AI 中文总结
本研究将性能保证控制（PGC）框架从干扰抑制扩展至参考跟踪，开发了带可证性能界的随机控制综合方法，并将其应用于智能水系统的防洪控制。
AI 中文摘要
现代雨水基础设施面临日益增长的需求，要求相应提升其容量。传统上，这些需求通过新建基础设施资产来满足，而这一过程成本高昂。近年来，许多系统运营商采用反馈控制技术提升系统性能取得了显著成功。然而，由此产生的闭环系统存在功率方向性约束，这会在反馈综合中引入非线性约束。本研究针对一类出现功率方向性约束的通用问题，开发了具有可证均方参考跟踪性能界的随机控制综合方法，并将该方法应用于基于真实世界智能水系统数值模型的防洪示例中。关键成果是将原本专为干扰抑制设计的性能保证控制（PGC）框架扩展为可适应参考跟踪控制目标的版本。
英文摘要
Modern stormwater infrastructure faces increased demands that require a corresponding increase in capacity. Traditionally, these demands have been met by constructing new infrastructure assets, which is a costly endeavor. More recently, many system operators have achieved great success in employing feedback control techniques to improve system performance. However, the resulting closed-loop system exhibits power directionality constraints that introduce nonlinear constraints in feedback synthesis. In this work, we develop a stochastic control synthesis procedure with provable performance bounds on mean-square reference tracking for a general class of problems in which power directionality constraints arise. The proposed method is then applied to a flood mitigation example using a numerical model of a real-world smart water system. The key result is an extension of the performance-guaranteed control (PGC) framework, which was originally designed for disturbance rejection, to accommodate reference tracking control objectives.
Comments6 pages, 6 figures. Accepted to the 65th IEEE Conference on Decision and Control (CDC)
