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Applied Sciences
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Data-Driven Control System: Methods and Applications
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Data-Driven Control System: Methods and Applications

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

Deadline for manuscript submissions: closed (30 January 2026) | Viewed by 6955

Editors

Dr. Wenfeng Hu

E-Mail Website
Guest Editor
School of Automation, Central South University, Changsha 410000, China
Interests: complex system intelligent control; multi-agent reinforcement learning; artificial intelligence; coordinated control of multi-mobile robot; distributed control of multi-agent system; networked control system; control and scheduling of high-speed rail
Special Issues, Collections and Topics in MDPI journals
Dr. M. (Meichen) Guo

E-Mail Website
Guest Editor
Delft Center for Systems and Control, Delft University of Technology, 2600 Delft, The Netherlands
Interests: data-driven control; nonlinear control; distributed control; output regulation; game theory; agriculture applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advent of data-driven strategies in control systems engineering is profoundly influencing technological advancements across numerous domains. This Special Issue, titled "Data-Driven Control Systems: Methods and Applications", seeks to present innovative ideas and experimental outcomes in the realm of data-driven control from theoretical concepts to practical implementations.

This Special Issue aims to cover a wide array of topics within data-driven control systems, including, but not limited to, the following: development and application of machine learning algorithms for real-time control, adaptive control mechanisms that learn from data in situ, and the utilization of big data analytics for enhancing control strategies. Additionally, we are interested in papers that explore the integration of IoT technologies with control systems to push the boundaries of automation and efficiency, as well as research that addresses the challenges of security, privacy, and robustness in these systems.

Specific areas of focus include advanced algorithmic solutions that facilitate predictive and adaptive control, techniques for managing and analyzing massive datasets in real-time to improve system responses, and the design of resilient architectures that support the demands of data-intensive, high-performance control applications. Theoretical explorations that ensure stability and reliability in control systems, along with practical applications demonstrating significant enhancements in sectors such as manufacturing, robotics, and smart grids, are particularly welcome.

We invite submissions of high-quality, original research papers that contribute to the following fields:

  • Data-driven algorithms for dynamic control systems;
  • Real-time analytics and decision-making processes;
  • Adaptive control techniques using machine learning;
  • Security and data privacy in networked control systems;
  • Sustainable and energy-efficient control solutions;
  • Data-driven controls in industry.

Dr. Wenfeng Hu
Dr. M. (Meichen) Guo
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

  • data-driven control
  • machine learning in control systems
  • networked control systems
  • adaptive control systems
  • real-time systems analytics
  • big data in control applications
  • sustainable and energy-efficient controls
  • security and privacy in data-driven systems

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Related Special Issue

Published Papers (3 papers)

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Research

37 pages, 8730 KB  
Article
Adaptive Data-Driven Control of Autonomous Underwater Vehicles: Bridging the Gap Between Simulation and Experimental Baseline via LSTM-MPC
by Ahmetcan Önal and Andaç Töre Şamiloğlu
Appl. Sci. 2026, 16(9), 4187; https://doi.org/10.3390/app16094187 - 24 Apr 2026
Viewed by 603
Abstract
This study proposes a robust data-driven control framework, LSTM-MPC, designed to enhance the velocity stabilization of Autonomous Underwater Vehicles (AUVs) operating under stochastic marine disturbances. Traditional control methods often struggle with the highly nonlinear and time-varying hydrodynamics of irregular waves. To address this, [...] Read more.
This study proposes a robust data-driven control framework, LSTM-MPC, designed to enhance the velocity stabilization of Autonomous Underwater Vehicles (AUVs) operating under stochastic marine disturbances. Traditional control methods often struggle with the highly nonlinear and time-varying hydrodynamics of irregular waves. To address this, we employ a Long Short-Term Memory (LSTM) recurrent neural network to capture complex temporal dependencies and provide accurate multi-step-ahead velocity predictions. These predictions are integrated into a Model Predictive Control (MPC) scheme, which optimizes control actions while respecting actuator constraints. A key contribution is the integration of an error-triggered online learning mechanism. Utilizing run-time weight synchronization via MATLAB Coder, the framework dynamically adapts to plant mismatches and high-frequency MEMS noise without an explicit analytical model. The architecture was validated using experimental data from a Pixhawk/ArduSub baseline. Results demonstrate that, under these stochastic conditions, the data-driven approach significantly outperforms the standard PID-based baseline. While adaptive PID variants offer improvements, the suggested framework drastically reduces tracking errors in rotational axes while maintaining high precision in translational velocities. This research confirms that adaptive, data-driven strategies can effectively bridge the gap between simulation and real-world deployment, offering a scalable solution for robust AUV autonomy in unpredictable environments. Full article
(This article belongs to the Special Issue Data-Driven Control System: Methods and Applications)
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29 pages, 6662 KB  
Article
Gradient vs. Non-Gradient-Based Model Free Control Algorithms: Analysis and Applications to Nonlinear Systems
by Andrei Baciu and Corneliu Lazar
Appl. Sci. 2025, 15(5), 2766; https://doi.org/10.3390/app15052766 - 4 Mar 2025
Cited by 2 | Viewed by 1772
Abstract
Against the background of the development of control systems, Data Driven Control (DDC) methods are becoming more and more popular, given the system’s independence from physical models and the possibility of quickly tuning the controller. The usefulness of such tuning algorithms increases with [...] Read more.
Against the background of the development of control systems, Data Driven Control (DDC) methods are becoming more and more popular, given the system’s independence from physical models and the possibility of quickly tuning the controller. The usefulness of such tuning algorithms increases with the complexity of the plants. Nonlinear models are the main class of processes for which such laws are amenable. According to the literature, a class of DDC methods exist that perform online estimation of plant behavior with an unknown structure, which is generically called Model Free. This title is assumed by two types of algorithms, which contain it in the name. One is the gradient-based algorithm, Model Free Adaptive Control, defined by Hou, which uses the concept of dynamic linearization through pseudo partial derivatives (PPD) and pseudo gradient (PG). The other is a non-gradient based algorithm, Model Free Control, defined by Fliess and Join, which uses the concept of the ultralocal model and intelligent PID controllers (iPID). For the gradient-based methods, in the compact form of dynamic linearization (CFDL), i.e., partial form dynamic linearization (PFDL), two algorithms are proposed to determine the initial value of the time-varying parameters PPD and PG from the dynamic performance perspective as they offer the best responses. The CFDL and PFDL variants of the MFAC control law, which have parameters that result from the application of the proposed algorithms, are compared with iP and iPD controllers on nonlinear control systems. Full article
(This article belongs to the Special Issue Data-Driven Control System: Methods and Applications)
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19 pages, 4589 KB  
Article
A Novel Robust Hybrid Control Strategy for a Quadrotor Trajectory Tracking Aided with Bioinspired Neural Dynamics
by Jianqi Li, Xin Li, Jianquan Lu, Binfang Cao and Jian Sun
Appl. Sci. 2024, 14(20), 9592; https://doi.org/10.3390/app14209592 - 21 Oct 2024
Cited by 5 | Viewed by 3080
Abstract
This paper introduces a novel hybrid control strategy for quadrotor UAVs inspired by neural dynamics. Our approach effectively addresses two common issues: the velocity jump problem in traditional backstepping control and the control signal chattering in conventional sliding mode control. The proposed system [...] Read more.
This paper introduces a novel hybrid control strategy for quadrotor UAVs inspired by neural dynamics. Our approach effectively addresses two common issues: the velocity jump problem in traditional backstepping control and the control signal chattering in conventional sliding mode control. The proposed system combines an outer-loop bioinspired backstepping controller with an inner-loop bioinspired sliding mode controller, ensuring smooth trajectory tracking even under external disturbances. We rigorously analyzed the system’s stability using Lyapunov stability theory. To validate our algorithm’s effectiveness, we conducted trajectory tracking experiments in both disturbance-free and step-disturbance conditions, comparing it with the traditional backstepping control, conventional sliding mode control, and saturated sliding mode control. The results demonstrate that our algorithm not only tracks trajectories more effectively but also significantly outperforms these methods in suppressing velocity jumps and signal chattering. Full article
(This article belongs to the Special Issue Data-Driven Control System: Methods and Applications)
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