Nonlinear Control

Nonlinear control focuses on designing controllers for systems whose dynamics are not linearly related to their inputs, aiming for stable and efficient operation despite inherent complexities. Current research emphasizes robust control strategies in the face of uncertainties and disturbances, often employing neural networks (including recurrent equilibrium networks and deep Koopman operators), Lyapunov-based methods, and model predictive control techniques. These advancements are crucial for improving the performance and safety of diverse applications, ranging from robotics and autonomous vehicles to aerospace systems and biomedical devices.