Control meets AI: Automation, Autonomy, and Intelligent Machines
Schedule: Day 1 – Tuesday 23 June 2026
Location: G41, Frederick Douglass Centre, Newcastle
It is not an exaggeration to say that we are witnessing dramatic developments in machine learning and artificial intelligence technologies. Control theory has had and continues to have common goals and intersections with machine learning and artificial intelligence. A major intellectual and technological challenge for the future is how we can the best of what these various fields can offer. In this talk, I will address this question in the setting of cyber-physical systems (CPS). I will describe notions of cognitive CPS as well as Physical AI. I will discuss how we may think about concepts of automation, autonomy, and they can offer guidelines on the future of intelligent machines.
Stabilization of Positive Systems with Positive Inputs: The Craft of Asymmetry
Schedule: Day 2 – Wednesday 24 June 2026
Location: G41, Frederick Douglass Centre, Newcastle
Biopopulations, chemical networks, energy storage, traffic densities, thermodynamics – these are systems with positive states and controls. Positive systems operate subject to their “one-sides logic.” Norms give way to highly asymmetric measures, CLF conditions are entirely unlike Sontag’s, feedback has to encode both aggressiveness and caution, with optimal costs on control and state capturing such a “bipolar”/asymmetric nature. I will present universal recipes for both stabilization and inverse optimal (HJB PDE-free) control. Not one linear system will appear in the talk as Brockett’s condition rules out the possibility of stabilizability of linear systems with positive inputs on the positive orthant for the states. Predator-prey dynamics will assist with elucidation of the general concepts.
Ontological Robustness for Certification of Autonomous Systems
Schedule: Day 3 – Thursday 25 June 2026
Location: G41, Frederick Douglass Centre, Newcastle
Learning-based control paradigms have seen many success stories with autonomous systems in recent years. A typical architecture in these systems involves layers for perception, planning and control, wherein each of these layers uses different tools and metrics for assessing robustness and performance. For example, the planners — that use vision-based sensors to update the navigation and motion planning — operate largely relying on distributionally robust stochastic optimal control, whereas the low-level controller can be a deterministic controller with its conventional gain and phase (time-delay) margin. We present a new analysis framework for addressing this ontology challenge inherent to autonomous systems. We derive distributional robustness guarantees for deterministic L1 adaptive controllers that can be used by any stochastic planner without facing a language barrier. The combined planner-controller framework can serve as foundation for development of certificates for V&V of learning-enabled systems. An overview of different projects at our lab that build upon this framework will be demonstrated to show different applications.