When stabilization and optimization meet: a codesign approach

下一代工程系统需要精确地执行复杂任务，并通过控制，优化，学习和计算的融合实现鲁棒性，弹性和适应环境的能力。许多技术，例如模型预测控制和强化学习，依赖于基于优化的控制算法和用于计算控制律的优化例程之间的复杂相互作用。该项目旨在为此类算法的稳定性，次优性和鲁棒性开发一个通用设计框架，可用于一系列新的应用，如无人驾驶汽车和无人机。

The next generation of engineered systems need to perform complex tasks with precision, and be robust, resilient and adaptive to their environment enabled by the confluence of control, optimization, learning and computation Understanding the interplay between robust stability and optimization is key to this endeavor. Many techniques, such as model predictive control and reinforcement learning, rely on an intricate interplay between an optimization-based control algorithm and an optimization routine used to calculate the control law. This project aims to develop a general design framework for stability, suboptimality and robustness of such algorithms, that can be used in range of novel applications, such as driverless cars and drones.