Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.6
2.2
Journals
Mathematics
Special Issues
Recent Advances in Adaptive Control Theory and Applications for Nonlinear...
share announcement

Recent Advances in Adaptive Control Theory and Applications for Nonlinear Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2719

Special Issue Editors

Dr. Jianhua Zhang

E-Mail Website
Guest Editor
School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China
Interests: neural networks; adaptive control; sliding mode control
Dr. Weicun Zhang

E-Mail Website
Guest Editor
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: adaptive control; self-tuning control; multiple model adaptive control; multiple model adaptive estimation; stability analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jing Na

E-Mail Website
Guest Editor
Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China
Interests: adaptive control; intelligent control; parameter estimation; nonlinear control and application
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qiang Chen

E-Mail Website
Guest Editor
Zhejiang Key Laboratory of Intelligent Perception and Control for Complex Systems, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Interests: adaptive and learning control; data-driven control of magnetically controlled capsules; intelligent adaptive control of electromechanical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Adaptive control serves as a foundational element of modern control engineering, enabling systems to autonomously maintain performance, stability, and robustness in the face of dynamic uncertainties and changing operating conditions. Its importance extends beyond theoretical innovation to mission-critical applications, effectively bridging the gap between idealized models and practical implementation.

However, the adaptive control of nonlinear systems involves significant challenges. These include establishing mathematically rigorous stability guarantees, ensuring robustness against unmodeled dynamics and external disturbances, and achieving computational efficiency sufficient for real-time operation.

This Special Issue, titled “Recent Advances in Adaptive Control Theory and Applications for Nonlinear Systems”, aims to highlight recent advances in the adaptive control theory and practical implementations. In response to feedback from the Editorial Board, we explicitly welcome contributions that also cover advanced topics in robust adaptive control with fixed/predefined-time convergence, data-driven adaptive dynamic programming and learning, adaptive control for multi-agent systems, adaptive model predictive control, adaptive control under multi-constraints, and hybrid learning-adaptive control fusion.

We encourage works that combine theoretical developments with real-world applications of control techniques, including, but not limited to, sliding mode control, intelligent observers, fractional-order controllers, high-gain estimation, machine learning-based adaptive schemes, adaptive graph neural networks, semi-supervised multi-modal fusion, permanent magnet motors, nonlinear modeling, optimal control, aircraft control, synchronization, system identification, and defect detection. Contributions exploring the convergence between robust linear theory and nonlinear dynamics are especially welcome.

Of particular interest are studies that demonstrate the interplay between mathematics and engineering, showcasing innovative uses of nonlinear models, optimal feedback strategies, and data-driven approaches applied to electrical, biological, or mechanical systems. Cross-disciplinary research combining control theory with robotics, biotechnology, computer vision, artificial intelligence, and process industries is highly encouraged.

We invite researchers from academia and industry to submit original papers that contribute to this vibrant and evolving field, strengthening the connection between mathematical theory and technological innovation.

Dr. Jianhua Zhang
Dr. Weicun Zhang
Prof. Dr. Jing Na
Prof. Dr. Qiang Chen
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics 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 2600 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

  • adaptive control
  • neural networks
  • sliding mode control
  • multi-agent systems
  • observer
  • synchronization
  • optimization
  • predictive control

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.

Published Papers (8 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

16 pages, 1649 KB  
Article
Experimental Validation of a Robust [FO-PID]λ Controller
by Nicoleta E. Badau, Ada M. Tudor and Cristina I. Muresan
Mathematics 2026, 14(4), 592; https://doi.org/10.3390/math14040592 - 8 Feb 2026
Abstract
Fractional order controllers are more frequently encountered in industrial applications due to their robustness and the improved performance they offer to the system. A large majority of research papers focus on methods for tuning controllers that are robust to gain variations. A novel [...] Read more.
Fractional order controllers are more frequently encountered in industrial applications due to their robustness and the improved performance they offer to the system. A large majority of research papers focus on methods for tuning controllers that are robust to gain variations. A novel approach to the design of a robust fractional order PID controller to variations in the time constant is studied in this manuscript. The procedure mentioned is developed for a first order plus time delay system. The robustness criterion used in the control algorithm is based on partial derivatives. The nonlinear system of equations obtained from all the imposed performance criteria is solved using the graphical method. To prove the efficiency of the proposed strategy, numerical simulations and experimental validation of the resulting controller are performed on a model of the DC servo system. The experimental results explicitly prove that the controller is robust to time-constant variations within the range of ±70%. Full article
(This article belongs to the Special Issue Recent Advances in Adaptive Control Theory and Applications for Nonlinear Systems)
14 pages, 1049 KB  
Article
Fractional Fuzzy Force-Position Control of Constrained Robots
by Aldo Jonathan Muñoz-Vázquez, Mohamed Gharib, Juan Diego Sánchez-Torres and Anh-Tu Nguyen
Mathematics 2026, 14(3), 565; https://doi.org/10.3390/math14030565 - 4 Feb 2026
Viewed by 147
Abstract
Modern robotic tasks often require interaction with the surrounding elements in the workspace. In some high-precision tasks, it is essential to stabilize the contact force on a smooth yet rigid surface, which can be modeled as a unilateral constraint. This challenge becomes increasingly [...] Read more.
Modern robotic tasks often require interaction with the surrounding elements in the workspace. In some high-precision tasks, it is essential to stabilize the contact force on a smooth yet rigid surface, which can be modeled as a unilateral constraint. This challenge becomes increasingly complex in the presence of disturbances. This study addresses these issues using a robust fuzzy force-position controller that combines the approximation capabilities of fuzzy inference systems with the nonlocal properties of fractional operators. The proposed approach extends the error integration to include proportional-integral-derivative (PID) components of the position error, along with the integral of the contact force error. This formulation leverages the orthogonality between force and velocity subspaces to achieve accurate force-position stabilization. Additionally, an adaptive mechanism enhances closed-loop performance and robustness. The effectiveness of the proposed controller is validated through analytical derivations and simulations, thereby demonstrating its reliability in constrained environments. Full article
(This article belongs to the Special Issue Recent Advances in Adaptive Control Theory and Applications for Nonlinear Systems)
►▼ Show Figures

Figure 1

24 pages, 1852 KB  
Article
State Estimation-Based Disturbance Rejection Control for Third-Order Fuzzy Parabolic PDE Systems with Hybrid Attacks
by Karthika Poornachandran, Elakkiya Venkatachalam, Oh-Min Kwon, Aravinth Narayanan and Sakthivel Rathinasamy
Mathematics 2026, 14(3), 444; https://doi.org/10.3390/math14030444 - 27 Jan 2026
Viewed by 205
Abstract
In this work, we develop a disturbance suppression-oriented fuzzy sliding mode secured sampled-data controller for third-order parabolic partial differential equations that ought to cope with nonlinearities, hybrid cyber attacks, and modeled disturbances. This endeavor is mainly driven by formulating an observer model with [...] Read more.
In this work, we develop a disturbance suppression-oriented fuzzy sliding mode secured sampled-data controller for third-order parabolic partial differential equations that ought to cope with nonlinearities, hybrid cyber attacks, and modeled disturbances. This endeavor is mainly driven by formulating an observer model with a T–S fuzzy mode of execution that retrieves the latent state variables of the perceived system. Progressing onward, the disturbance observers are formulated to estimate the modeled disturbances emerging from the exogenous systems. In due course, the information received from the system and disturbance estimators, coupled with the sliding surface, is compiled to fabricate the developed controller. Furthermore, in the realm of security, hybrid cyber attacks are scrutinized through the use of stochastic variables that abide by the Bernoulli distributed white sequence, which combat their unpredictability. Proceeding further in this framework, a set of linear matrix inequality conditions is established that relies on the Lyapunov stability theory. Precisely, the refined looped Lyapunov–Krasovskii functional paradigm, which reflects in the sampling period that is intricately split into non-uniform intervals by leveraging a fractional-order parameter, is deployed. In line with this pursuit, a strictly (Φ1,Φ2,Φ3)−ϱ dissipative framework is crafted with the intent to curb norm-bounded disturbances. A simulation-backed numerical example is unveiled in the closing segment to underscore the potency and efficacy of the developed control design technique. Full article
(This article belongs to the Special Issue Recent Advances in Adaptive Control Theory and Applications for Nonlinear Systems)
17 pages, 2398 KB  
Article
Predefined-Time Trajectory Tracking of Mechanical Systems with Full-State Constraints via Adaptive Neural Network Control
by Na Liu, Xuan Yu, Jianhua Zhang, Yichen Jiang and Cheng Siong Chin
Mathematics 2026, 14(3), 396; https://doi.org/10.3390/math14030396 - 23 Jan 2026
Viewed by 253
Abstract
An adaptive control strategy is developed and analyzed for trajectory tracking of mechanical systems subject to simultaneous model uncertainties and full-state constraints. To overcome the significant hurdle of guaranteeing both transient and steady-state performance within a user-defined time, a novel predefined-time adaptive neural [...] Read more.
An adaptive control strategy is developed and analyzed for trajectory tracking of mechanical systems subject to simultaneous model uncertainties and full-state constraints. To overcome the significant hurdle of guaranteeing both transient and steady-state performance within a user-defined time, a novel predefined-time adaptive neural network (NN) control scheme is proposed. By integrating predefined-time stability theory with a nonlinear mapping framework, a control scheme is developed to rigorously enforce full-state constraints while achieving predefined-time convergence. Radial basis function neural networks (RBFNNs) are employed to approximate the unknown system dynamics, with adaptive laws designed for online learning. The nonlinear mapping is strategically incorporated to ensure that the full-state constraints are never violated throughout the entire operation. Furthermore, through Lyapunov stability theory, it is proved that all signals of the resulting closed-loop system are uniformly ultimately bounded, and most importantly, the trajectory tracking error converges to a small neighborhood of zero within a predefined time, which can be explicitly set regardless of initial conditions. Comparative simulation results on a representative mechanical system are provided to demonstrate the superiority of the proposed controller, showcasing its faster convergence, higher tracking accuracy, and guaranteed constraint satisfaction compared to conventional finite-time and adaptive NN control methods. Full article
(This article belongs to the Special Issue Recent Advances in Adaptive Control Theory and Applications for Nonlinear Systems)
►▼ Show Figures

Figure 1

21 pages, 5307 KB  
Article
Observer-Based Adaptive Event-Triggered Fault-Tolerant Control for Bidirectional Consensus of MASs with Sensor Faults
by Shizhong Yang, Hongchao Wei and Shicheng Liu
Mathematics 2026, 14(2), 265; https://doi.org/10.3390/math14020265 - 10 Jan 2026
Viewed by 338
Abstract
The adaptive event-triggered fault-tolerant control problem for bidirectional consensus of multi-agent systems (MASs) subject to sensor faults and external disturbances is investigated. A hierarchical algorithm is first introduced to eliminate the dependence on Laplacian matrix information, thereby reducing computational complexity. Subsequently, a disturbance [...] Read more.
The adaptive event-triggered fault-tolerant control problem for bidirectional consensus of multi-agent systems (MASs) subject to sensor faults and external disturbances is investigated. A hierarchical algorithm is first introduced to eliminate the dependence on Laplacian matrix information, thereby reducing computational complexity. Subsequently, a disturbance observer (DO) and a compensation signal were constructed to accommodate external disturbances, filtering errors, and approximation errors introduced by the radial basis function neural network (RBFNN). Compared with the absence of a disturbance observer, the tracking performance was improved by 15.2%. In addition, a switching event-triggered mechanism is considered, in which the advantages of fixed-time triggering and relative triggering are integrated to balance communication frequency and tracking performance. Finally, the boundedness of all signals under the proposed fault-tolerant control (FTC) scheme is established. It has been clearly demonstrated by the simulation results that the proposed mechanism achieves a 39.8% reduction in triggering frequency relative to the FT scheme, while simultaneously yielding a 5.0% enhancement in tracking performance compared with the RT scheme, thereby highlighting its superior efficiency and effectiveness. Full article
(This article belongs to the Special Issue Recent Advances in Adaptive Control Theory and Applications for Nonlinear Systems)
►▼ Show Figures

Figure 1

28 pages, 2435 KB  
Article
Neural Network-Based Adaptive Finite-Time Control for Pure-Feedback Stochastic Nonlinear Systems with Full State Constraints, Actuator Faults, and Backlash-like Hysteresis
by Mohamed Kharrat and Paolo Mercorelli
Mathematics 2026, 14(1), 30; https://doi.org/10.3390/math14010030 - 22 Dec 2025
Viewed by 277
Abstract
This paper addresses the tracking control problem for pure-feedback stochastic nonlinear systems subject to full state constraints, actuator faults, and backlash-like hysteresis. An adaptive finite-time control strategy is proposed, using radial basis function neural networks to approximate unknown system dynamics. By integrating barrier [...] Read more.
This paper addresses the tracking control problem for pure-feedback stochastic nonlinear systems subject to full state constraints, actuator faults, and backlash-like hysteresis. An adaptive finite-time control strategy is proposed, using radial basis function neural networks to approximate unknown system dynamics. By integrating barrier Lyapunov functions with a backstepping design, the method guarantees semi-global practical finite-time stability of all closed-loop signals. The strategy ensures that all states remain within prescribed limits while achieving accurate tracking of the reference signal in finite time. The effectiveness and superiority of the proposed approach are demonstrated through simulations, including a numerical example and a rigid robot manipulator system, with comparisons to existing methods highlighting its advantages. Full article
(This article belongs to the Special Issue Recent Advances in Adaptive Control Theory and Applications for Nonlinear Systems)
►▼ Show Figures

Figure 1

31 pages, 2903 KB  
Article
Optimal Control of a Small Flexible Aircraft Using an Active Gust Alleviation Device
by Yanxuan Wu, Yifan Fu, Hao Li, Xudong Luo and Haonan Sun
Mathematics 2025, 13(24), 3986; https://doi.org/10.3390/math13243986 - 14 Dec 2025
Viewed by 324
Abstract
Small flexible-wing aircraft are vulnerable to gusts due to their low inertia and operating regime at low-Reynolds-number regimes, compromising flight stability and mission reliability. This paper introduces a novel active gust alleviation device (AGAD) installed at the wingtip, which works in concert with [...] Read more.
Small flexible-wing aircraft are vulnerable to gusts due to their low inertia and operating regime at low-Reynolds-number regimes, compromising flight stability and mission reliability. This paper introduces a novel active gust alleviation device (AGAD) installed at the wingtip, which works in concert with the conventional tail-plane to form a multi-surface control system. To coordinate these surfaces optimally, a quasi-static aeroelastic aircraft model is established, and a linear–quadratic regulator (LQR) controller is designed. A key innovation is the integration of an extended state observer (ESO) to estimate the unmeasurable, gust-induced angle of attack in real time, allowing the LQR to effectively counteract unsteady disturbances. Comparative simulations against a baseline (tail-plane-only control) demonstrate the superiority of the combined AGAD-tail strategy: the peak gust responses in pitch angle and normal acceleration are reduced by over 57% and 20%, respectively, while structural loads at the wing root are also significantly attenuated. Furthermore, the AGAD enhances maneuverability, reducing climb time by 20% during a specified maneuver. This study confirms that the integrated AGAD and LQR-ESO framework provides a practical and effective solution for enhancing both the stability and agility of small flexible aircraft in gusty environments, with direct benefits for applications like precision inspection and monitoring. Full article
(This article belongs to the Special Issue Recent Advances in Adaptive Control Theory and Applications for Nonlinear Systems)
►▼ Show Figures

Figure 1

23 pages, 8067 KB  
Article
Closed-Loop Inner–Outer Dual-Loop Attitude Adjustment Control for Dual-Super Spacecraft with Pointing Constraints
by Jiaxiang Xie, Jie Qin, Chensheng Cai, Fanwei Meng and Aiping Pang
Mathematics 2025, 13(23), 3748; https://doi.org/10.3390/math13233748 - 21 Nov 2025
Cited by 1 | Viewed by 350
Abstract
As a high-precision and high-stability engineering platform for aerospace missions, the dual-super spacecraft is subject to numerous environmental constraints and disturbances in increasingly complex space environments, posing significant challenges to its attitude maneuvering process. Unlike traditional spacecraft, the dual-super spacecraft consists of two [...] Read more.
As a high-precision and high-stability engineering platform for aerospace missions, the dual-super spacecraft is subject to numerous environmental constraints and disturbances in increasingly complex space environments, posing significant challenges to its attitude maneuvering process. Unlike traditional spacecraft, the dual-super spacecraft consists of two cabins: a payload cabin and a platform cabin, with a magnetic levitation mechanism installed between them to prevent vibration transmission. This paper establishes a multi-coupled attitude model for the payload cabin, the platform cabin, and the magnetic levitation mechanism between them. Additionally, a collision avoidance control strategy is designed for the magnetic levitation mechanism to ensure the operational safety of the entire system. To address the external environmental constraints, a closed-loop dual-loop control framework is proposed for the payload cabin. The outer-loop performs stability control on the payload cabin, while the inner-loop employs explicit reference governor (ERG) to handle pointing constraints. The platform cabin follows the attitude control of the payload cabin, forming a master–slave coordinated control scheme. Simulation results demonstrate that the proposed multi-coupled control system framework performs effectively, ensuring both the satisfaction of pointing constraints and the operational safety of the dual-super spacecraft system. Full article
(This article belongs to the Special Issue Recent Advances in Adaptive Control Theory and Applications for Nonlinear 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 1-8
Back to TopTop