Collaborative Research: Robustness of Networked Model Predictive Control under Scheduling Constraints

协作研究：调度约束下网络模型预测控制的鲁棒性

• 批准号: 1436284
• 负责人: Fumin Zhang
• 金额: $ 28.01万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1436284&HistoricalAwards=false
• 关键词: Collaborative; Research; Robustness; Networked; Model

Connecting multiple actuators, controllers, and sensors over shared data networks is a common means of reducing cost and increasing maintainability in modern industrial applications, including automobiles, aircraft, and manufacturing facilities. In most of these applications precise timing is necessary for proper system function, and timing deviations have the potential to cause detrimental and even life-threatening deterioration of performance. However it is inherent to shared networks that contention may occur, meaning that more than one connected device wants to transmit data over the network at the same time. This project will develop real-time networked controllers that resolve contention while achieving desired control objectives. Furthermore, they will be robust to perturbations of the physical system and the network itself. The focus of the project is on network architectures that are common in industry, and the results will apply particularly to automotive control and robotic applications. As reflected in the expertise of the PIs, the project combines insightful engineering with sophisticated mathematics, towards the goal of producing practically useful controllers that have rigorous performance guarantees. Through a series of outreach activities, the project will help broaden participation of underrepresented groups in STEM research.This project will address among the most challenging and important networked systems problems. It will entail fundamental research to overcome current limitations of model-based control of industrial networks. The project will use a new robust model predictive control framework and event-triggered timing model that combines the strengths of autonomous control and optimization. The work will develop an event-triggered timing model for receding horizon model predictive control of a real-time network, that will handle task dependency and timing variations and adaptively compensate for contentions and time delays. This will allow multiple sensor and actuator nodes for each control loop, a necessity for state-of-the-art networked industrial applications. The controller will respect state and input constraints, optimize cost criteria, predict timing variations, and ensure robustness to perturbations. It will provide least-conservative estimates of robust positive invariant sets in the workspace, and overcome the conservativeness of the best existing results, where the state space is usually chosen to be a sublevel set of a Lyapunov function whose boundary is determined by the supremum of the perturbations. Instead, the controller will seek maximal perturbation bounds that can be allowed before state constraints are violated. Much of the specific implementation, as well as the experimental validation, will emphasize CANbus networks. Because CANbus is popular for real-time industrial control applications, and is the standard protocol for the automotive industry, this will maximize the immediate impact of the results.

在汽车、飞机和制造设施等现代工业应用中，通过共享数据网络连接多个执行器、控制器和传感器是降低成本和提高可维护性的常用方法。在大多数这样的应用中，精确的定时是系统正常运行所必需的，而定时偏差有可能导致有害的、甚至危及生命的性能恶化。但是，共享网络可能会发生争用，这意味着多个连接的设备希望同时通过网络传输数据。该项目将开发实时联网控制器，在解决争用的同时实现预期的控制目标。此外，它们将对物理系统和网络本身的扰动具有健壮性。该项目的重点是工业中常见的网络体系结构，其结果将特别适用于汽车控制和机器人应用。正如PI的专业知识所反映的那样，该项目将有洞察力的工程与复杂的数学相结合，以实现生产具有严格性能保证的实用控制器的目标。通过一系列外展活动，该项目将有助于扩大代表不足的群体对STEM研究的参与。该项目将解决最具挑战性和最重要的联网系统问题。这将需要进行基础研究，以克服目前基于模型的工业网络控制的局限性。该项目将使用一种新的稳健模型、预测控制框架和事件触发定时模型，结合了自主控制和优化的优点。该工作将开发一种用于实时网络的滚动时间模型预测控制的事件触发定时模型，该模型将处理任务相关性和定时变化，并自适应地补偿争用和时延。这将允许每个控制回路有多个传感器和执行器节点，这对于最先进的联网工业应用是必要的。该控制器将尊重状态和输入约束，优化成本标准，预测时序变化，并确保对扰动的鲁棒性。它将提供工作空间中鲁棒正不变集的最小保守性估计，并克服现有最佳结果的保守性，其中状态空间通常被选择为Lyapunov函数的子级集，其边界由扰动的上确界确定。相反，控制器将寻求在违反状态约束之前可以允许的最大摄动界。许多具体实施以及实验验证都将重点放在CanBus网络上。由于CanBus在实时工业控制应用中广受欢迎，并且是汽车行业的标准协议，因此这将最大限度地提高结果的即时影响。