Collaborative Research: EPCN: Distributed Optimization-based Control of Large-Scale Nonlinear Systems with Uncertainties and Application to Robotic Networks

合作研究：EPCN：基于分布式优化的大型不确定性非线性系统控制及其在机器人网络中的应用

• 批准号: 2210320
• 负责人: Zhong-Ping Jiang
• 金额: $ 30万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210320&HistoricalAwards=false
• 关键词: Collaborative; Research; EPCN; Distributed; Optimization

Control and optimization need to be conducted simultaneously in numerous applications: smart grids, transportation networks, cooperative robotics, healthcare, and other autonomous systems interacting via wireless or physically-linked communications. These two tasks are typically treated distinctly, approached by independent designs. As a result, the two tasks interfere with one another and require performance compromises in at least of the two. For instance, optimality is obtained, but slowly, or convergence is rapid, but to suboptimal motions. A deep integration of control and optimization holds great promise. The integration is made difficult by the surge in complexity of contemporary control systems, reflected in the dynamic order, model uncertainty, and unreliable networking. The key challenge for concurrently running the mutually interfering optimization and control is the stability of the overall system or, if stability is ensured, the convergence rate. The control-optimization interference has been the hallmark of both classical adaptive control (controller-estimator interference) and extremum seeking (optimizer-controller interference), which are special cases of concurrent control and optimization. This project will advance the mathematical foundations of distributed optimization-based control and develop new tools and methods for real-time distributed optimization-based control design of large-scale and nonlinear uncertain systems. The methodology will be validated by means of cooperative robotic networks.The tools developed in this project, for real-time distributed optimization-based control algorithms for large-scale nonlinear systems with uncertainties, are of transformative nature. The algorithms designed will be applicable to heretofore intractable large-scale systems, including uncertain networked nonlinear systems and robotic networks described by Euler-Lagrange equations. To de-conflict the entanglement of optimization and control, the PIs pursue three research tasks: (1) the synthesis of distributed optimization algorithms that are robust to uncertainties, (2) the design of tracking controllers for each local system to follow in real time the desired output that aims to globally minimize certain global cost, and (3) the integration of optimization and control algorithms for global convergence of optimization algorithms and stability of the closed-loop network. The project builds on the PIs’ foundational contributions in nonlinear small-gain theory, fortified uncertainty-attenuating controllers and estimators for modular adaptive control design, and on their complementary skillsets in learning-based control and in real-time optimization by extremum seeking. The deliverable is a controller-optimizer co-design with a greatly enlarged applicability, in terms of the generality of the nonlinear plants and the achieved robustness and adaptivity, as compared to current methods which rely on linearly-bounded interactions among the modules.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.

控制和优化只需在许多应用中进行：智能电网，运输网络，合作机器人技术，医疗保健以及其他通过无线或物理链接的通信进行交互的自主系统。这两个任务通常通过独立设计进行明显处理。结果，这两个任务彼此干扰，并且至少在两者中都需要绩效妥协。例如，获得最佳性，但缓慢或收敛速度很快，但要屈服于次优的运动。控制和优化的深层整合具有巨大的希望。当代控制系统的复杂性激增使集成变得困难，这反映在动态顺序，模型不确定性和不可靠的网络中。同时运行相互干扰优化和控制的主要挑战是整体系统的稳定性，或者，如果确保稳定性，则收敛速率。控制性优化干扰一直是经典自适应控制（控制器 - 估计器干扰）和超级寻求（优化器控制器干扰）的标志，这是并发控制和优化的特殊情况。该项目将推动基于分布式优化的控制的数学基础，并开发新的工具和方法，用于实时分布式优化的大规模和非线性不确定系统的控制设计。该项目中开发的工具用于实时分布式优化的控制算法，用于具有不确定性的大规模非线性系统，具有变革性的性质。设计的算法将适用于迄今为止棘手的大规模系统，包括不确定的网络非线性系统和Euler-Lagrange方程描述的机器人网络。为了消除对优化和控制的纠缠，PI购买了三个研究任务：（1）分布式优化算法的合成，对不确定性具有可靠性，（2）每个本地系统的跟踪控制器的设计，以实时遵循的每个本地系统的设计，旨在实时的输出，旨在全球化的整体成本和（3）的整体成本和（3）的整合性和（3）的整合性，以及（3）和闭环网络的稳定性。该项目以非线性小增益理论的基本贡献为基础，强化了模块化自适应控制设计的不确定性侵入控制器和估计器，以及其在基于学习的控制方面的完整技能以及实时寻求的实时优化。与当前的方法相比，可交付的可交付者是一种控制型射击器共同设计，其适用性大大提高，其一般性以及实现的鲁棒性和适应性，与当前依赖于模块线性互动的方法相比，该奖项之间的奖励是NSF的法定任务，反映了通过评估的诚实的依据，该奖项已被认为是诚实的构成者的范围。