The field of nonlinear systems control is moving towards more data-driven approaches, leveraging collected measurements to construct robust and adaptive control strategies. This shift enables the handling of systems with unknown or uncertain models, ensuring stability and performance under noisy and constrained conditions. Noteworthy papers include:

• Data-Driven Computation of Polytopic Invariant Sets for Noisy Nonlinear Systems, which presents a framework for computing contractive sets using noisy measurements.
• Event-triggered control of nonlinear systems from data, which extends data-based event-triggered control designs to nonlinear systems.
• Necessary and Sufficient Conditions for PID Design of MIMO Nonlinear Systems, which provides a rigorous design theory for PID control of nonlinear uncertain MIMO systems.
• Physics-Based Communication Compression via Lyapunov-Weighted Event-Triggered Control, which proposes a directional triggering mechanism to reduce communication overhead in networked systems.