This three-year $300,000 Project Grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering (47.041) federal grant program, funds research at New York University to advance the mathematical foundations and develop new tools for real-time distributed optimization-based control of large-scale nonlinear uncertain systems. Specifically, the award supports three research tasks: 1) synthesizing distributed optimization algorithms robust to uncertainties; 2) designing tracking controllers for local systems to minimize global costs in real time; and 3) integrating optimization and control algorithms for global convergence and closed-loop network stability. The researchers will validate the methodology using cooperative robotic networks. The deliverable is an integrated controller-optimizer co-design applicable to heretofore intractable networked nonlinear systems described by Euler-Lagrange equations. This work builds on the researchers' expertise in nonlinear control theory, robust estimation, and real-time optimization to enable concurrent optimization and control of complex engineering systems.