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Mathematics
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Intelligent Control and Applications of Nonlinear Dynamic System
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Intelligent Control and Applications of Nonlinear Dynamic System

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 18 December 2025 | Viewed by 1445

Special Issue Editors

Prof. Dr. Dawei Gong

E-Mail Website
Guest Editor
Department of Mechatronics Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: intelligent control; robotics; nonlinear control; neural network
Dr. Jilie Zhang

E-Mail Website
Guest Editor
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610032, China
Interests: multi-agent consistent cooperative control and adaptive dynamic programming
Dr. Yang Deng

E-Mail Website
Guest Editor
Department of Automation, Tsinghua University, Beijing 100084, China
Interests: nonlinear control; time-delay systems; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nonlinear dynamic systems are prevalent in numerous fields, including engineering, physics, biology, and economics. Their intrinsic complexity often leads to unpredictable behaviors and challenges in both analysis and control. As a result, achieving effective control strategies for these systems has become a major research focus. With advancements in computational intelligence, including machine learning, artificial intelligence, and adaptive control techniques, researchers are now able to tackle the intricacies of nonlinear dynamics more effectively than ever before.

The aim of this Special Issue is to bring together cutting-edge research that explores the intersection of intelligent control methodologies and the applications of nonlinear dynamic systems. We seek contributions that showcase innovative approaches to modeling, analysis, and control of these systems, leveraging intelligent control techniques to enhance system performance and robustness. Potential topics may include, but are not limited to, the following:

  • Development of novel machine learning algorithms for pattern recognition and system identification in nonlinear dynamic environments.
  • Adaptive and robust control strategies tailored for nonlinear systems exhibiting varying dynamics and uncertainties.
  • Real-time control applications utilizing intelligent systems in robotics, automotive engineering, and other high-stake settings.
  • Implementation of fuzzy logic control, neural networks, and genetic algorithms in optimizing the performance of nonlinear dynamic systems.
  • Case studies elucidating practical applications and empirical results stemming from the integration of intelligent control in nonlinear dynamics.

Prof. Dr. Dawei Gong
Dr. Jilie Zhang
Dr. Yang Deng
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. Mathematics 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 2600 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

  • adaptive dynamic programming
  • reinforcement learning
  • optimal control
  • robot control
  • model-free adaptive control
  • containment control

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Published Papers (2 papers)

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33 pages, 15730 KiB  
Article
Design and Analysis of Modular Reconfigurable Manipulator System
by Yutong Wang, Junjie Li, Ke Wang and Shaokun Wang
Mathematics 2025, 13(7), 1103; https://doi.org/10.3390/math13071103 - 27 Mar 2025
Viewed by 212
Abstract
With the continuous development of modern robotics technology, in order to overcome the obstacles to the ability to complete tasks due to the fixed structure of the robot itself, to realize the reconfigurable purpose of the manipulator, it can be assembled into different [...] Read more.
With the continuous development of modern robotics technology, in order to overcome the obstacles to the ability to complete tasks due to the fixed structure of the robot itself, to realize the reconfigurable purpose of the manipulator, it can be assembled into different degrees of freedom or configurations according to the needs of different tasks, which has the characteristics of a compact structure, high integrability, and low cost. The overall design scheme of a cable-free modular reconfigurable manipulator is proposed, and based on the target design parameters, the structural design of each module is completed, and the module library is constructed. Each module realizes rapid assembly or disassembly through a new type of docking mechanism module, which improves the flexibility and reliability of the manipulator. Meanwhile, a finite element analysis is carried out on the whole manipulator to optimize the structure that does not meet the strength and stiffness requirements. The wireless energy transmission module is integrated into the joint module to realize the cable-free design of the manipulator in the structure. The kinematic models of each module are established separately, providing a method to quickly construct the kinematics of different configurations of the manipulator, and the dexterity of the workspace is analyzed. Then, two methods, joint space planning and Cartesian space planning, are adopted to generate the corresponding motion paths and kinematic curves, which successfully verifies the reasonableness of the kinematics of the designed manipulator. Finally, combined with the results of the dynamics simulation, the corresponding dynamics curves of the end of each joint are generated to further verify the reliability of its design. It provides a new way of thinking for the research and development of highly intelligent and highly integrated manipulators. Full article
(This article belongs to the Special Issue Intelligent Control and Applications of Nonlinear Dynamic System)
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17 pages, 6358 KiB  
Article
Continuous Multi-Target Approaching Control of Hyper-Redundant Manipulators Based on Reinforcement Learning
by Han Xu, Chen Xue, Quan Chen, Jun Yang and Bin Liang
Mathematics 2024, 12(23), 3822; https://doi.org/10.3390/math12233822 - 3 Dec 2024
Cited by 1 | Viewed by 998
Abstract
Hyper-redundant manipulators based on bionic structures offer superior dexterity due to their large number of degrees of freedom (DOFs) and slim bodies. However, controlling these manipulators is challenging because of infinite inverse kinematic solutions. In this paper, we present a novel reinforcement learning-based [...] Read more.
Hyper-redundant manipulators based on bionic structures offer superior dexterity due to their large number of degrees of freedom (DOFs) and slim bodies. However, controlling these manipulators is challenging because of infinite inverse kinematic solutions. In this paper, we present a novel reinforcement learning-based control method for hyper-redundant manipulators, integrating path and configuration planning. First, we introduced a deep reinforcement learning-based control method for a multi-target approach, eliminating the need for complicated reward engineering. Then, we optimized the network structure and joint space target points sampling to implement precise control. Furthermore, we designed a variable-reset cycle technique for a continuous multi-target approach without resetting the manipulator, enabling it to complete end-effector trajectory tracking tasks. Finally, we verified the proposed control method in a dynamic simulation environment. The results demonstrate the effectiveness of our approach, achieving a success rate of 98.32% with a 134% improvement using the variable-reset cycle technique. Full article
(This article belongs to the Special Issue Intelligent Control and Applications of Nonlinear Dynamic System)
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