CAREER: Robust Learning Control with Application to Intelligent Building Systems

职业：鲁棒学习控制及其在智能建筑系统中的应用

• 批准号: 9732986
• 负责人: Peter Young
• 金额: $ 20万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-05-01 至 2004-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9732986&HistoricalAwards=false
• 关键词: CAREER; Robust; Learning; Control; Application

9732986YoungThe research focuses on the development of new methodologies for robust controller analysis and design, which will be combined with reinforcement learning techniques to develop a new control paradigm: robust learning control. These new analysis and design tools will then be used to address two specific application areas for intelligent building systems: structural control and environmental control. These problems are highly multidisciplinary in nature, and present interesting and important research challenges. At the same time simplified versions of these problems will be used as effective educational tools in a multidisciplinary undergraduate teaching laboratory.The theoretical and computational part of the work will aim towards developing computationally efficient analysis and synthesis methods for a general class of robust performance problems for complex multivariable uncertain systems. These will allow one to address problems with parametric uncertainty and (possibly nonlinear) dynamic uncertainty, with both unknown disturbances and known fixed inputs. These theoretical results will be used as the basis for studying reinforcement learning controllers, by developing an uncertainty model for the learning process within the above robustness framework. This will in turn be used to develop a new controller design methodology for robust leaning controllers, which combine the best aspects of robust and reinforcement learning control. The controller will have guaranteed insensitivity to plant/parameter variations and disturbance signals, while at the same time it will be capable of precisely tuning itself to the nonlinearities and time-variations of a particular plant.The first application area for these new techniques will be vibration supression in tall buildings. The buildings will be subjected to loading which might arise from earthquakes and/or high winds. The goal is to equip the building with sensors and actuators under computer control, creating an intelligent building which has the ability to sense and react to its enviroment. Computer simulations, based on mathematical models, will be comtined with wind-tunnel experiments on a dynamically-scaled physical model. A DSP-based real-time digital feedback control scheme will be used to implement advanced feedback controllers, operating at a sufficiently high bandwidth to effect control of wind-induced vibration on the structure.These techniques will also be applied to design controllers for building environmental systems. These Heating, Ventilation, and Air-Conditioning (HVAC) systems present very challenging control problems because they are complex nonlinear time-varying systems, and yet the controller is required to function on first powering up, preferably without human intervention. Furthermore, high performance is required for energy efficiency, while at the same time robust stability is essential for safety reasons. The new robust learning controllers will be tested both in simulation and on an experimental HVAC system. ***

9732986 Youngth The研究重点是开发可靠的控制器分析和设计的新方法，该方法将与增强学习技术结合使用，以开发新的控制范式：强大的学习控制。 然后，这些新的分析和设计工具将用于解决智​​能建筑系统的两个特定应用领域：结构控制和环境控制。 这些问题本质上是高度多学科的，并且提出了有趣而重要的研究挑战。 同时，这些问题的简化版本将用作多学科的本科教学实验室中的有效教育工具。工作的理论和计算部分旨在为复杂多变量不确定的系统的一般强大的绩效问题开发计算高效的分析和合成方法。 这些将使人们能够解决参数不确定性和（可能是非线性）动态不确定性的问题，既有未知的干扰和已知的固定输入。 这些理论结果将通过在上述鲁棒性框架内为学习过程开发一个不确定性模型来研究增强学习控制者的基础。 反过来，这将用于开发一种新的控制器设计方法，用于可靠的倾斜控制器，该方法结合了强大和强化学习控制的最佳方面。 控制器将保证对植物/参数变化和干扰信号的不敏感性，同时它将能够精确调整特定植物的非线性和时间变化。这些新技术的第一个应用区域将是高大建筑中的振动振动。 这些建筑物将受到地震和/或大风可能引起的负载。 目的是为建筑物配备在计算机控制下的传感器和执行器，从而创建一个智能建筑，该建筑能够感知并应对其环境。 基于数学模型的计算机仿真将与动态尺度的物理模型上的风孔实验相加。 基于DSP的实时数字反馈控制方案将用于实施高级反馈控制器，该控制器在足够高的带宽上运行，以在结构上实现风诱导的振动的控制。这些技术还将应用于建筑环境系统的设计控制器。 这些加热，通风和空调（HVAC）系统具有非常具有挑战性的控制问题，因为它们是复杂的非线性时间变化系统，但是控制器需要在首先加电时运行，最好是无需人工干预即可。 此外，能源效率需要高性能，而同时出于安全原因，稳健稳定性至关重要。 新的鲁棒学习控制器将在模拟和实验性HVAC系统中进行测试。 ***