The field of control systems is moving towards the development of more robust and safety-critical systems. Recent research has focused on probabilistic robust control approaches, which provide a framework for analyzing and designing systems that can operate effectively in the presence of uncertainty. Another key area of research is the development of control barrier functions, which provide a means of synthesizing safety filters that ensure safety in systems. Furthermore, the use of symbolic control techniques and continuously parametrized control barrier functions are also being explored to provide more flexible and adaptive control systems.

Notable papers in this area include:

• 'Probabilistic Robustness in the Gap Metric', which utilizes the gap metric to gauge the robustness of a controller in the presence of stochastic uncertainties.
• 'Inverse Optimal Control with Constraint Relaxation', which leverages the concept of exact penalty functions to improve the estimation accuracy of inverse optimal control methods.
• 'On the Properties of Optimal-Decay Control Barrier Functions', which formalizes the process of choosing the class K function in control barrier functions using optimal-decay control barrier functions.
• 'Invariance Guarantees using Continuously Parametrized Control Barrier Functions', which introduces a safe control framework utilizing continuously parametrized control barrier functions to provide invariance guarantees for safety-critical systems.