Synthesizing Neural Network Controllers with Closed-Loop Dissipativity Guarantees

arXiv:2404.07373v1 Announce Type: cross
Abstract: In this paper, a method is presented to synthesize neural network controllers such that the feedback system of plant and controller is dissipative, certifying performance requirements such as L2 gain bounds. The class of plants considered is that of linear time-invariant (LTI) systems interconnected with an uncertainty, including nonlinearities treated as an uncertainty for convenience of analysis. The uncertainty of the plant and the nonlinearities of the neural network are both described using integral quadratic …

abstract arxiv class cs.lg cs.sy eess.sy feedback linear loop network neural network paper performance plants requirements systems type uncertainty