The field of control systems is witnessing a significant shift towards the integration of neural networks and machine learning techniques to enhance stability and performance. Recent developments have focused on designing controllers that can guarantee stability and stabilization of unknown or uncertain systems, particularly in continuous-time domains. The use of neural networks to parametrize control Lyapunov functions and the development of novel neural ordinary differential equation (ODE) controllers are notable advancements in this area. These approaches have shown promise in providing stability guarantees for mechanical systems and other complex dynamics. Furthermore, data-driven control methods have emerged as a viable alternative for stabilizing unknown linear-threshold network dynamics. The development of adaptive neural control schemes with desired approximation properties has also improved the tracking performance of control systems. Noteworthy papers in this area include:

• Neural Incremental Input-to-State Stable Control Lyapunov Functions for Unknown Continuous-time Systems, which introduces a novel control Lyapunov function for unknown continuous-time systems.
• Negative Imaginary Neural ODEs: Learning to Control Mechanical Systems with Stability Guarantees, which proposes a neural control method for mechanical systems with stability guarantees.