Collaborative Research: Robustness of Networked Model Predictive Control Satisfying Critical Timing Constraints

协作研究：满足关键时序约束的网络模型预测控制的鲁棒性

• 批准号: 1436774
• 负责人: Michael Malisoff
• 金额: $ 16.09万
• 依托单位: Louisiana State University
• 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 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1436774&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.

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并提高现代工业应用（包括汽车，飞机和制造设施）的可维护性的常见手段。在大多数这些应用中，精确的时机对于正确的系统功能是必要的，并且时机偏差有可能导致绩效的有害甚至威胁生命的恶化。但是，共享网络可能会发生争执，这意味着多个连接的设备希望同时通过网络传输数据。该项目将开发实时网络控制器，这些控制器可以在实现所需的控制目标的同时解决争议。此外，它们对物理系统和网络本身的扰动将是强大的。该项目的重点是在行业中常见的网络体系结构上，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，该项目将有见地的工程与精致的数学结合在一起，以生产具有严格性能保证的实际有用的控制器。通过一系列的外展活动，该项目将有助于扩大代表性不足的STEM研究的参与。该项目将解决最具挑战性和最重要的网络系统问题之一。它将需要基础研究，以克服基于模型的工业网络控制的当前局限性。该项目将使用新的强大模型预测控制框架和事件触发的时序模型，该模型结合了自主控制和优化的优势。这项工作将开发一个事件触发的时序模型，用于对实时网络的水平模型预测控制，该模型将处理任务依赖性和时机变化，并适应性地补偿论点和时间延迟。这将允许每个控制循环的多个传感器和执行器节点，这是最先进的网络工业应用程序的必要性。控制器将尊重状态和输入约束，优化成本标准，预测定时变化并确保对扰动的鲁棒性。它将在工作空间中提供最不保守的估计值，并克服最佳现有结果的保守性，其中通常选择状态空间是一组lyapunov函数集，其边界由互动的上限确定。相反，控制器将寻求在违反状态约束之前允许的最大扰动界限。大部分特定实现以及实验验证将强调Canbus网络。由于Canbus在实时工业控制应用中很受欢迎，并且是汽车行业的标准协议，因此这将最大程度地提高结果的直接影响。