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

在现代工业应用（包括汽车、飞机和制造设施）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手段。在大多数这些应用中，精确的定时是系统正常工作所必需的，而定时偏差有可能导致有害甚至危及生命的性能恶化。然而，共享网络固有的争用可能会发生，这意味着多个连接的设备希望同时在网络上传输数据。该项目将开发实时网络控制器，在实现预期控制目标的同时解决争用。此外，它们对物理系统和网络本身的扰动具有鲁棒性。该项目的重点是工业中常见的网络架构，其成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，该项目将富有洞察力的工程与复杂的数学相结合，以生产具有严格性能保证的实用控制器为目标。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群体参与STEM研究。该项目将解决最具挑战性和最重要的网络系统问题。这将需要基础研究来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鲁棒模型预测控制框架和事件触发定时模型，结合自主控制和优化的优势。该工作将开发一个事件触发的定时模型，用于实时网络的消退地平线模型预测控制，该模型将处理任务依赖性和定时变化，并自适应地补偿竞争和时间延迟。这将允许每个控制回路有多个传感器和执行器节点，这是最先进的网络化工业应用的必要条件。控制器将尊重状态和输入约束，优化成本标准，预测时间变化，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估计，并克服最佳现有结果的保守性，其中状态空间通常被选择为李雅普诺夫函数的子水平集，其边界由扰动的极值决定。相反，控制器将寻求在状态约束被违反之前可以允许的最大摄动界。许多具体的实现，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流行，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。

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.