The field of control systems is witnessing significant developments, with a focus on innovative methods for efficient resource usage, robust stability, and optimal control. Researchers are exploring data-driven approaches, such as Koopman operator theory and meta-learning, to improve the performance of control systems. Event-triggered control strategies are being designed to reduce communication instances, while online optimal parameter compensation methods are being developed to ensure robust stability in nonlinear systems. Additionally, quadratic control frameworks and model predictive control are being applied to dynamic systems to achieve trajectory tracking and adapt to changing conditions. Noteworthy papers include:

• Koopman-Based Event-Triggered Control from Data, which introduces a novel approach to event-triggered control for discrete-time nonlinear systems.
• Online Optimal Parameter Compensation method of High-dimensional PID Controller for Robust stability, which provides a condition for online dynamic parameter regulation to ensure robust stability.
• No-Regret Model Predictive Control with Online Learning of Koopman Operators, which studies a problem of simultaneous system identification and model predictive control of nonlinear systems.
• Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving, which proposes a novel framework that combines a Kalman filter-based online adaptation scheme with meta-learned parameters.