Event Triggered Unknown Networked Control System Design by using Adaptive Dynamic Programming

该项目将为网络控制系统（NCS）开发新的通用控制设计，例如智能电网和现代汽车系统所需的新型分布式控制系统。与许多传统的NCS系统不同，这些系统将从一开始就进行设计，以便随着时间的推移最大限度地提高性能，充分考虑网络中的通信延迟和意外事件，以及非线性，连续随机干扰和本PI过去已经解决的其他问题。他打算开发严格的数学证明，这些新的设计将始终导致稳定的操作。他还将通过他现有的工业大学智能维护中心，维护一个直接实际应用的管道。这些设计是通用的、基于学习的和大规模并行的，在某种程度上也可以帮助我们理解哺乳动物的大脑如何执行超出更传统类型设计范围的任务。这些设计将是以前由NSF资助的工作的延伸，在刘易斯和刘编辑的RLADP手册中有所描述。ADP，像线性规划一样，是一类挑战，而不仅仅是一种特定的方法，尽管成功的方法之间存在联系。ADP包括所有针对非线性和随机干扰下的多级优化一般问题的方法。为了科普一般的非线性任务，这个项目将包括神经网络，已被证明是更有效的一般非线性函数的逼近器比更传统的系统。 这里的主要新奇之处在于能够将这些能力与通信延迟和随机离散事件的存在相结合，这在大多数NCS中是一个重要的实际问题。

This project will develop new universal control designs for Network Control Systems (NCS) such as the new distributed control systems needed for the smart grid and modern automotive systems. Unlike many traditional NCS systems, these will be designed from the start so as to maximize performance over time, fully accounting for communication delays in networks and unexpected events, as well as nonlinearities, continuous random disturbances and other issues which this PI has addressed in the past. He intends to develop rigorous mathematical proofs that these new designs will always lead to stable operation. He will also maintain a pipeline to immediate practical applications, through his existing Industry-University Center on Intelligent Maintenance. The designs are intended to be general, learning-based and massively parallel, in a way which may also help us understand how mammal brains can perform tasks beyond the scope of more traditional types of design.These designs will be an extension of work previously funded by NSF, described in the Handbook of RLADP, edited by Lewis and Liu. ADP, like linear programming, is a class of challenges, not just one specific method, though there are relations between the successful methods. ADP includes all the methods aimed at the general problem of multistage optimization in the face of nonlinearity and stochastic disturbance. In order to cope with general nonlinear tasks, this project will include neural networks which have been proved to be more effective as approximators of general nonlinear functions than more traditional systems. The chief novelty here is the ability to combine these capabilities with the presence of communication delays and random discrete events, which are an important practical issue in most NCS.