CAREER: Robust Learning Control with Application to Intelligent Building Systems

这项研究集中于开发新的鲁棒控制器分析和设计方法，并将与强化学习技术相结合，开发一种新的控制范式：鲁棒学习控制。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具体应用领域：结构控制和环境控制。这些问题是高度多学科性质的，并提出了有趣和重要的研究挑战。同时，这些问题的简化版本将被用作多学科本科教学实验室的有效教育工具。这项工作的理论和计算部分将致力于为复杂多变量不确定系统的一类一般鲁棒性能问题开发计算高效的分析和综合方法。这将使人们能够处理参数不确定性和(可能是非线性的)动态不确定性问题，其中既有未知的扰动，也有已知的固定输入。这些理论结果将作为研究强化学习控制器的基础，通过在上述鲁棒性框架内建立学习过程的不确定性模型。这将被用来开发一种新的控制器设计方法，用于鲁棒学习控制器，它结合了健壮和强化学习控制的最佳方面。该控制器将保证对对象/参数变化和干扰信号不敏感，同时它将能够精确地调整自己以适应特定对象的非线性和时变。这些新技术的第一个应用领域将是高层建筑的振动抑制。建筑物将承受可能由地震和/或大风引起的荷载。其目标是在计算机控制下为建筑配备传感器和执行器，创造一座能够感知环境并对环境做出反应的智能建筑。基于数学模型的计算机模拟将与动态比例物理模型上的风洞实验相结合。基于数字信号处理器的实时数字反馈控制方案将被用来实现先进的反馈控制器，其工作在足够高的带宽以实现对结构的风致振动控制。这些技术也将被应用于建筑环境系统的控制器设计。这些供暖、通风和空调(HVAC)系统提出了非常具有挑战性的控制问题，因为它们是复杂的非线性时变系统，但控制器需要在首次通电时工作，最好是在没有人工干预的情况下。此外，为了提高能效，需要高性能，同时出于安全原因，稳健的稳定性是必不可少的。新的鲁棒学习控制器将在模拟和实验的暖通空调系统上进行测试。***

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. ***