Advanced Nonlinear and Learning-Based Control Techniques for Complex Dynamical Systems, 2nd Edition
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".
Deadline for manuscript submissions: 15 January 2025 | Viewed by 2587
Special Issue Editors
Interests: thermoacustics; synthetic jet actuators; flow-induced-noise control; marine vehicle control; flow control
Special Issues, Collections and Topics in MDPI journals
Interests: real-time learning-based control; machine learning; multi-agent systems; control & systems theory; robotics
Special Issues, Collections and Topics in MDPI journals
Interests: real-time optimization-based control and estimation methods, nonlinear control, and machine learning, with special emphasis on foundational theory and experimental realization on robotic and autonomous systems
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:
- 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
Dr. Erkan Kayacan
Guest Editors
Manuscript Submission Information
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Keywords
- optimization
- observer design
- adaptive control
- learning control
- intelligent control
- robust control
- formation control
- geometric control
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