Workshops Listing

This proposed CDC workshop is inspired by a fabulous Workshop on Robust Control for Keith Glover’s 80th birthday held in Cambridge, England on April 25, 2026. It reviewed the past 50 years of exceptional progress in robust control theory; its application throughout technology, biology, medicine, and science; what new challenges we face in the next 50 years in theory and diverse applications; and particularly a need for Robust AI and Robust Architecture as rigorous and relevant as Robust Control has been.

Traffic signal control optimization is increasingly critical as growing vehicle volumes, mixed fleet compositions, and constrained road infrastructure strain urban mobility networks across the United States.

This one-day workshop brings together researchers to review and celebrate the remarkable progress in stochastic control and systems theory over the past few decades, assess the current state of the field, and look into the challenges and opportunities that will shape its future. We will invite a number of well-known experts, who are veterans in this field; many of them are IEEE life members to give presentations. There will be plenty time for discussions and exchange of ideas during the workshop.

The workshop’s primary objective is to create a platform for connecting various communities, fostering collaboration for the advancement of a compositional theory of control architectures. This workshop is open to participants from diverse backgrounds and experience levels, catering to both newcomers and seasoned professionals.

Preview and lookahead information have long played a central role in control, in which the predicted quantity is internal to the controller, such as predictive models (e.g., model predictive control), value functions approximations (e.g., approximate dynamic programming), and internal output predictors (e.g., internal model control).

Stochastic optimization problems are ubiquitous in control, machine learning, and game theory, yet standard approaches often rely on precise probabilistic descriptions of the uncertainty that are rarely available in practice.

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Autonomous systems are increasingly being deployed in environments where they must interact with each other, with humans, and with the physical world. This is the case, for example, when considering self-driving cars in a city environment, drones coordinating deliveries, or robotized warehouses.

Recent advances in generative AI have led to remarkable empirical success, but their theoretical understanding remains limited, particularly with respect to efficiency, reliability, and controllability.

AI is increasingly embedded in systems that make consequential decisions, not as a research prototype or a demonstration, but rather in deployment. Learned components manage complex systems under uncertainty, and large language models (LLMs) serve as reasoning and decision layers in systems that interact with the real world.

The increasing availability of data has made data-driven methods a cornerstone of modern control design. Yet bridging the gap between laboratory performance and field reliability remains one of the central challenges facing the control community today.

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The explosive growth of artificial intelligence (AI) workloads is reshaping electric power systems at an unprecedented pace. Hyperscale and AI-focused data centers, with single-site loads now reaching hundreds of megawatts, are placing extraordinary new demands on transmission and distribution networks, electricity markets, and decarbonization roadmaps.

Bridging Theory, Data, and Industrial Practice

Process control is entering a period of rapid transformation driven by advances in artificial intelligence, the decarbonization of industrial systems, and the growing autonomy of complex processes.

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The growing use of robots in safety-critical settings creates a pressing need for control methods that remain reliable under model errors, changing conditions, and uncertain interactions.

Space systems have become critical infrastructure for global communication, Earth observation, and scientific discovery. Satellite constellations, now comprising thousands of spacecraft, are projected to reach tens of thousands in the coming decade due to rapid commercialization.

Increasing stress on our natural resources, coupled with rapid automation and digitalization of our societies, is leading to daunting societal challenges.

The objective of the workshop is to bring together researchers to share and discuss key challenges, recent advances and emerging applications of guidance, navigation and control (GNC) and autonomy for space domain spanning LEO to cislunar/lunar operations to deep space missions.

The deployment of autonomous systems in the real world, such as self-driving vehicles and unmanned aerial systems, requires rigorous methods for verifying and reasoning about whether a system can maintain safety, broadly understood as the ability to enforce constraints on system behaviors.