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Distributed Control for Robotics
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Distributed Control for Robotics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 9622

Special Issue Editors

Dr. Mujahed Al-Dhaifallah

E-Mail Website
Guest Editor
1. Control and Instrumentation Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
2. Interdisciplinary Research Center (lRC) for Renewable Energy and Power Systems, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Interests: system identification; control systems; renewable and sustainable energy; optimization; artificial intelligence
Dr. Ibrahim Aljamaan

E-Mail Website
Guest Editor
Biomedical Engineering Department, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia
Interests: system identification; control systems; machine learning and artificial intelligence; optimization; nonlinear control systems; control system for biomedical applications

Special Issue Information

Dear Colleagues,

Humans have already attempted to simplify the work from ancient times by exploiting the mechanisms and machines assisting them. One of these machines is the industrial robot. The robot has to be equipped with the orientation mechanism, the positioning mechanism, the industrial sensors, the power drives, and the control system. The control system has all required information as one's tasks drive all motion robots and process.

Several control applications are, by nature, distributed over different processes as well as over several processors. Accomplishing such a system concerning the startup of processes, internal communications, and state changes instantly becomes complex. The architecture is primarily intended for robot control but has a wide range of potential applications.

The primary purpose of this Special Issue is to focus on the recent developments of distributed control for robotics and related applications by providing a multidisciplinary forum of discussion among scientists who have been interested in exploring such applications in different branches of engineering. We also invite expository and review papers by senior researchers to elucidate finer points or highlight techniques of broad interest.

Potential topics include but are not limited to the following:

  • Distributed control;
  • Industrial automation;
  • Industrial control;
  • System identification and state estimation;
  • Robot control and robotics;
  • Mathematical approach to motion coordination algorithm;
  • Robotic network with relative sensing;
  • Robotic network models;
  • Medical robotic network and control;
  • Distributed control and Internet of Things

Dr. Mujahed Al-Dhaifallah
Dr. Ibrahim Aljamaan
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. Applied Sciences 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.

Published Papers (5 papers)

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Research

18 pages, 10188 KiB  
Article
Robust Control of a Bimorph Piezoelectric Robotic Manipulator Considering Ellipsoidal-Type State Restrictions
by Francisco Moreno-Guzman, Ivan Salgado, David Cruz-Ortiz and Isaac Chairez
Appl. Sci. 2022, 12(15), 7589; https://doi.org/10.3390/app12157589 - 28 Jul 2022
Viewed by 1502
Abstract
The current study presents an adaptive control approach to solve the tracking trajectory problem for a robotic manipulator that uses a gripper based on bimorph piezoelectric actuators. The development of an adaptive gain state feedback form that considers the state restrictions is proposed [...] Read more.
The current study presents an adaptive control approach to solve the tracking trajectory problem for a robotic manipulator that uses a gripper based on bimorph piezoelectric actuators. The development of an adaptive gain state feedback form that considers the state restrictions is proposed using a novel class of barrier Lyapunov function that drives the effective control of joints and piezoelectric actuators. The proposed method allows for the inclusion of complex combinations of state restrictions in the Lyapunov function, yielding the construction of differential forms for the gains in the controller that can handle the evolution of trajectories of the robotic arm inside the restricted region. The proposed control design successfully tracks reference trajectories for both joints of the robotic arm as well as the motion of the piezoelectric device during several operative scenarios. A comprehensive experimental study evaluates the effect of introducing state-dependent gain considering state restrictions of the ellipsoidal type. The comparison of the mean square error confirms the contributions of the developed control action, showing better tracking quality for less control power with the same evaluation, which is a desirable characteristic in the controlled motion of micromanipulators. The proposed controller solves the tracking trajectory problem for the micromanipulation system, satisfies the motion restrictions, and allows better tracking performance to be enforced. Furthermore, comparison of the obtained trajectories seems to validate the proposed controller’s contribution concerning a feedback form with fixed gains. Full article
(This article belongs to the Special Issue Distributed Control for Robotics)
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14 pages, 14701 KiB  
Article
Collaborative Multi-Robot Formation Control and Global Path Optimization
by Di Liang, Zhongyi Liu and Ran Bhamra
Appl. Sci. 2022, 12(14), 7046; https://doi.org/10.3390/app12147046 - 12 Jul 2022
Cited by 9 | Viewed by 1910
Abstract
For multi-robot cooperative formation and global path planning, we propose to adjust the repulsive field function and insert a dynamic virtual target point to solve the local minima and target unreachability problems of the traditional artificial potential field (APF) method. The convergence speed [...] Read more.
For multi-robot cooperative formation and global path planning, we propose to adjust the repulsive field function and insert a dynamic virtual target point to solve the local minima and target unreachability problems of the traditional artificial potential field (APF) method. The convergence speed and global optimization accuracy of ant colony optimization (ACO) are improved by introducing improved state transfer functions with heuristic information of the artificial potential field method and optimizing the pheromone concentration update rules. A hybrid algorithm combining APF and improved ACO is used to calculate an optimal path from the starting point to the end point for the leader robot. A cooperative multi-robot formation control method combining graph theory and consistency algorithm is proposed based on path planning of the leader robot. Taking AGVs in a logistics distribution center as an example, the feasibility of the improved algorithm and its effectiveness in solving the multi-robot path planning problem are verified. Full article
(This article belongs to the Special Issue Distributed Control for Robotics)
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30 pages, 10312 KiB  
Article
GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning
by Abhilasha Singh, Venkatesan Kalaichelvi, Ashlyn DSouza and Ram Karthikeyan
Appl. Sci. 2022, 12(14), 6860; https://doi.org/10.3390/app12146860 - 6 Jul 2022
Cited by 1 | Viewed by 2756
Abstract
Weld seam identification with industrial robots is a difficult task since it requires manual edge recognition and traditional image processing approaches, which take time. Furthermore, noises such as arc light, weld fumes, and different backgrounds have a significant impact on traditional weld seam [...] Read more.
Weld seam identification with industrial robots is a difficult task since it requires manual edge recognition and traditional image processing approaches, which take time. Furthermore, noises such as arc light, weld fumes, and different backgrounds have a significant impact on traditional weld seam identification. To solve these issues, deep learning-based object detection is used to distinguish distinct weld seam shapes in the presence of weld fumes, simulating real-world industrial welding settings. Genetic algorithm-based state-of-the-art object detection models such as Scaled YOLOv4 (You Only Look Once), YOLO DarkNet, and YOLOv5 are used in this work. To support actual welding, the aforementioned architecture is trained with 2286 real weld pieces made of mild steel and aluminum plates. To improve weld detection, the welding fumes are denoised using the generative adversarial network (GAN) and compared with dark channel prior (DCP) approach. Then, to discover the distinct weld seams, a contour detection method was applied, and an artificial neural network (ANN) was used to convert the pixel values into robot coordinates. Finally, distinct weld shape coordinates are provided to the TAL BRABO manipulator for tracing the shapes recognized using an eye-to-hand robotic camera setup. Peak signal-to-noise ratio, the structural similarity index, mean square error, and the naturalness image quality evaluator score are the dehazing metrics utilized for evaluation. For each test scenario, detection parameters such as precision, recall, mean average precision (mAP), loss, and inference speed values are compared. Weld shapes are recognized with 95% accuracy using YOLOv5 in both normal and post-fume removal settings. It was observed that the robot is able to trace the weld seam more precisely. Full article
(This article belongs to the Special Issue Distributed Control for Robotics)
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12 pages, 3189 KiB  
Article
Construction and Evaluation of a Control Mechanism for Fuzzy Fractional-Order PID
by Mujahed Al-Dhaifallah
Appl. Sci. 2022, 12(14), 6832; https://doi.org/10.3390/app12146832 - 6 Jul 2022
Cited by 7 | Viewed by 1416
Abstract
In this research, a control mechanism for fuzzy fractional-order proportional integral derivatives was suggested (FFOPID). The fractional calculus application has been used in different fields of engineering and science and showed to be improved in the past few years. However, there are few [...] Read more.
In this research, a control mechanism for fuzzy fractional-order proportional integral derivatives was suggested (FFOPID). The fractional calculus application has been used in different fields of engineering and science and showed to be improved in the past few years. However, there are few studies on the implementation of the fuzzy fractional-order controller for control in real time. Therefore, for an experimental pressure control model, a fractional order PID controller with intelligent fuzzy tuning was constructed and its results were calculated through simulation. To highlight proposed control scheme advantages, the performances of the controller were inspected under load disturbances and variations in set-point conditions. Furthermore, with classical PID control schemes and fractional order proportional integral derivative (FOPID), a comparative study was made. It is revealed from the results that the suggested control scheme outclasses other categories of the control schemes. Full article
(This article belongs to the Special Issue Distributed Control for Robotics)
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13 pages, 1023 KiB  
Article
Identification of Wiener Box-Jenkins Model for Anesthesia Using Particle Swarm Optimization
by Ibrahim Aljamaan and Ahmed Alenany
Appl. Sci. 2022, 12(10), 4817; https://doi.org/10.3390/app12104817 - 10 May 2022
Viewed by 1259
Abstract
Anesthesia refers to the process of preventing pain and relieving stress on the patient’s body during medical operations. Due to its vital importance in health care systems, the automation of anesthesia has gained a lot of interest in the past two decades and, [...] Read more.
Anesthesia refers to the process of preventing pain and relieving stress on the patient’s body during medical operations. Due to its vital importance in health care systems, the automation of anesthesia has gained a lot of interest in the past two decades and, for this purpose, several models of anesthesia are proposed in the literature. In this paper, a Wiener Box-Jenkins model, consisting of linear dynamics followed by a static polynomial nonlinearity and additive colored noise, is used to model anesthesia. A set of input–output data is generated using closed-loop simulations of the Pharmacokinetic–Pharmacodynamic nonlinear (PK/PD) model relating the drug infusion rates, in [μgkg−1min−1], to the Depth of Anesthesia (DoA), in [%]. The model parameters are then estimated offline using particle swarm optimization (PSO) technique. Several Monte Carlo simulations and validation tests are conducted to evaluate the performance of the identified model. The simulation showed very promising results with a quick convergence in less than 10 iterations, with a percentage error less than 1.5%. Full article
(This article belongs to the Special Issue Distributed Control for Robotics)
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