Collaborative Research: CPS: Medium: Constraint Aware Planning and Control for Cyber-Physical Systems

协作研究：CPS：中：网络物理系统的约束感知规划和控制

• 批准号: 2039054
• 负责人: Ricardo Sanfelice
• 金额: $ 60万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039054&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Medium; Constraint

The objective of this work is to generate new fundamental science for computer controlled complex physical systems, a broad class of cyber-physical systems (CPS) and demonstrate this science in aerial vehicles and walking robots. The new science enables autonomous planning and control in the presence of failures and abrupt changes in system variables. A framework for the design of algorithms that exploit awareness of the physical and design constraints to autonomously self-adapt their motion plan and control actions will be generated. The approach exploits elements from geometry, adaptive control, and hybrid control to advance the knowledge on modeling, planning, and design of CPS with constraints, non-smooth, and intertwined continuous and discrete dynamics. Unlike current approaches, which separate the task associated with planning the motion from the design of the algorithm used for control, the algorithms to emerge from this project self-learn and self-adapt in real time to cope with unexpected changes in motion and specification constraints so as to enable autonomous systems to perform robustly and safely, and degrade gracefully under failure conditions. Specifically, the new algorithms will learn and monitor the physical and design constraints in real time and adapt both planner and controller by selecting the appropriate constraints to enforce, with robustness and safety guarantees. The capabilities of the new tools will be demonstrated on multi-legged robots in harsh environments that make them prone to failures, and on aerial vehicles in contested/adversarial environments.The proposed plan contributes to Science of Cyber-Physical Systems by addressing modeling, motion planning, and design of CPS with constraints, non-smooth, and intertwined continuous and discrete dynamics. The merits of the proposal fall into four broad categories: (i) a framework to mathematically formulate learning-based planning and control for CPS with awareness of its constraints, (ii) novel architectures that lead to robust adaptive constraint satisfaction, (iii) deep understanding of roles and priorities of system constraints in CPS, and (iv) tools and design techniques that permit engineers to deploy constraint aware algorithms. The results of this work are broad in their application to all kinds of CPS that require planning and control, in particular, autonomous systems in transportation (air and ground). Synergistic collaborations with researchers at Samsung, the start-up Ghost Robotics, and at the University of Bologna are instrumental in disseminating the application of our results to industry and academia. A synergistic outreach program at UCSC and the University of Michigan impacts high school students and teachers.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.

这项工作的目的是为计算机控制的复杂物理系统，一类广泛的网络物理系统（CPS）生成新的基本科学，并在飞机和步行机器人中演示了这项科学。 新科学在存在故障和系统变量突然变化的情况下实现了自主计划和控制。 设计算法的设计框架将产生对物理和设计约束的认识以自主自动化其运动计划和控制动作的意识。 该方法利用了从几何，自适应控制和混合控制中利用元素，以促进对CP的建模，计划和设计的知识，并具有约束，非平滑和相互交织的连续和离散的动态。 与当前的方法不同，该方法将与计划运动相关的任务与用于控制的算法的设计相关的任务不同，该算法是从该项目自学和实时实时从该项目中出现的算法，以应对运动和规范限制的意外变化，从而使自治系统启用自动源系统，以稳健，安全地稳固，并在处于良好的失败状态下进行脱颖而出。 具体而言，新算法将实时学习和监视物理和设计约束，并通过选择适当的约束来使用稳健性和安全保证来适应计划者和控制器。 新工具的功能将在恶劣环境中的多腿机器人上展示，这使它们容易发生故障，并在有争议/对抗性环境中的飞机上进行飞机。拟议的计划通过通过约束，非平衡和插入式的互动和插入性的互动的CPS来解决CPS的模型，运动计划和设计，从而为网络物理系统的科学解决CPS的科学。该提案的优点分为四个广泛的类别：（i）数学上对CP进行基于学习的计划和控制的框架，并意识到其约束，（ii）新型体系结构，可导致强大的适应性约束满意度，（iii）对CPS和（IV）工具技术的最深刻理解和（IV）工具技术的效果和优先级的深刻理解。这项工作的结果在应用于需要计划和控制的各种CP，特别是运输中的自治系统（空气和地面）方面广泛。与三星的研究人员，启动幽灵机器人技术以及博洛尼亚大学的协同合作有助于传播我们的结果在行业和学术界的应用。 UCSC和密歇根大学的协同宣传计划影响了高中生和教师。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

项目成果

Robust Finite-Time Parameter Estimation for Linear Dynamical Systems

线性动力系统的鲁棒有限时间参数估计

• DOI: 10.1109/cdc45484.2021.9683268
• 发表时间: 2021
• 期刊: Proceedings of the 60th IEEE Conference on Decision and Control
• 作者: Johnson, R.;Saoud, A.;Sanfelice, R.
• 通讯作者: Sanfelice, R.

Hybrid Concurrent Learning for Hybrid Linear Regression

混合线性回归的混合并发学习

• DOI: 10.1109/cdc51059.2022.9992473
• 发表时间: 2022
• 期刊: 2022 IEEE 61st Conference on Decision and Control
• 作者: Johnson, Ryan S.;Sanfelice, Ricardo G.
• 通讯作者: Sanfelice, Ricardo G.

Uniting Nesterov’s Accelerated Gradient Descent and the Heavy Ball Method for Strongly Convex Functions with Exponential Convergence Rate

结合 Nesterov 的加速梯度下降法和重球法来求解具有指数收敛率的强凸函数

• 发表时间: 2021
• 期刊: Proceedings of the American Control Conference
• 作者: Hustig-Schultz, D.;Sanfelice, R.G.
• 通讯作者: Sanfelice, R.G.

A Self-Triggered Control Strategy to Guarantee Forward Invariance

保证前向不变性的自触发控制策略

• 发表时间: 2021
• 期刊: Proceedings of the American Control Conference
• 作者: Kooi, D.;Sanfelice, R.G.
• 通讯作者: Sanfelice, R.G.

Robust Output Feedback Control Design in the Presence of Sporadic Measurements

存在零星测量时的鲁棒输出反馈控制设计

• DOI: 10.1109/tac.2022.3213616
• 发表时间: 2023
• 期刊: IEEE Transactions on Automatic Control
• 影响因子: 6.8
• 作者: Merco, Roberto;Ferrante, Francesco;Sanfelice, Ricardo G.;Pisu, Pierluigi
• 通讯作者: Pisu, Pierluigi