Uncertainty-Aware Control: Theory, Algorithms, and Applications

ECC 2026 Workshop

Control systems are increasingly deployed in high-uncertainty, high-stakes applications such as autonomous vehicles, robotics, and energy systems. As a result, uncertainty-aware control algorithms have gained significant interest, as they explicitly account for disturbances, model mismatch, and varying environments, enabling principled trade-offs between robustness and performance while ensuring reliability and efficiency.

Motivated by the scale and complexity of modern applications, recent research has focused on extending these methods to increasingly challenging settings, including nonlinear and high-dimensional systems and risk-averse as well as distributionally robust problems, and on developing computationally efficient algorithms capable of real-time operation.

At the same time, advances in learning-based control raise new questions about how uncertainty should be modeled, propagated, and exploited in safety-critical control systems.

This workshop brings together researchers working on the theory, algorithms, and applications of uncertainty-aware control to discuss recent advances, current limitations, open challenges, and promising directions for future research.

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Invited Speakers

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Organizers

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Topics

The workshop focuses on uncertainty-aware methods for decision making and control, including:

• Robust, stochastic, risk-averse, and distributionally robust control
• Optimal control and model predictive control
• Reachability analysis
• Learning-based uncertainty modeling (Gaussian processes, Bayesian deep learning, diffusion models)
• Learning-based control and reinforcement learning
• Data-driven model predictive control
• Applications in robotics, autonomous systems, and energy systems

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Schedule