Model Predictive Control with Discrete/Continuous Decisions: Theory, Computation, and Application

具有离散/连续决策的模型预测控制：理论、计算和应用

• 批准号: 1854007
• 负责人: James Rawlings
• 金额: $ 5.12万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-10 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1854007&HistoricalAwards=false
• 关键词: Model; Predictive; Control; Discrete; Continuous

1603768 PI: RawlingsTitle: Model Predictive Control with Discrete/Continuous Decisions: Theory, Computation, and ApplicationSystems requiring online discrete operating decisions, also known as actuators in the control context, are ubiquitous. In the historical development of feedback control theory relevant to process control, however, the discrete decisions were always removed from the online control problem and considered at a different level in the automation system, often using heuristic rules, such as cycling through some bank of furnaces or chillers in a pre-specified order. As a result, optimization of these discrete decisions has seen essentially no implementation in process control applications, while optimization of the continuous decisions (valve positions, applied voltages, torques, etc.) has reached a high level, as demonstrated by the widespread use of model predictive control (MPC) in the process industries. The goal of this research project is to enable optimization and feedback control of processes that have discrete as well as continuous decision variables. The PI proposes to develop both new theory and computational software to address this class of problems. The research will be conducted in close collaboration with an industrial partner, Johnson Controls, and the research results will be tested in applications involving control of heating, ventilation, and air conditioning (HVAC) systems in commercial buildings. Energy use in buildings is responsible for a significant fraction of energy consumption and carbon emissions in the US.The proposed new theory encompasses both nominal closed-loop stability and inherent robustness to model errors and disturbances, and requires extension of standard MPC with continuous decisions (valve positions, applied voltages, torques, etc.) to MPC with both continuous and discrete variables (on/off switches for chillers, heaters, pumps; which equipment to use when during a periodic, cyclic operation, etc.). The proposed project aims to develop: (i) new theory for MPC with discrete/continuous actuators, (ii) new free-source software to solve the MPC control problem with discrete actuators, and (iii) an approach for decomposing the large-scale, complex building HVAC control problem into tractable sub-problems. The enabling computational software will be made available to all researchers in the free-source language CasADi, which has become the leading language for solving large, complex, and structured optimal control problems. Adding discrete decision variables to CasADi will provide practitioners with a tool to implement the results of this research in a broad range of industrial applications. The PI plans to train a graduate student and develop new educational materials on control system design.

1603768 PI：RawlingsTitle：离散/连续决策的模型预测控制：理论、计算和应用需要在线离散操作决策的系统，也称为控制环境中的执行器，是无处不在的。然而，在与过程控制相关的反馈控制理论的历史发展中，离散决策总是从在线控制问题中删除，并在自动化系统的不同级别上考虑，通常使用启发式规则，例如以预先指定的顺序循环通过一些炉或冷却器。结果，这些离散决策的优化在过程控制应用中基本上没有实现，而连续决策（阀位置、施加电压、扭矩等）的优化在过程控制应用中基本上没有实现。模型预测控制（MPC）在过程工业中的广泛应用表明，它已经达到了很高的水平。这个研究项目的目标是使优化和反馈控制的过程中，有离散以及连续的决策变量。PI建议开发新的理论和计算软件来解决这类问题。该研究将与工业合作伙伴约翰逊控制公司密切合作进行，研究结果将在涉及商业建筑供暖、通风和空调（HVAC）系统控制的应用中进行测试。建筑物中的能源使用是美国能源消耗和碳排放的重要组成部分。所提出的新理论包括标称闭环稳定性和对模型误差和干扰的固有鲁棒性，并需要扩展标准MPC的连续决策（阀门位置，施加电压，扭矩等）。到具有连续和离散变量的MPC（冷水机、加热器、泵的开/关开关;在周期性、循环操作期间使用哪些设备等）。该项目旨在开发：（i）离散/连续执行器MPC的新理论，（ii）新的自由源软件解决离散执行器MPC控制问题，以及（iii）将大规模复杂建筑HVAC控制问题分解为易于处理的子问题的方法。所有研究人员都可以使用免费源代码语言CasADi，该语言已成为解决大型，复杂和结构化最优控制问题的领先语言。将离散决策变量添加到CasADi将为从业者提供一种工具，以在广泛的工业应用中实施本研究的结果。PI计划培养一名研究生，并开发有关控制系统设计的新教材。