Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
2.9
Journals
Applied Sciences
Special Issues
Optimization, Navigation and Automatic Control of Intelligent Systems
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Optimization, Navigation and Automatic Control of Intelligent Systems

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

Deadline for manuscript submissions: closed (20 July 2026) | Viewed by 2418

Editors

Prof. Dr. Carlos E. Castañeda

E-Mail Website
Guest Editor
Centro Universitario de los Lagos, Universidad de Guadalajara, Lagos de Moreno Jalisco 47460, Mexico
Interests: automatic control; artificial intelligence; power system control; renewable energies
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Antonio Valderrabano-Gonzalez

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, Mexico
Interests: power electronics; control of power electronic converters; FACTS devices; power quality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of intelligent systems has revolutionized various domains, including robotics, autonomous vehicles, industrial automation, and smart infrastructure. To enhance the efficiency, reliability, and adaptability of such systems, cutting-edge research in optimization, navigation, and automatic control is essential. This Special Issue aims to compile innovative contributions that address key challenges and propose novel solutions in these areas. Topics of interest include, but are not limited to, advanced control algorithms, machine learning-based optimization, path planning and navigation for autonomous systems, real-time decision-making, and adaptive control strategies. Papers on both theoretical developments and practical applications are welcome, with a focus on improving the performance, robustness, and autonomy of intelligent systems.

This Special Issue seeks to foster interdisciplinary collaboration among researchers working in control theory, artificial intelligence, robotics, and related fields, providing a platform for the sharing of insights and advancements that push the boundaries of intelligent automation.

Prof. Dr. Carlos E. Castañeda
Prof. Dr. Antonio Valderrabano-Gonzalez
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 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-anonymized 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

  • intelligent control systems
  • optimization algorithms
  • autonomous navigation
  • adaptive and nonlinear control
  • machine learning in control systems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 3954 KB  
Article
Adaptive Navigation Framework for Mobile Robots with Heterogeneous and Low-Fidelity Sensing
by Molly Watson, Zach Carter and Yeganeh Madadi
Appl. Sci. 2026, 16(16), 8316; https://doi.org/10.3390/app16168316 - 21 Aug 2026
Viewed by 77
Abstract
Simultaneous localization and mapping (SLAM) is a foundational capability for autonomous navigation in unknown environments. Its performance is strongly coupled to the type, quality, and reliability of available localization and perception sensor data, limiting the portability of navigation systems across heterogeneous mobile robot [...] Read more.
Simultaneous localization and mapping (SLAM) is a foundational capability for autonomous navigation in unknown environments. Its performance is strongly coupled to the type, quality, and reliability of available localization and perception sensor data, limiting the portability of navigation systems across heterogeneous mobile robot platforms. This paper presents an adaptive navigation framework designed to support portability across heterogeneous mobile robot platforms by decoupling localization providers from platform-specific localization and perception sensing configurations. A sensor abstraction layer normalizes heterogeneous and low-fidelity sensor localization and perception inputs into a unified representation, enabling structured operational modes constructed according to available sensing modalities, computational constraints, and environmental characteristics. A learning-based performance prediction module is further designed to estimate impending SLAM degradation and support proactive mode switching. Due to middleware constraints within the Pepper NAOqi stack, this predictive component was not deployed during experimental evaluation and remains part of the proposed architecture for future validation. Experimental results on real indoor navigation tasks demonstrate improved robustness and adaptive performance compared with fixed SLAM configurations without manual retuning. Full article
(This article belongs to the Special Issue Optimization, Navigation and Automatic Control of Intelligent Systems)
►▼ Show Figures

Figure 1

22 pages, 2204 KB  
Article
Real-Time Speed Regulation of Direct Current Electric Motors Controlled by an Electric Motor Drive System Based on Diverse Power Converter Topologies
by Santiago Elvira-Ceja, Antonio Valderrabano-Gonzalez, Carlos E. Castañeda and Hossam A. Gabbar
Appl. Sci. 2026, 16(3), 1357; https://doi.org/10.3390/app16031357 - 29 Jan 2026
Cited by 1 | Viewed by 768
Abstract
This paper presents a systematic approach for designing an electric motor drive system (EMDS) for a permanent magnet DC motor to achieve precise speed regulation using a classical PID controller. Smooth voltage trajectory planning based on Bézier curves is employed to mitigate high [...] Read more.
This paper presents a systematic approach for designing an electric motor drive system (EMDS) for a permanent magnet DC motor to achieve precise speed regulation using a classical PID controller. Smooth voltage trajectory planning based on Bézier curves is employed to mitigate high voltage and current peaks during step speed transitions, improving dynamic performance, reducing electrical stress, and making the control system physically realizable. A comparative evaluation of inverting buck–boost, positive buck–boost, and quadratic DC–DC converters is conducted using the same motor and controller, enabling the identification of the most suitable controller–converter pairing. Experimental results demonstrate that, with an appropriate converter topology and voltage trajectory, peak voltages and currents are significantly reduced, resulting in a smoother control action and reliable speed regulation without the need for complex control schemes. Full article
(This article belongs to the Special Issue Optimization, Navigation and Automatic Control of Intelligent Systems)
►▼ Show Figures

Figure 1

21 pages, 3297 KB  
Article
Model Predictive Control of Underwater Tethered Payload
by Mark O’Connor, Andy Simoneau and Rickey Dubay
Appl. Sci. 2025, 15(18), 10122; https://doi.org/10.3390/app151810122 - 17 Sep 2025
Cited by 1 | Viewed by 829
Abstract
A fully automated, buoy-based deployment sensor system is being developed to acquire high-quality water column data, and requires a controller to accurately position an array of sensors at various depths. The sensor system will be potentially deployed under rough ocean conditions. Depth is [...] Read more.
A fully automated, buoy-based deployment sensor system is being developed to acquire high-quality water column data, and requires a controller to accurately position an array of sensors at various depths. The sensor system will be potentially deployed under rough ocean conditions. Depth is measured by a pressure sensor and adjusted through a rotating drum powered by a stepper motor. The proposed controller uses a model predictive control algorithm, a type of optimal control that predicts system response to optimize control actions used to track a desired variable-depth, setpoint profile. The profile is calculated to ensure smooth motion of the system, preventing motor malfunction. A simplified system model was created and used to simulate an open-loop test and system response. Constraints were applied to the control actions to match the practical limitations of the stepper motor. The simulated results show successful tracking of both a shallow and deep profile. At this stage of testing, the effects of ocean currents are considered by using a simple disturbance that provides the effect of ocean currents. A practical prototype that can implement the model predictive controller was tested on the physical buoy-based system with good control performance. Full article
(This article belongs to the Special Issue Optimization, Navigation and Automatic Control of Intelligent Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop