CAREER: A Universal Framework for Safety-Aware Data-Driven Control and Estimation

该学院早期职业发展计划（Career）奖支持为机器人和电力系统提供安全数据驱动控制和估计方法的研究，从而促进科学进步，促进繁荣和福利，并确保国防安全。本研究将基于动态系统数学建模的最新进展，为控制策略和安全措施的同步设计开发一个通用框架。在这种情况下，安全、控制系统和数据驱动方法之间的交叉仍处于萌芽阶段，尽管数据处理能力取得了巨大进步，但采用和接受安全数据驱动范式仍有重大障碍需要克服。该项目将通过自动合成高度复杂系统的安全感知控制律（作为其实时输入输出信息的功能）来解决这些挑战。这项工作的成果将非常适合于工程、生物学和制造业的各种应用，这些应用对经济发展和国家在全球舞台上的竞争力具有重要意义。该项目的研究目标还辅之以有条理的行业外展和教学计划，旨在将研究与教育结合起来，加强行业合作，促进代表性不足的社区的参与，以造福整个社会。所研究的框架利用了一种新的系统表示框架的代数结构，该框架系统地编码其输入-输出行为，即Chen-Fliess框架。这种编码自然适合用于控制和估计的数据驱动范例的基础。通过消除对状态空间坐标框架的需求，这种方法本质上允许对系统信息使用代数优化例程，否则将需要过度参数化，从而导致不可行性。因此，在保持系统输入输出行为的前提下，研究了更快、更低功耗的优化算法。因此，控制障碍函数的可达性分析和综合将在这个代数框架下进行，以在数据驱动的环境中推进这些方法。该项目的具体目标是：(1)为数据驱动控制系统的分析和优化提供一个代数框架，(2)在Chen-Fliess框架中开发输入-输出可达性分析，(3)在Chen-Fliess框架中开发基于可达性分析和控制障碍函数的数据驱动方法，用于综合安全控制规律。研究工作将在自动驾驶汽车、执行同步定位和测绘的多机器人系统以及数据驱动的电力系统调节问题上得到验证。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development Program (CAREER) award supports research that enables safe data-driven control and estimation methods for robotics and power systems, thereby promoting the progress of science, advancing prosperity and welfare, and securing the national defense. This research will develop a universal framework for the simultaneous design of control policies and safety measures based on recent advances in the mathematical modeling of dynamical systems. In this context, the intersection between safety, control systems, and data-driven methodologies is still nascent and there are major hurdles to overcome for the adoption and acceptance of the safe data-driven paradigm despite the immense progress in data processing capabilities. This project will solve these challenges through the automatic synthesis of safety-aware control laws for highly complex systems as a function of their real-time input-output information. The outcomes of this work will be well-suited for a variety of applications in engineering, biology, and manufacturing that are of essential significance for the economic development and the competitiveness of the nation on the global stage. The project’s research objectives are complemented by a methodical industry outreach and pedagogical plan aimed at blending research and education, strengthening industry collaboration, and boosting the participation of underrepresented communities for the benefit of society at large.The researched framework leverages the algebraic structure of a novel framework for system representation that systematically encodes its input-output behavior, namely the Chen-Fliess framework. This encoding naturally fits the underpinnings of the data-driven paradigm for control and estimation. This methodology essentially enables the use of algebraic optimization routines on the system’s information, by eliminating the need for a state-space coordinate frame that otherwise will require over-parametrizations that can lead to infeasibility. Consequently, faster and less power-consuming optimization algorithms are researched with the capability of retaining the system’s input-output behavior. Thus, reachability analysis and synthesis of the control barrier functions will then be performed under this algebraic framework to advance these methodologies in the data-driven setting. The specific objectives of this project are to (1) provide an algebraic framework for the analysis and optimization of data-driven control systems, (2) develop input-output reachability analysis in the Chen-Fliess framework, and (3) develop data-driven methods for the synthesis of safe control laws based on reachability analysis and control barrier functions in the Chen-Fliess framework. The researched work will be validated on autonomous vehicles, a multi-robot system performing simultaneous localization and mapping, and a data-driven power system regulation problem.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.