CAREER: Learning for Real-Time Embedded Optimization

In today’s constantly changing world, cyber-physical systems must be able to safely react to changing conditions and unexpected disruptions by making high-quality decisions in real-time. Mathematical optimization offers a powerful and flexible tool to formulate and solve decision-making problems, with numerous applications in engineering, computer science, and operations research. However, applying optimization to real-time decision-making has always been limited by two fundamental challenges: ensuring robustness to uncertain problem parameters (e.g., energy demand, obstacle positions); and ensuring real-time algorithm convergence on embedded platforms with limited computational resources (e.g., embedded microcontrollers). This project seeks to address these challenges using machine learning to develop the next-generation real-time optimization tools to safely control cyber-physical systems. These results will lead to open-source software for embedded optimization, which will ease their application in a wide range of engineering disciplines (e.g., energy, transportation, and autonomous systems) where real-time optimization is considered too computationally expensive and unreliable. The research outcomes of this project will be integrated into a synergistic education plan to train future engineers by promoting optimization and computational thinking. These initiatives include the development of new learning units for undergraduate and graduate core courses in optimization; a new biennial workshop for graduate students on learning, decision, and control; and a new student-created podcast. To achieve these results, we will exploit the repetitive nature of real-time decision-making. In practical scenarios, we solve the same optimization problem repeatedly with different data. For example, control systems update the input actions as sensor signals (e.g., system state) and goals (e.g., desired trajectory) vary. The key insight of this project is to exploit this structure to develop specialized data-driven optimization tools that are reliable and efficient. (i) We will learn data-driven problem formulations with constraint satisfaction guarantees for decision-making under uncertainty. (ii) We will develop data-driven tools to design and numerically verify real-time optimization algorithms, with tight convergence guarantees. The embedded optimization techniques developed in this research will be validated through extensive real-world experiments on real-time embedded platforms for space exploration and unmanned aerial vehicles. 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.