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Processes
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Modeling and Simulation of Robot Intelligent Control System
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Modeling and Simulation of Robot Intelligent Control System

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2407

Special Issue Editors

Dr. Raouf Fareh

E-Mail Website
Guest Editor
Electrical Engineering Department, University of Sharjah, Sharjah 27272, United Arab Emirates
Interests: advanced control engineering; intelligent control systems; industrial robots, mobile robots; autonomous navigation; vision guided robotic systems
Dr. Mohammad H. Rahman

E-Mail Website
Guest Editor
BioRobotics Lab, Mechanical/Biomedical Engineering Department, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA
Interests: robotics; biomedical engineering; mechanical engineering; control systems; spinal cord injury rehabilitation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on the "Modeling and Simulation of Robot Intelligent Control System" focuses on the latest research on the theoretical and practical aspects of modeling, simulating, and controlling intelligent robotic systems. This issue covers a range of topics, including advanced control techniques for robotic systems, the simulation of robotic behaviors, the integration of artificial intelligence in robots, and the development of algorithms for autonomous navigation and task execution. Researchers are invited to submit their latest findings on improving the efficiency, accuracy, and reliability of robot control systems through innovative modeling and simulation approaches. This Special Issue seeks contributions that address these challenges and propose solutions for enhancing robotic systems' adaptability and performance in various environments.

Dr. Raouf Fareh
Dr. Mohammad H. Rahman
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. Processes 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 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

  • robot control systems
  • intelligent robots
  • simulation
  • intelligent control systems
  • artificial intelligence
  • modeling of robotic systems
  • decision-making algorithms

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Published Papers (3 papers)

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Research

20 pages, 10090 KiB  
Article
A Novel P + d Control Scheme for Time-Delayed Telerobotic Systems with Damping Adjustment
by Lei Xi, Haochen Zhang, Jianrong Liu, Wenxu Zhang and Yangming Fan
Processes 2025, 13(3), 611; https://doi.org/10.3390/pr13030611 - 21 Feb 2025
Viewed by 310
Abstract
This paper presents a novel Proportional Damping Injection (P + d) control scheme that incorporates a damping regulation strategy and an adaptive method for networked telerobotic systems. Unlike the traditional P + d controller with a fixed damping coefficient, the proposed approach includes [...] Read more.
This paper presents a novel Proportional Damping Injection (P + d) control scheme that incorporates a damping regulation strategy and an adaptive method for networked telerobotic systems. Unlike the traditional P + d controller with a fixed damping coefficient, the proposed approach includes a dynamic damping adjuster, designed based on position error, to enhance the position tracking speed and improve the system robustness. To address uncertainties in dynamic models and external forces, Radial Basis Function (RBF) neural networks and an adaptive strategy are employed for dynamic estimation. The closed-loop stability of the teleoperation system was rigorously established using the Lyapunov–Krasovskii method, and the relationship between the controller gains and communication delay boundaries was explicitly derived. Finally, simulations and experimental results validated the system’s stability and effectiveness, demonstrating the advantages of the proposed controller. Full article
(This article belongs to the Special Issue Modeling and Simulation of Robot Intelligent Control System)
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29 pages, 6555 KiB  
Article
Robust Control Design and Optimization for Under-Actuated Mechanical Systems Considering Fuzzy Uncertainties
by Xiaofei Chen, Jie Fang and Jiandong Li
Processes 2025, 13(3), 609; https://doi.org/10.3390/pr13030609 - 21 Feb 2025
Viewed by 404
Abstract
This paper addresses the robust control problem for under-actuated mechanical systems subject to uncertainties. The key challenge lies in achieving precise control with insufficient degrees of freedom while maintaining robustness against system uncertainties. We propose a novel control framework that characterizes bounded, time-varying [...] Read more.
This paper addresses the robust control problem for under-actuated mechanical systems subject to uncertainties. The key challenge lies in achieving precise control with insufficient degrees of freedom while maintaining robustness against system uncertainties. We propose a novel control framework that characterizes bounded, time-varying uncertainties through fuzzy set theory, leading to a fuzzy dynamical system formulation. The main contributions are threefold: (1) the development of a deterministic robust controller that eschews traditional IF-THEN rules while guaranteeing system stability through a Lyapunov–Minimax analysis; (2) the formulation of a performance optimization scheme that minimizes both fuzzy system average performance and control costs, with proven existence and uniqueness of the analytical solution; and (3) the establishment of stability conditions using the Lyapunov theory for time-varying systems with bounded uncertainties. The theoretical framework is validated through both numerical simulations and experimental implementation on a linear motor-driven inverted pendulum system. The experimental results demonstrate significant performance improvements over conventional approaches: the optimal robust controller achieves 34.89% and 29.20% reductions in cart position and pendulum angle errors, respectively, from the initial conditions. A comparative analysis with traditional PD control shows a reduction in steady-state errors from 0.00318 m to 0.00057 m for the cart position and from 0.01117 rad to 0.00055 rad for the pendulum angle, validating the effectiveness of the proposed methodology. Full article
(This article belongs to the Special Issue Modeling and Simulation of Robot Intelligent Control System)
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15 pages, 2059 KiB  
Article
Intelligent Fuzzy Logic-Based Internal Model Control for Rotary Flexible Robots
by Omar Mohamed Gad, Raouf Fareh, Sofiane Khadraoui, Maamar Bettayeb and Mohammad Habibur Rahman
Processes 2024, 12(9), 1908; https://doi.org/10.3390/pr12091908 - 5 Sep 2024
Cited by 2 | Viewed by 1156
Abstract
Recently, there has been widespread and vital adoption of flexible manipulators due to their increased prevalence. This is attributed to the growing demand for flexibility in various tasks like refueling operations, inspections, and maintenance activities. Nevertheless, these robots are under-actuated systems characterized by [...] Read more.
Recently, there has been widespread and vital adoption of flexible manipulators due to their increased prevalence. This is attributed to the growing demand for flexibility in various tasks like refueling operations, inspections, and maintenance activities. Nevertheless, these robots are under-actuated systems characterized by a nonlinear behavior and present dynamic coupling interactions that contribute to the complexity of the control process. The main control objective is to achieve an accurate tracking of the desired position while simultaneously reducing oscillations occurring in the link. Therefore, this paper proposes integrating the tuning and adaptive control by employing fuzzy logic methodology in conjunction with internal model control (IMC). The suggested controller takes advantage of intelligent techniques, simple structure, robustness, and easy tuning of the conventional IMC. Both triangular and trapezoidal Membership Functions (MFs) are applied in this study to create a pair of Fuzzy Logic Controllers (FLCs) based on the Mamdani method. These controllers are employed to dynamically adjust the parameters of the IMC, in contrast to the fixed parameters used in the conventional IMC approach. The effectiveness of the suggested Adaptive-based Fuzzy IMC (AFIMC) is showcased through simulation and practical experimentation, in scenarios both with and without disturbances. Results indicate that this technique outperforms conventional IMC in achieving control objectives and rejecting disturbances. Full article
(This article belongs to the Special Issue Modeling and Simulation of Robot Intelligent Control System)
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