Advances in Intelligent Robotics Control

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1326

Special Issue Editor


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Guest Editor
Robotics Research Center, Beijing Jiaotong University, Beijing 100044, China
Interests: robotic sensing and control; robotic mechanisms
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Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue on Advances in Intelligent Robotics Control, focusing on the most recent innovations in robotics and intelligent control systems. This Special Issue aims to showcase cutting-edge research that addresses key challenges in the design, development, and application of intelligent control mechanisms in robotics.

The scope of this issue encompasses a wide range of topics, including but not limited to autonomous systems, adaptive and learning control strategies, real-time robotic sensing, human–robot interaction, and robotic manipulation in unstructured environments. We particularly welcome contributions that explore novel control algorithms, robust adaptive control for uncertain environments, and the integration of machine learning techniques into robotic control systems.

As robotics continues to evolve in various domains—such as healthcare, industrial automation, and autonomous vehicles—there is a growing need to explore more efficient, safe, and intelligent control strategies. The research collected in this issue will address these emerging challenges and build on existing literature by integrating advanced artificial intelligence, control theory, and real-world applications to push the boundaries of what intelligent robotics can achieve.

We invite theoretical and applied contributions offering new insights and innovative approaches to intelligent robotic control.

Dr. Guangrong Chen
Guest Editor

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Keywords

  • intelligent robotics control
  • autonomous systems
  • machine learning in robotics
  • adaptive control
  • human–robot interaction
  • real-time control systems
  • robotic sensing

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Published Papers (1 paper)

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Research

21 pages, 4263 KiB  
Article
Non-Singular Fast Terminal Sliding Mode Control of 6-PUS Parallel Systems Based on Adaptive Disturbance Estimation
by Wenjing Niu, Xin Guo, Zhi Lan and Wenyuan Liang
Electronics 2025, 14(6), 1111; https://doi.org/10.3390/electronics14061111 - 11 Mar 2025
Viewed by 474
Abstract
The 6-PUS (P: prismatic joint; U: universal joint; S: spherical joint) parallel mechanism study in this paper is the core mechanism used in the rehabilitation robot for children with cerebral palsy, which has a dynamic platform that acts on the pelvis of the [...] Read more.
The 6-PUS (P: prismatic joint; U: universal joint; S: spherical joint) parallel mechanism study in this paper is the core mechanism used in the rehabilitation robot for children with cerebral palsy, which has a dynamic platform that acts on the pelvis of the child with cerebral palsy to provide support for the child. The complexity of the parallel mechanism leads to complex dynamics analysis and modeling errors, and the design of the controller must consider singularities, unknown external disturbances, stability, and so on. In response to the above considerations, this paper analyses the dynamics of a 6-PUS parallel mechanism and designs a non-singular fast terminal sliding mode control based on adaptive disturbance estimation to address the modeling errors and unknown external disturbance to which the system is subjected in practical applications. Feed-forward compensation of the controller is achieved by estimating the external disturbance and modeling errors to which the system is subjected with an adaptive disturbance estimation strategy. The non-singular fast terminal sliding mode controller is used to suppress the inherent jitter phenomenon of sliding mode control while ensuring the error between the actual trajectory and the desired trajectory converges quickly. Finally, the simulation results demonstrate that the designed controller can achieve highly accurate trajectory tracking despite the presence of modeling errors and external disturbances. Full article
(This article belongs to the Special Issue Advances in Intelligent Robotics Control)
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