The University of California, San Diego will receive $603,100 from the National Science Foundation under the Engineering (47.041) federal grant program to develop model-free fixed-time equilibrium-seeking controllers for decision-making tasks in connected autonomous systems. The three-year project aims to advance the design and analysis of optimization algorithms that can solve decision problems in real-time using only current information, without relying on system models. The university will synthesize new classes of feedback controllers guaranteed to achieve prescribed-time convergence for constrained variational inequalities governing multi-objective decision making. Researchers will test the algorithms in applications including traffic congestion control and coordinated motion of connected mobile robots. The funding supports collaborations with industry to inform algorithm development based on practical computational and network constraints. Results will be integrated into courses and disseminated through workshops to cultivate workforce skills for national technology needs.
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