The field of control and modeling of complex systems is witnessing significant advancements, with a focus on developing innovative methods to address the challenges posed by uncertainty, non-linearity, and interconnectedness. Researchers are exploring new approaches to control and modeling, such as robust control architectures, distributed formation control protocols, and adaptive event-triggered schemes, to improve the performance and stability of complex systems. Notably, the development of hypernetworks for adaptive and generalizable forecasting is enabling smooth transitions across parameterized system behaviors, facilitating a unified model that captures dynamic behavior across a broad range of system parameterizations. Furthermore, distributed Lyapunov functions are being used to characterize high-dimensional systems with non-convex regions of attraction, providing accurate convex approximations of both volumes and shapes. Some noteworthy papers in this area include:

• Full-Pose Tracking via Robust Control for Over-Actuated Multirotors, which proposes a robust cascaded control architecture for over-actuated multirotors, enabling full-pose tracking and effectively addressing key challenges such as preventing infeasible pose references and enhancing robustness against disturbances.
• Ground-Effect-Aware Modeling and Control for Multicopters, which presents a control method that combines dynamic inverse and disturbance models to mitigate the influence of ground effect on multicopter control, reducing control error by 45.3%.
• Beyond Static Models: Hypernetworks for Adaptive and Generalizable Forecasting in Complex Parametric Dynamical Systems, which introduces the Parametric Hypernetwork for Learning Interpolated Networks, a framework that enables smooth transitions across parameterized system behaviors, facilitating a unified model that captures dynamic behavior across a broad range of system parameterizations.