Nonlinear System Modelling and Control
A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".
Deadline for manuscript submissions: 30 April 2025 | Viewed by 549
Special Issue Editor
Interests: nonlinear system identification and control; machine learning and networked control systems; multi-agent systems; distributed control
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Modelling of nonlinear systems and control is a vital field in engineering and mathematics, aiming to understand, model, and manipulate systems exhibiting nonlinear behavior. Unlike linear systems, where outputs are directly proportional to inputs, nonlinear systems feature more complex dynamics, making them ubiquitous in real-world applications such as robotics, aerospace, energy applications, bioengineering, economics and many more. The process of system modelling/identification involves constructing mathematical models based on observed data. For nonlinear systems, this typically entails capturing the system’s input–output relationships and internal dynamics, which can exhibit characteristics such as saturation, hysteresis, and multi-stability. Techniques employed include nonlinear regression, neural networks, and differential equation modelling, often requiring advanced algorithms and significant computational resources. Once a nonlinear system is accurately identified, control strategies can be developed to achieve desired behaviors. Nonlinear control methods, such as feedback linearization, sliding mode control, and adaptive control, are designed to handle the intricacies of nonlinear dynamics, ensuring stability, performance, and robustness. In summary, modelling and control of nonlinear systems are essential for developing sophisticated technologies and enhancing system performance in complex environments, reflecting a crucial intersection of theoretical research and practical application.
Dr. Chathura Wanigasekara
Guest Editor
Manuscript Submission Information
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Keywords
- adaptive control
- robust control
- data-driven modelling
- sliding mode control
- model predictive control (MPC)
- fuzzy logic modelling
- neural network modelling
- networked control systems
- hybrid systems
- nonlinear observer design
- optimal control
- lyapunov-based modelling
- nonlinear system reduction
- time delay systems
- applications of nonlinear system modelling and control
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