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Electronics
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Control and Design of Intelligent Robots
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Control and Design of Intelligent Robots

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 November 2025 | Viewed by 2293

Special Issue Editors

Dr. Guanglei Wu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
Interests: robotic design and control; robotic technology and industrial application
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Huiping Shen

E-Mail Website
Guest Editor
School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
Interests: parallel mechanisms

Special Issue Information

Dear Colleagues,

With the advancing of industrialization and the advent of the information age, intelligent robots play an increasingly important role in the increasing applications in various areas; therefore, the control and design of such systems have increasingly attracted attention from industrial and academic communities. This Special Issue entitled “Control and Design of Intelligent Robots” aims to collect some recent and cutting-edge research on the design and control of intelligent robots. The scope of the presented works ranges from design methodologies control algorithms to robot development. Reviews on different aspects of intelligent robots are also welcome. This collection aims to contribute to the related theories, methods, and technical guidance to intelligent robots’ technological and industrial development based on the current development trend in intelligent robotic systems.

The topic of this Special Issue includes but not limited to:

  • Robot design and mechanism synthesis;
  • Design principles for intelligent systems;
  • Robot control in dynamic environments;
  • Applications of computational intelligence in various robots;
  • Implementation in physical intelligent robotic systems;
  • Application of intelligent control techniques to robotics and manipulative systems;
  • Intelligent robotic systems theory, design, and applications;
  • Evolutionary computation, neural networks, fuzzy logic, learning.

Dr. Guanglei Wu
Prof. Dr. Huiping Shen
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. Electronics is an international peer-reviewed open access semimonthly 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

  • design methodology of robot structure
  • advanced control algorithms
  • applications of intelligent robots
  • human–robot collaboration
  • driverless technology
  • brain–computer interface

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Published Papers (3 papers)

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Research

27 pages, 3471 KiB  
Article
Control of a Dumper Vehicle with a Trailer Using Partial Feedback Linearization
by Jaume Franch, Jose-Manuel Rodriguez-Fortun and Rafael Herguedas
Electronics 2025, 14(11), 2293; https://doi.org/10.3390/electronics14112293 - 4 Jun 2025
Viewed by 360
Abstract
The control of vehicles towing trailers is of significant interest to industry due to their wide-ranging applications across various sectors. Trailers play essential roles in logistics, mining, and other fields. This study focuses on the control of a dumper with a trailer specifically [...] Read more.
The control of vehicles towing trailers is of significant interest to industry due to their wide-ranging applications across various sectors. Trailers play essential roles in logistics, mining, and other fields. This study focuses on the control of a dumper with a trailer specifically used for the monitoring of terrain stability in mining operations. The trailer is equipped with a radar system for detecting potential ground shifts that could jeopardize fieldwork safety. While numerous studies have addressed the control of Ackerman vehicles and trailers, this dumper presents a unique challenge due to its rear-axle steering mechanism. Due to this configuration, which has not been extensively studied in the literature, although the differential flatness of the system is proven, computation of the flat outputs leads to a system of partial differential equations that cannot be solved analytically. For this reason, this paper examines partial feedback linearization to facilitate control and proposes a solution for trajectory tracking that also stabilizes jack-knifing tendencies between the vehicle and trailer. The designed control system was successfully validated in a virtual environment. Full article
(This article belongs to the Special Issue Control and Design of Intelligent Robots)
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24 pages, 793 KiB  
Article
Nonlinear Observer Based on an Integrated Active Controller Applied to a Tractor with a Towed Implement System
by Claudia Verónica Vera Vaca, Cuauhtémoc Acosta Lúa, Joel Hinojosa-Dávalos, Claudia Carolina Vaca García and Stefano Di Gennaro
Electronics 2025, 14(8), 1575; https://doi.org/10.3390/electronics14081575 - 13 Apr 2025
Viewed by 280
Abstract
In this paper, a methodological framework employing an observer-based nonlinear controller is presented for controlling the lateral velocity of a farm tractor, as well as the yaw velocity of the agricultural implement. This approach relies on measurements obtained from sensors installed on a [...] Read more.
In this paper, a methodological framework employing an observer-based nonlinear controller is presented for controlling the lateral velocity of a farm tractor, as well as the yaw velocity of the agricultural implement. This approach relies on measurements obtained from sensors installed on a modern farm tractor, including lateral and longitudinal accelerations, longitudinal velocity, yaw rate, steering angle, and the differential yaw rate between the farm tractor and the implement. The nonlinear observer estimates the longitudinal and lateral velocities of the vehicle, as well as the roll dynamics of the implement, and ensures the exponential convergence of the observed variables. The control objective is formulated to ensure error feedback control, guaranteeing accurate tracking of the lateral velocity and yaw rate of the farm tractor and implement, following the reference patterns for these variables. The reference system is modeled as an “ideal” tractor operating without attachments. To evaluate the proposed controller’s performance, two test maneuvers were conducted. The first test involved the classic U-turn maneuver, commonly executed by tractors, while the second was a double-step maneuver, a standard in ground vehicle testing. Both maneuvers were simulated using MATLAB–Simulink to evaluate the controller’s effectiveness and robustness against parameter variations. Full article
(This article belongs to the Special Issue Control and Design of Intelligent Robots)
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23 pages, 8182 KiB  
Article
Sound Source Localization Using Deep Learning for Human–Robot Interaction Under Intelligent Robot Environments
by Hong-Min Jo, Tae-Wan Kim and Keun-Chang Kwak
Electronics 2025, 14(5), 1043; https://doi.org/10.3390/electronics14051043 - 6 Mar 2025
Cited by 1 | Viewed by 1197
Abstract
In this paper, we propose Sound Source Localization (SSL) using deep learning for Human–Robot Interaction (HRI) under intelligent robot environments. The proposed SSL method consists of three steps. The first step preprocesses the sound source to minimize noise and reverberation in the robotic [...] Read more.
In this paper, we propose Sound Source Localization (SSL) using deep learning for Human–Robot Interaction (HRI) under intelligent robot environments. The proposed SSL method consists of three steps. The first step preprocesses the sound source to minimize noise and reverberation in the robotic environment. Excitation source information (ESI), which contains only the original components of the sound source, is extracted from a sound source in a microphone array mounted on a robot to minimize background influence. Here, the linear prediction residual is used as the ESI. Subsequently, the cross-correlation signal between each adjacent microphone pair is calculated by using the ESI signal of each sound source. To minimize the influence of noise, a Generalized Cross-Correlation with the phase transform (GCC-PHAT) algorithm is used. In the second step, we design a single-channel, multi-input convolutional neural network that can independently learn the calculated cross-correlation signal between each adjacent microphone pair and the location of the sound source using the time difference of arrival. The third step classifies the location of the sound source after training with the proposed network. Previous studies have primarily used various features as inputs and stacked them into multiple channels, which made the algorithm complex. Furthermore, multi-channel inputs may not be sufficient to clearly train the interrelationship between each sound source. To address this issue, the cross-correlation signal between each sound source alone is used as the network input. The proposed method was verified on the Electronics and Telecommunications Research Institute-Sound Source Localization (ETRI-SSL) database acquired from the robotic environment. The experimental results revealed that the proposed method showed an 8.75% higher performance in comparison to the previous works. Full article
(This article belongs to the Special Issue Control and Design of Intelligent Robots)
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