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

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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.