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Phenomena in Nonlinear Dynamical Systems: Theory and Application

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 1132

Special Issue Editor


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Guest Editor
Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
Interests: nonlinear vibrations; rotor dynamics; mechanisms with variable mass

Special Issue Information

Dear Colleagues,

Although research in the field of dynamics and oscillations dates back to ancient times, some problems still remain unsolved;  these issues primarily concern non-linearities in systems or processes. The aim of this Special Issue is to publish the results of research on the influence of both physical and geometric nonlinearities on system dynamics. Hence, this issue focuses on research in the dynamics and vibrations of nonlinear mechanical systems and processes. The following topics are of interest:

  • Mathematical modelling of nonlinear dynamical and vibration systems and processes;
  • New analytical and numerical solving procedures for strong nonlinear oscillators;
  • Nonlinear phenomena in dynamic systems;
  • Bifurcation and deterministic chaos;
  • Control in nonlinear oscillators;
  • Analogies between nonlinear mechanical and other systems;
  • Application of nonlinear oscillators in applied sciences and engineering.

The Special Issue on “Phenomena in Nonlinear Dynamical Systems: Theory and Application” welcomes not only submissions on the aforementioned topics but also on all recent research covered by the topic in the title. The call is open to a broad thematic range of papers covering recent applications of dynamics, vibration and control theory.

Prof. Dr. Lívija Cveticanin
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 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. Applied Sciences 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 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

  • vibrations: theory and application
  • dynamics of mechanisms and machines
  • multi-degrees-of-freedom systems
  • control systems

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Published Papers (1 paper)

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Research

19 pages, 6497 KiB  
Article
Nonlinear Model Predictive Control with Evolutionary Data-Driven Prediction Model and Particle Swarm Optimization Optimizer for an Overhead Crane
by Tom Kusznir and Jarosław Smoczek
Appl. Sci. 2024, 14(12), 5112; https://doi.org/10.3390/app14125112 - 12 Jun 2024
Viewed by 889
Abstract
This paper presents a new approach to the nonlinear model predictive control (NMPC) of an underactuated overhead crane system developed using a data-driven prediction model obtained utilizing the regularized genetic programming-based symbolic regression method. Grammar-guided genetic programming combined with regularized least squares was [...] Read more.
This paper presents a new approach to the nonlinear model predictive control (NMPC) of an underactuated overhead crane system developed using a data-driven prediction model obtained utilizing the regularized genetic programming-based symbolic regression method. Grammar-guided genetic programming combined with regularized least squares was applied to identify a nonlinear autoregressive model with an exogenous input (NARX) prediction model of the crane dynamics from input–output data. The resulting prediction model was implemented in the NMPC scheme, using a particle swarm optimization (PSO) algorithm as a solver to find an optimal sequence of the control actions satisfying multi-objective performance requirements and input constraints. The feasibility and performance of the controller were experimentally verified using a laboratory crane actuated by AC motors and compared with a discrete-time feedback controller developed using the pole placement technique. A series of experiments proved the effectiveness of the controller in terms of robustness against operating condition variation and external disturbances. Full article
(This article belongs to the Special Issue Phenomena in Nonlinear Dynamical Systems: Theory and Application)
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