Dynamic Systems & Controls

Despite their growing sophistication, robotic systems still struggle to reliably handle the uncertain, open-world situations that emerge continually in our homes, cities, and roads.

When designing complex systems, we need to consider multiple trade-offs at various abstraction levels and scales, and choices of single components need to be studied jointly. For instance, the design of future mobility solutions (e.g., autonomous vehicles, micromobility) and the design of the mobility systems they enable are closely coupled.

Autonomous agents, whether on the ground or in the air, must navigate shared spaces, avoiding obstacles or coordinating within a group. Effective navigation in dynamic environments requires adaptive methods that ensure safety while achieving the agents' goals. In this talk I will present two of our latest results in multi-agent navigation: AVOCADO and Gen-Swarms.

Direct policy search has achieved great empirical success in reinforcement learning. Many recent studies have revisited its theoretical foundation for continuous control, which reveals elegant nonconvex geometry in various benchmark problems.

Our work is broadly motivated by the emergence of learning-based methods in control theory and robotics, with a specific focus on scenarios that have humans in-the-loop with control systems.

Machine learning is creating new paradigms and opportunities in the design of advanced process control systems for chemical processes.

This talk presents recent results on nonlinear observers (estimation algorithms) and their applications in motion estimation problems ranging from wearable sensors to autonomous vehicles.  First, a new observer design technique that integrates the classical high-gain observer with a novel LPV/LMI observer to provide significant advantages compared to both methods is presented.

Data-driven modeling typically involves simplifications of systems through dimensionality reduction (less variables) or through dimensionality enlargement (more variables, but simpler, perhaps linear, dynamics).  Autoencoders with narrow bottleneck layers are a typical approach to the former (allowing the discovery of dynamics taking place in a lower-dimensional manifold), while autoencoders with wide layers provi

Safety is a critical requirement for real-world systems, including autonomous vehicles, robots, power grids and more. Over the past decades, many methods have been developed for safety verification and safe control design in deterministic systems.

The development of extremum seeking (ES) has progressed, over the past hundred years, from static maps, to finite-dimensional dynamic systems, to networks of static and dynamic agents. Extensions from ODE dynamics to maps and agents that incorporate delays or even partial differential equations (PDEs) is the next natural step in that progression through ascending research challenges.