CAREER: Switching and Logic in Control

在这份建议书中，我们描绘了一个自动控制领域的五年研究和教育计划。 该计划的总体目标是为致力于科学研究的职业生涯奠定基础，并将其融入南加州大学的教育计划。我们的研究和教育工作将集中在混合动力控制系统领域，即，结合了联合收割机连续动态和离散逻辑的系统。 我们特别感兴趣的监督控制算法，利用离散逻辑的在线学习和适应的目的。 在这种情况下使用逻辑是在15年前的控制文献中提出的。 大多数早期的工作是理论性的，其动机是希望构建能够稳定非常大类系统的通用控制器。 这些控制器的通用性是以性能非常差为代价的，并且由此产生的算法不能用于大多数实际应用中。 从那时起，在这一领域取得了很大的进展，现在监督控制实际上是作为一种技术，以达到非常高的性能和鲁棒性的突然变化的过程或控制objectives.The我们的研究的科学目标是开发一个框架，加快分析和设计的学习算法，利用开关和逻辑。 为此，我们将隔离的监督控制算法的设计中出现的基本问题，并探讨他们在一个系统的方式。 我们在这个建议中表明，虽然许多文献中提出的算法来自根本不同的方法，他们共享的关键结构，并表现出几个共同的属性。 一个共同的框架将有助于更好地了解其主要特点和局限性。 我们在这里分离出两个我们认为对广泛建立监督控制至关重要的具体问题：1。将这些算法的应用领域扩展到复杂非线性系统的控制。 特别是，当只有输出反馈可用和/或当有显着的测量噪声和未建模的动态。2.开发理论工具来优化这些系统的性能。 在这里，性能应理解为在一个短暂的和渐近的意义。

0093762HespanhaIn this proposal we delineate a five year-research and education plan in the area of Automatic Control. The overall objective of this plan is to build the foundation for a career devoted to scientific research and its integration in the educational program at the University of Southern California.Our research and education efforts will be focused in the area of hybrid control systems, i.e., systems that combine continuous dynamics with discrete logic. We are particularly interested in supervisory control algorithms that utilize discrete logic for the purpose of online learning and adaptation. The use of logic in this context was proposed in the control literature more than fifteen years ago. Most of the early work was of a theoretical nature, motivated by the desire to construct universal controllers capable of stabilizing very large classes of systems. The universality of these controllers was achieved at the expense of very poor performance and the resulting algorithms could not be used in most practical applications. Since then a great deal of progress has been made in this area and now supervisory control is actually proposed as a technique to attain very high performance and robustness to sudden changes in the process or the control objectives.The scientific goal of our research is to develop a framework to expedite the analysis and design of learning algorithms that utilize switching and logic. Towards this end we will isolate the fundamental issues that arise in the design of supervisory control algorithms and explore them in a systematic fashion. We show in this proposal that although many of the algorithms proposed in the literature originate from fundamentally different approaches, they share key structures and exhibit several common properties. A common framework will provide a better understanding of their key features and limitations. We isolate here two specific topics that we judge crucial for the wide establishment of supervisory control:1. Extend the domain of application of these algorithms to the control of complex nonlinear systems. In particular, when only output-feedback is available and/or when there is significant measurement noise and unmodeled dynamics.2. Develop theoretical tools to optimize the performance of these systems. Here, performance should be understood both in a transient and in an asymptotic sense.