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: 2210315
• 负责人: Miroslav Krstic
• 金额: $ 30万
• 依托单位: University of California-San Diego
• 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=2210315&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.

控制和优化需要在许多应用中同时进行：智能电网、交通网络、协作机器人、医疗保健和其他通过无线或物理连接通信进行交互的自主系统，这两项任务通常通过独立设计来区别对待。结果，这两个任务相互干扰，并且需要在至少两个任务中进行性能妥协，例如，获得最优性，但速度缓慢，或者收敛速度很快，但对于次优运动来说，控制和优化的深度集成非常重要。整合是。当代控制系统的复杂性激增，体现在动态顺序、模型不确定性和不可靠的网络上，这使得并行运行相互干扰的优化和控制的关键挑战是整个系统的稳定性，或者是否确保稳定性。 ，收敛速度。控制优化干扰一直是经典自适应控制（控制器估计器干扰）和极值搜索（优化器控制器干扰）的标志，它们是并发控制和优化的特殊情况。推进基于分布式优化的控制的数学基础，并开发用于大规模非线性不确定系统的基于分布式优化的实时控制设计的新工具和方法。该方法将通过协作机器人网络进行验证。所开发的工具。在这个项目中，针对具有不确定性的大规模非线性系统的基于实时分布式优化的控制算法，所设计的将适用于迄今为止难以处理的大规模系统，包括不确定的网络化非线性算法系统和机器人网络。经过为了消除优化和控制之间的冲突，PI 致力于三个研究任务：(1) 综合对不确定性具有鲁棒性的分布式优化算法，(2) 为每个局部系统设计跟踪控制器。实时跟踪期望的输出，旨在全局最小化某些全局成本；（3）优化和控制算法的集成，以实现优化算法的全局收敛和闭环网络的稳定性。 PI 在非线性小增益理论、用于模块化自适应控制设计的强化不确定性衰减控制器和估计器以及基于学习的控制和极值搜索优化器协同设计的实时优化方面的互补技能方面做出了基础贡献。与依赖于非线性对象之间线性有界相互作用的当前方法相比，在非线性对象的通用性以及所实现的鲁棒性和适应性方面，大大扩大了适用性该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。