The field of control systems is moving towards the development of more robust and adaptive control strategies, with a focus on handling uncertainties and nonlinear dynamics. Recent work has explored the use of integral quadratic constraints (IQCs) to model and synthesize robust controllers for discrete-time systems, as well as the development of disturbance-adaptive model predictive control (MPC) frameworks. These advances have the potential to improve the performance and reliability of control systems in a wide range of applications. Notable papers in this area include:

• A novel framework for multi-objective robust controller synthesis using IQCs, which can minimize closed-loop performance measures such as the H∞-norm and energy-to-peak gain.
• A disturbance-adaptive MPC framework that adjusts the disturbance model based on measured constraint violations, ensuring recursive feasibility and asymptotic bounds on average constraint violations.
• A probabilistic extension to Pontryagin's maximum principle, which minimizes the mean Hamiltonian with respect to epistemic uncertainty and provides a principled framework for controlling uncertain systems with learned dynamics.