model. At the same time, a smaller and more complex dataset of 60 was produced according to the actual situation. Upon testing, good experimental results were obtained. Full article
(This article belongs to the Special Issue Intelligent Control and Robotics)
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