Machine Learning and Data-Driven Methods for the Control of Nonlinear Dynamical Systems
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 185
Special Issue Editor
Special Issue Information
Dear Colleagues,
Modern engineering systems are becoming increasingly complex due to the integration of components and technologies such as IoT sensors, advanced mechanisms, predictive maintenance, digital twins, and physical AI. These systems often exhibit high-dimensional nonlinear dynamics, uncertainties, and interactions with changing environments, which make the conventional model-based controller design approaches difficult to apply or inefficient for achieving high levels of performance. Machine-learning-based and data-driven modeling and control methods may be able to provide effective solutions to these challenging problems.
The aim of this Special Issue is to highlight the recent advances in machine-learning-based and data-driven methods for modeling and control of dynamical systems with nonlinearities, uncertainties, large numbers of inputs/outputs, or interactions with changing environments. The goal is to bring together the recent theoretical developments and modern applications in these areas.
In this Special Issue, original research articles and reviews are welcome for submission. Research areas may include (but are not limited to) the following:
- Data-driven control: safe learning control, data-driven model predictive control, Data-Enabled Predictive Control (DeePC);
- Data-driven modeling: behavioral systems theory and Willem’s fundamental lemma, data-driven Koopman methods, universal differential equations, Extended Dynamic Mode Decomposition (eDMD), Sparse Identification of Nonlinear Dynamics (SINDy), Physics-Informed Neural Networks (PINNs);
- Robotics applications: soft robotics and actuators, wearable robotics, human–robot collaboration (HRI), biohybrid robots, power and energy efficiency considerations in robotics;
- Mechatronics applications: micro/nano electromechanical systems, advanced manufacturing.
Dr. Farshad Merrikh-Bayat
Guest Editor
Manuscript Submission Information
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Keywords
- data-driven control
- machine-learning-based techniques
- nonlinear dynamical system
- data-enabled predictive control
- mechatronics
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