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Applied Sciences
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Intelligent Control of Dynamical Processes and Systems
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Intelligent Control of Dynamical Processes and Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 5100

Special Issue Editors

Dr. Francisco Jurado

E-Mail Website
Guest Editor
División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/Instituto Tecnológico de La Laguna, Torreón 27000, Mexico
Interests: intelligent control; robotics; image processing; computer vision; control theory; mechatronics; automation and robotics; automation; control and instrumentation
Prof. Dr. Alma Y. Alanis

E-Mail Website
Guest Editor
Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), University of Guadalajara, Guadalajara 44330, Mexico
Interests: intelligent control; discrete-time nonlinear systems; artificial neural networks; applications to electromechanical systems; biomedical systems; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the difficulties associated with attempting to control complex processes, higher-order plants and multi-body dynamics with highly nonlinear interconnections, the decentralized control approach may be a good choice in the proposal and design of fault detection and control schemes for these classes of dynamical processes and systems. The decentralized control approach has proven to be a suitable option for some dynamic systems whose mathematical/parametric model is viewed as symmetrical with respect to a coordinate axis or to more than one plane of motion. The decentralized control approach also offers the degree of freedom of to propose a different controller for each of the subsystems that constitute the whole system. It is essential to establish the assumptions related to the dynamical interconnections between such subsystems.

The aim of this Special Issue is to present recent applications and advances pertaining to the decentralized control approach of complex dynamical processes and systems. Potential topics include (but are not limited to) modeling, identification, tracking control, decentralized control, adaptive control, artificial neural network control, fuzzy control, nonlinear control, optimization, reinforcement learning, biological processes, chemical processes, wastewater treatment, renewable energy processes, mobile robots, unmanned aerial vehicles, robotic manipulators, soft robotics, applications and experiments.

Best regards,

Dr. Francisco Jurado
Prof. Dr. Alma Y. Alanis
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. 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

  • modeling
  • identification
  • decentralized control
  • nonlinear control
  • intelligent control
  • optimization
  • reinforcement learning
  • robotics
  • biological processes
  • industrial processes
  • applications

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Published Papers (2 papers)

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24 pages, 13928 KiB  
Article
Advances in the Kinematics of Hexapod Robots: An Innovative Approach to Inverse Kinematics and Omnidirectional Movement
by Jorge A. Lizarraga, Jose A. Garnica, Javier Ruiz-Leon, Gustavo Munoz-Gomez and Alma Y. Alanis
Appl. Sci. 2024, 14(18), 8171; https://doi.org/10.3390/app14188171 - 11 Sep 2024
Cited by 2 | Viewed by 2387
Abstract
Hexapod robots have gained significant attention due to their potential applications in complex terrains and dynamic environments. However, traditional inverse kinematics approaches often face challenges in meeting the precision required for adaptive omnidirectional movement. This work introduces a novel approach to addressing these [...] Read more.
Hexapod robots have gained significant attention due to their potential applications in complex terrains and dynamic environments. However, traditional inverse kinematics approaches often face challenges in meeting the precision required for adaptive omnidirectional movement. This work introduces a novel approach to addressing these challenges through the Directed Angular Restitution (DAR) method. The DAR method offers significant innovation by simplifying the calculation of rotational transformations necessary for aligning vectors across different planes, thus enhancing control, stability, and accuracy in robotic applications. Unlike conventional methods, the DAR method extends the range of trigonometric functions and incorporates spin functions to ensure continuous and smooth trajectory tracking. This innovative approach has been rigorously tested on a hexapod robot model, demonstrating superior performance in movement precision and stability. The results confirm that the DAR method provides a robust and scalable solution for the inverse kinematics of hexapod robots, making it a critical advancement for applications in robotics and automation where precise control and adaptability are paramount. Full article
(This article belongs to the Special Issue Intelligent Control of Dynamical Processes and Systems)
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15 pages, 645 KiB  
Article
Neural Robust Control for a Mobile Agent Leader–Follower System
by David Rodriguez-Castellanos, Marco Blas-Valdez, Gualberto Solis-Perales and Marco Antonio Perez-Cisneros
Appl. Sci. 2024, 14(13), 5374; https://doi.org/10.3390/app14135374 - 21 Jun 2024
Cited by 1 | Viewed by 895
Abstract
A controller employing a combined new strategy of output feedback linearization and a recurrent high-order neural network (RHONN) adaptive approach for a mobile agent leader–follower system is presented. The controller structure is based on feedback linearization; then, a scheme of lumping uncertainties which [...] Read more.
A controller employing a combined new strategy of output feedback linearization and a recurrent high-order neural network (RHONN) adaptive approach for a mobile agent leader–follower system is presented. The controller structure is based on feedback linearization; then, a scheme of lumping uncertainties which are estimated via the RHONN is incorporated; with this estimate, the controller is able to produce a robust control action for mobile agents so they track a prescribed reference trajectory. Moreover, the nonlinear system part is transformed into a linearizable one; then, a specific function lumps all the nonlinearities, uncertain parameters, and unmodeled dynamics of the system; this overall function is estimated via the RHONN. Thus, both parametric uncertainties and unmodeled dynamics between agents can be compensated via the controller, and, subsequently, follower agents track the reference provided by the leader. The obtained controller is such that the estimation scheme is not based on high-gain controllers. Here, it is underlined that the main contribution consists of designing a nonlinear controller and combining it with an RHONN to estimate the nonlinear uncertainties in the leader–follower system. This control action includes robust features provided by the online recurrence and the nonlinear base of the neural network in which not general but specific parametric disturbances and unmodeled discrepancies are identified or compensated. For this control scheme, only nominal values of the system parameters are required, as well as the velocities of the agents. Numeric simulation of the model and designed tracking control are carried out in which the control law is applied to a two-wheeled differential mobile robot model, obtaining satisfactory results for tracking angular velocities of the wheels. Full article
(This article belongs to the Special Issue Intelligent Control of Dynamical Processes and Systems)
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