Advances in Nonlinear Control Theory Applied to Dynamic Systems
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 10 July 2025 | Viewed by 652
Special Issue Editors
Interests: nonlinear control; robust control; parametric uncertainties; sliding mode control; adaptive control; machine learning; stability analysis; mathematical modeling; dynamical systems; numerical simulation; real-time simulation
Special Issues, Collections and Topics in MDPI journals
Interests: nonlinear control; robust control; parametric uncertainties; sliding mode control; adaptive control; machine learning; stability analysis; mathematical modeling; dynamical systems; numerical simulation; real-time simulation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
During the last decade, the recent advancements in nonlinear control theory have provided innovative solutions for effectively managing the inherent complexities of dynamic systems. It is often the case that classical linear control methodologies prove inadequate when confronted with systems exhibiting nonlinear characteristics, parametric uncertainties, and external disturbances.
New advances in nonlinear control theory address these challenges by developing advanced techniques such as sliding mode control, adaptive control, robust control, and other nonlinear control techniques. These methods permit their application in dynamic systems spanning diverse sectors, including robotics, aerospace, electric vehicles, drones, and biological systems, among many others where precise control is critical to system performance and safety. Furthermore, developments in computational tools have enhanced the capacity to model, simulate, and implement nonlinear control strategies thus facilitating the design of more efficient and robust dynamic systems. There is a growing tendency to integrate machine learning algorithms and artificial intelligence with nonlinear control in order to enhance system adaptability and optimize real-time performance. These advances underscore the significance of nonlinear control theory in shaping the future of dynamic systems, enabling them to operate with greater reliability in uncertain environments.
Prof. Dr. Cuauhtemoc Acosta Lua
Dr. Stefano Di Gennaro
Guest Editors
Manuscript Submission Information
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Keywords
- nonlinear control
- robust control
- parametric uncertainties
- sliding mode control
- adaptive control
- machine learning
- stability analysis
- mathematical modeling
- dynamical systems
- numerical simulation
- real-time simulation
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