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Advanced Nonlinear and Learning-Based Control Techniques for Complex Dynamical Systems,...
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Advanced Nonlinear and Learning-Based Control Techniques for Complex Dynamical Systems, 3rd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 December 2026 | Viewed by 1725

Editors

Prof. Dr. Mahmut Reyhanoglu

E-Mail Website
Guest Editor
Mechanical Engineering, Douglas D. Schumann School of Engineering, Wentworth Institute of Technology, Boston, MA 02115, USA
Interests: thermoacustics; synthetic jet actuators; flow-induced-noise control; marine vehicle control; flow control
Special Issues, Collections and Topics in MDPI journals
Dr. Mohammad Jafari

E-Mail Website
Guest Editor
Robotics Engineering Program, Columbus State University, Columbus, GA 31907, USA
Interests: real-time learning-based control; machine learning; multi-agent systems; control & systems theory; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There has been a great deal of excitement during the recent past over the emergence of new mathematical techniques for the modeling and analysis of complex dynamical systems. For example, recent years have witnessed an explosion of work on the development of both learning-based and nonlinear control system models in a geometric form that are globally defined without singularities or ambiguities. These models are applied to the motion planning and feedback control of constrained robotic systems. These fascinating topics require the use of diverse parts of mathematics. Nonlinear and learning-based control system theory and various design techniques are used widely in the robotics arena, especially in developing nonlinear robust control algorithms. The design of these systems involves advanced techniques including nonlinear optimization, machine learning, adaptive estimation, and nonlinear observer and control design methodologies. In this context, this Special Issue welcomes the submission of papers from a wide range of researchers in applied mathematics and various engineering disciplines.

Potential topics include, but are not limited to, the following:

  • Nonlinear optimization techniques;
  • Nonlinear observer design;
  • Nonlinear adaptive estimation;
  • Nonlinear robust control;
  • Reduced-order modeling and control;
  • Learning-based/intelligent control;
  • Neuro-adaptive control;
  • Gaussian-process-based control methods;
  • Real-time learning-based control;
  • Multi-agent systems control;
  • Formation/flocking control;
  • Geometric control theory and applications.

Prof. Dr. Mahmut Reyhanoglu
Dr. Mohammad Jafari
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. Electronics 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

  • optimization
  • observer design
  • adaptive control
  • learning control
  • intelligent control
  • robust control
  • formation control
  • geometric control

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Published Papers (3 papers)

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Research

40 pages, 13940 KB  
Article
Design and Experimental Validation of an ISMC-Based Position Controller with Supervisory RBF Neural Network and PIO for BLDC Motor Systems
by Young Ik Son, Haneul Cho and Junho Kang
Electronics 2026, 15(15), 3373; https://doi.org/10.3390/electronics15153373 - 31 Jul 2026
Viewed by 208
Abstract
This paper proposes a robust position control method that integrates integral sliding mode control (ISMC), a radial basis function neural network (RBF–NN), and a proportional–integral observer (PIO) for a BLDC motor system subject to harmonic-drive loads under nonlinear friction, model uncertainty, and time-varying [...] Read more.
This paper proposes a robust position control method that integrates integral sliding mode control (ISMC), a radial basis function neural network (RBF–NN), and a proportional–integral observer (PIO) for a BLDC motor system subject to harmonic-drive loads under nonlinear friction, model uncertainty, and time-varying disturbances. In the proposed structure, the RBF–NN suppresses the major nonlinear equivalent disturbance components online, while the PIO estimates the residual disturbance remaining after the RBF–NN action. The PIO residual-disturbance estimate is further incorporated into the RBF–NN weight-update law to provide residual-disturbance information to the RBF–NN adaptation and improve the coordination between the two compensation mechanisms. The closed-loop stability of the proposed controller is demonstrated using Lyapunov analysis. The proposed method is validated through comparative simulations using an identified LuGre friction model and hardware experiments under step and sinusoidal reference inputs and disturbances, including baseline and additional-load conditions with increased model uncertainty. The results show that the proposed controller reduces residual tracking errors more effectively than conventional ISMC, ISMC+RBF, and internal model principle (IMP)+PIO controllers without requiring excessive control input, demonstrating its practical robustness in complex BLDC motor drive systems. Full article
(This article belongs to the Special Issue Advanced Nonlinear and Learning-Based Control Techniques for Complex Dynamical Systems, 3rd Edition)
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30 pages, 1436 KB  
Article
Computationally Efficient Predictive Control Using SINDy Models
by Maciej Ławryńczuk and Aleksander Samek
Electronics 2026, 15(12), 2530; https://doi.org/10.3390/electronics15122530 - 8 Jun 2026
Viewed by 462
Abstract
The Sparse Identification of Nonlinear Dynamics (SINDy) method yields compact and interpretable models that preserve physical system properties, offering a superior alternative to black-box models. This work proposes a computationally efficient Model Predictive Control (MPC) algorithm for SINDy models. The algorithm employs a [...] Read more.
The Sparse Identification of Nonlinear Dynamics (SINDy) method yields compact and interpretable models that preserve physical system properties, offering a superior alternative to black-box models. This work proposes a computationally efficient Model Predictive Control (MPC) algorithm for SINDy models. The algorithm employs a successively obtained online linear Taylor approximation of the model for future prediction, while the full SINDy model captures past dynamics. As a result, the nonlinear MPC problem is reformulated as a tractable quadratic program. The implementation covers three discretization schemes: the first-order Euler and the simplified and full fourth-order Runge–Kutta. Simulation benchmarks for population dynamics and aircraft models show that the algorithm achieves performance comparable to nonlinear MPC with significantly lower complexity, enabling real-time use. Full article
(This article belongs to the Special Issue Advanced Nonlinear and Learning-Based Control Techniques for Complex Dynamical Systems, 3rd Edition)
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19 pages, 9046 KB  
Article
A Learning-Based Closed-Loop Fluid Flow Regulation
by Mahmut Reyhanoglu and Mohammad Jafari
Electronics 2026, 15(5), 953; https://doi.org/10.3390/electronics15050953 - 26 Feb 2026
Viewed by 481
Abstract
This paper presents a learning-based robust control strategy for fluid flow dynamic systems, designed to compensate for modeling uncertainties and unknown disturbances inherent to closed-loop active flow control applications. The method is grounded in nonlinear control theory and uses gradient descent learning rules [...] Read more.
This paper presents a learning-based robust control strategy for fluid flow dynamic systems, designed to compensate for modeling uncertainties and unknown disturbances inherent to closed-loop active flow control applications. The method is grounded in nonlinear control theory and uses gradient descent learning rules to continuously update control parameters and disturbance estimate. The resulting closed-loop system achieves robust regulation while maintaining simplicity and interpretability in both implementation and analysis. Numerical simulations are conducted to validate the approach. Full article
(This article belongs to the Special Issue Advanced Nonlinear and Learning-Based Control Techniques for Complex Dynamical Systems, 3rd Edition)
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