Vibration Isolation and Control in Mechanical Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 492

Special Issue Editors


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Guest Editor
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China
Interests: precision engineering; mechanical design; nanomanufacturing; metrology
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China
Interests: energy harvesting; vibration suppression; fluid-structure interaction

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Guest Editor
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China
Interests: robotics; micro-positioning; smart actuators; nonlinear system control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit your original research or review papers to our new Special Issue entitled “Vibration Isolation and Control in Mechanical Systems” in Machines. Vibration is a phenomenon inherently present in virtually all mechanical systems, from small-scale devices to large industrial machines, and it plays a crucial role in both performance and safety. Vibrations must be isolated or suppressed in precision applications, such as semiconductor manufacturing, medical equipment, and advanced optics. On the other hand, vibrations can also be harnessed for beneficial purposes, including vibration-assisted manufacturing and vibration-based energy harvesting. To better manipulate and master vibrations, theoretical insights, innovative technologies, and practical implementations are essential.

This Special Issue aims to compile cutting-edge research that explores theoretical developments, integrates state-of-the-art techniques, and implements practical solutions for vibration isolation and control. We welcome original research articles, review papers, and case studies, with particular encouragement for contributions involving real-world experiments. Research topics may include (but are not limited to) the following:

  • Active and passive vibration control;
  • Non-linear vibration control of mechanical systems;
  • Advanced control algorithms and strategies for vibration suppression;
  • Vibration isolation in precision instruments;
  • Smart materials and structures for vibration suppression;
  • Metamaterials and metastructures for vibration suppression;
  • Modal analysis and frequency response analysis;
  • Fluid–Structure Interaction (FSI) vibration control technology;
  • Vibration-based energy harvesting techniques;
  • Machine learning-based approaches to vibration control.

We look forward to receiving your contributions.

Dr. Chenglin Li
Dr. Juntong Xing
Dr. Bingxiao Ding
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. Machines 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

  • vibration isolation
  • vibration control
  • modal analysis
  • non-linear dynamics
  • signal processing
  • precision instruments
  • machine design
  • smart materials and structures
  • mechanical metamaterials

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Published Papers (1 paper)

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Research

21 pages, 3286 KiB  
Article
ELM-GA-Based Active Comfort Control of a Piggyback Transfer Robot
by Liyan Feng, Xinping Wang, Teng Liu, Kaicheng Qi, Long Zhang, Jianjun Zhang and Shijie Guo
Machines 2025, 13(8), 748; https://doi.org/10.3390/machines13080748 - 21 Aug 2025
Viewed by 129
Abstract
The improvement of comfort in the human–robot interaction for care recipients is a significant challenge in the development of nursing robots. The existing methods for enhancing comfort largely depend on subjective comfort questionnaires, which are prone to unavoidable errors. Additionally, traditional passive movement [...] Read more.
The improvement of comfort in the human–robot interaction for care recipients is a significant challenge in the development of nursing robots. The existing methods for enhancing comfort largely depend on subjective comfort questionnaires, which are prone to unavoidable errors. Additionally, traditional passive movement control approaches lack the ability to adapt and effectively improve care recipient comfort. To address these problems, this paper proposes an active, personalized intelligent control method based on neural networks. A muscle activation prediction model is established for the piggyback transfer robot, enabling dynamic adjustments during the care process to improve human comfort. Initially, a kinematic analysis of the piggyback transfer robot is conducted to determine the optimal back-carrying trajectory. Experiments were carried out to measure human–robot contact forces, chest holder rotation angles, and muscle activation levels. Subsequently, an Online Sequential Extreme Learning Machine (OS-ELM) algorithm is used to train a predictive model. The model takes the contact forces and chest holder rotation angle as inputs, while outputting the latissimus dorsi muscle activation levels. The Genetic Algorithm (GA) is then employed to dynamically adjust the chest holder’s rotation angle to minimize the difference between actual muscle activation and the comfort threshold. Comparative experiments demonstrate that the proposed ELM-GA-based active control method effectively enhances comfort during the piggyback transfer process, as evidenced by both subjective feedback and objective measurements of muscle activation. Full article
(This article belongs to the Special Issue Vibration Isolation and Control in Mechanical Systems)
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