Stability and Control of Nonlinear Systems

Dear Colleagues,

Nonlinear systems are ubiquitous in modern engineering, ranging from power electronics and renewable energy systems to autonomous vehicles and industrial automation. The stability and control of these systems present significant challenges due to their inherent complexity, time-varying dynamics, and susceptibility to uncertainties and disturbances. Unlike linear systems, nonlinear systems exhibit diverse behaviors such as bifurcations, chaos, and multiple equilibria, necessitating advanced theoretical frameworks and control strategies to ensure reliable performance.

Traditional control approaches for nonlinear systems often rely on Lyapunov stability theory and adaptive techniques. However, these methods may not adequately address the stringent performance requirements of contemporary applications, such as fast transient response, robustness to uncertainties, and computational efficiency. The emergence of data-driven methods, artificial intelligence, and machine learning has opened new avenues for controlling complex nonlinear systems, offering promising solutions where model-based approaches fall short. Moreover, finite-time and fixed-time control strategies have gained prominence for applications requiring guaranteed convergence within prescribed time bounds, such as robotic manipulation, spacecraft attitude control, and microgrid synchronization.

Recent advances in sliding mode control, model predictive control, and observer-based techniques have further enhanced our ability to stabilize and control nonlinear systems under various constraints. The integration of event-triggered mechanisms and sampled-data control has also contributed to resource-efficient implementations in networked control systems. This Special Issue aims to provide a comprehensive platform for disseminating cutting-edge research that bridges theoretical developments with practical applications in stability analysis and control of nonlinear systems.

Topics of interest include, but are not limited to, the following:

• Stability analysis techniques for nonlinear systems (Lyapunov methods, finite/fixed/predefined-time stability);
• Advanced control strategies (sliding mode control, adaptive control, model predictive control, backstepping control);
• Data-driven, AI-based, and machine learning control approaches for nonlinear systems;
• Neural network-based control and reinforcement learning applications;
• Bifurcation analysis and chaos control in nonlinear dynamical systems;
• Robust and fault-tolerant control design under uncertainties;
• Observer design and state estimation for nonlinear systems;
• Event-triggered control and sampled-data control strategies;
• Applications in power electronics, renewable energy systems, microgrids, and smart grids;
• Applications in robotics, autonomous vehicles, and industrial automation;
• Synchronization and consensus control of networked nonlinear systems.

We look forward to receiving your valuable contributions.

Dr. Imran Ghous
Dr. Jahanzeb Akhtar
Guest Editors

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