CAREER: Data-Driven Control of Dynamical Networks: Robustness, Risk, and Network Architectures

Recent spectacular technological advances and unprecedented availability of data are providing opportunities to fundamentally reimagine the control and information architectures and algorithms for emerging complex networked systems, such as autonomous robot teams for transport

and delivery and energy grids with massive renewable penetration. However, these networks are posing major challenges for safe, efficient, and robust operation and control. Their accelerating complexity, especially from rapid integration of machine learning components, threatens to outpace our understanding of their robustness properties and lead to severe failure and safety risks.

This project will build a rigorous framework for analysis and design of robust, risk-constrained data-driven control algorithms and architectures for dynamical networks. The proposed innovations will have broad societal impact by enabling enhancements to the safety, efficiency and robustness of various emerging complex networks that are vital to future society.

The application focus will be on autonomous energy grids and distributed multi-robot teams, but the fundamental knowledge advancements can also impact other emerging critical infrastructure networks. Furthermore, the integrated research and education plan features an extensive array of activities that will train scientists and engineers and promote public understanding of data-driven control in dynamical networks, especially around issues of robustness, risk, and safety.

This project will significantly advance knowledge via a transformative robust and risk-aware integration of model-based control and data-based learning approaches that explicitly incorporate uncertainty from finite-data estimates. The overall approach will combine performance and robust-

ness guarantees from model-based stochastic control, dynamic game theory, and risk-based distributionally robust optimization with modern non-asymptotic martingale concentration bounds and bootstrap techniques from statistics. Robustness to uncertainties in finite-data model estimates will be explicitly incorporated in two innovative ways: (1) a rich stochastic dynamic game framework that combines adversarial inputs and multiplicative noise to promote robustness to both highly-structured parametric and non-parametric uncertainties; (2) the use of axiomatic risk theory and distributionally robust optimization to guarantee meaningful risk-based safety constraints.

On this basis, the project will develop techniques for fully data-driven dynamic output feedback control, active exploration, and locally optimal, robust, and provable convergent algorithms for nonlinear systems using iterative stochastic dynamic games with locally learned and refined models. The proposed research will also advance knowledge by developing innovative self-tuning architectures and regularized policy optimization algorithms for network control architecture design, and illuminating how underlying network structure and size impose fundamental limits on data-driven control and estimation.

The approach will be illustrated, refined, and validated via numerical simulations and hardware testbed experiments for autonomous energy grids and distributed heterogeneous multi-robot teams. A complementary education plan will: (1) create interactive exhibitions via extensive educational outreach programs at the Perot Museum of Nature & Science and UT Dallas; (2) enhance curriculum for dynamical networks with new courses, interactive web technologies, and project-based learning; (3) mentor senior capstone design projects and undergraduate researchers that directly support the proposed research.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.