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Machines
Special Issues
Advanced Design, Control, and Optimization for Parallel Manipulators
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Advanced Design, Control, and Optimization for Parallel Manipulators

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machine Design and Theory".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 902

Special Issue Editor

Dr. Ameer Hamza Khan

E-Mail Website
Guest Editor
Smart City Research Institute, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: robotics; manipulation; control; optimization; SLAM
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to contribute to this Special Issue on "Advanced Design, Control, and Optimization for Parallel Manipulators". Parallel manipulators have gained significant attention in recent years due to their stiffness, load-carrying capacity, and precision advantages, making them ideal for high-performance applications in aerospace, medical robotics, manufacturing, and scientific instrumentation. Despite their potential, the nonlinear kinematics, constrained workspaces, and complex dynamics pose major challenges for widespread deployment.

This Special Issue aims to review and advance the state of the art in parallel manipulators' modeling, design, and control. We welcome original contributions and comprehensive reviews addressing recent theoretical developments, novel design methodologies, real-time control strategies, and optimization techniques that enhance these systems' performance, reliability, and adaptability. These topics align with the Special Issue's scope and focus on robotics, control engineering, mechanical systems, and intelligent systems. We encourage multidisciplinary works that bridge mechanical design, artificial intelligence, and control theory to enable next-generation parallel robots.

Dr. Ameer Hamza Khan
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • novel architectures and topologies for parallel manipulators
  • kinematic and dynamic modeling techniques
  • trajectory planning and motion optimization
  • AI-augmented model predictive control
  • fault diagnosis and robust control

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Published Papers (1 paper)

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Research

20 pages, 3772 KB  
Article
Multibody Based Parameter Estimation of Stewart Platform Using Particles Swarm Optimization
by Mohamed M. Elshami, Haitham El-Hussieny, Hiroyuki Ishii and Ayman Nada
Machines 2026, 14(2), 218; https://doi.org/10.3390/machines14020218 - 12 Feb 2026
Viewed by 490
Abstract
Parameter estimation plays an important role in improving the accuracy, control, and diagnostic performance of mechanisms, particularly in parallel mechanisms such as the Stewart platform, which are increasingly used in high-precision automation, advanced manufacturing, and machine-centric applications. This paper presents a multibody–based framework [...] Read more.
Parameter estimation plays an important role in improving the accuracy, control, and diagnostic performance of mechanisms, particularly in parallel mechanisms such as the Stewart platform, which are increasingly used in high-precision automation, advanced manufacturing, and machine-centric applications. This paper presents a multibody–based framework for generalized dynamic modeling and inertial parameter estimation of parallel robotic manipulators, demonstrated on the DeltaLab-SMT EX800 Stewart platform. A systematic constrained multibody dynamic formulation is developed using an iterative kinematic–dynamic coupling scheme to compute generalized coordinates and their time derivatives under prescribed motion trajectories. The proposed identification manifold is experimentally validated on the physical test rig, in which the platform motion is executed via the control/DAQ system, while inertial measurements are acquired using an external 6-axis motion sensor to obtain direct acceleration data from the moving platform. Platform acceleration measurements are mapped through the inverse dynamics of the multibody model to derive the corresponding generalized forces, providing a practical and cost-effective alternative to direct force measurement with transducers. A Kalman filter is subsequently employed to combine the measured and the model-predicted data, yielding optimally filtered estimates of the inertial coordinates for accurate parameter identification. Inertial parameters are estimated using particle swarm optimization and bench marked against a gradient-based Levenberg–Marquardt approach, with comparison in terms of convergence behavior, robustness, and estimation accuracy. The results support the proposed framework as a measurement-informed benchmark methodology for parameter estimation of parallel manipulators. Full article
(This article belongs to the Special Issue Advanced Design, Control, and Optimization for Parallel Manipulators)
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