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Dynamics and Control System Design for Robotics

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1194

Special Issue Editors

School of Mechanical Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: robot design and control
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Guest Editor
Department of Mechanical Engineering, BK21 FOUR ERICA-ACE Center, Hanyang University, Ansan 15588, Republic of Korea
Interests: robot design and control

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Guest Editor
Department of Robot Engineering, Keimyung University, Dalseo-gu, Daegu 42601, Republic of Korea
Interests: robot design and control

Special Issue Information

Dear Colleagues,

Dynamics and control are at the core of robotics research. Developing dynamic modeling and analysis based on dynamic characteristics is very important to understand the motion and interaction of the robotics system. By feedback signals from sensors, dynamic control is well-defined historically, and control methods are widely applied in robotics systems for high accuracy and adaptability. Recently, the machine learning approach is also very popular in the dynamic control of the robotics for large data collection from robot motion. This Special Issue aims to investigate recent research findings on dynamics and control system design in robotics. We welcome the recent results from dynamics and control theory to the application of the theory to the robots including humanoids and drones.

Topics of interest include, but are not limited to, dynamic modeling, dynamic analysis, motion control, interaction control, and machine-learning-based controls. Review papers are also welcomed to this Special Issue.

Dr. Taewon Seo
Dr. Taegyun Kim
Dr. Sungkeun Yoo
Guest Editors

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Keywords

  • robot design
  • robot control
  • robot sensing

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Published Papers (2 papers)

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Research

15 pages, 3559 KB  
Article
An Adaptive External Torque Estimation Algorithm for Collision Detection in Robotic Arms
by Cheng Yan, Ming Lyu, Yaowei Chen and Jie Zhang
Sensors 2025, 25(20), 6315; https://doi.org/10.3390/s25206315 - 13 Oct 2025
Viewed by 334
Abstract
As robotic applications rapidly expand into increasingly complex and dynamic environments, greater emphasis is being placed on the intelligence and safety of human–robot collaboration at the task execution level. In shared human–robot workspaces, even the most precise motion planning cannot fully prevent collisions. [...] Read more.
As robotic applications rapidly expand into increasingly complex and dynamic environments, greater emphasis is being placed on the intelligence and safety of human–robot collaboration at the task execution level. In shared human–robot workspaces, even the most precise motion planning cannot fully prevent collisions. To address this critical safety concern, we propose a variational Bayesian Kalman filtering-based external torque estimation algorithm that integrates the robot’s dynamic model while avoiding additional system complexity. We begin by reviewing the robot dynamics framework and the classical external torque estimation method based on generalized momentum. We then derive a Kalman filter-based approach for external torque estimation in robotic manipulators and analyze the adverse effects arising from mismatches in process noise covariance. Finally, we introduce a sliding window-based variational Bayesian Kalman filter, which dynamically estimates the current process noise covariance while simultaneously mitigating the accumulation of recursive errors. Full article
(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)
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28 pages, 2725 KB  
Article
Intelligent Counter-UAV Threat Detection Using Hierarchical Fuzzy Decision-Making and Sensor Fusion
by Fani Arapoglou, Paraskevi Zacharia and Michail Papoutsidakis
Sensors 2025, 25(19), 6091; https://doi.org/10.3390/s25196091 - 2 Oct 2025
Viewed by 698
Abstract
This paper proposes an intelligent hierarchical fuzzy decision-making framework for threat detection and identification in Counter-Unmanned Aerial Vehicle (Counter-UAV) systems, based on the fusion of heterogeneous sensor data. To address the increasing complexity and ambiguity in modern UAV threats, this study introduces a [...] Read more.
This paper proposes an intelligent hierarchical fuzzy decision-making framework for threat detection and identification in Counter-Unmanned Aerial Vehicle (Counter-UAV) systems, based on the fusion of heterogeneous sensor data. To address the increasing complexity and ambiguity in modern UAV threats, this study introduces a novel three-stage fuzzy inference architecture that supports adaptive sensor evaluation and optimal pairing. The proposed methodology consists of three-layered Fuzzy Inference Systems (FIS): FIS-A quantifies sensor effectiveness based on UAV flight altitude and detection probability; FIS-B assesses operational suitability using sensor range and cost; and FIS-C synthesizes both outputs, along with sensor capability overlap, to determine the composite suitability of sensor pairs. This hierarchical structure enables detailed analysis and system-level optimization, reflecting real-world constraints and performance trade-offs. Simulation-based evaluation using diverse sensor modalities (EO/IR, Radar, Acoustic, RF), supported by empirical data and literature, demonstrates the framework’s ability to handle uncertainty, enhance detection reliability, and support cost-effective sensor deployment in Counter-UAV operations. The framework’s modularity, scalability, and interpretability represent significant advancements in intelligent Counter-UAV system design, offering a transferable methodology for dynamic threat environments. Full article
(This article belongs to the Special Issue Dynamics and Control System Design for Robotics)
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