A neural network-based approach to hybrid systems identification for control

arXiv:2404.01814v1 Announce Type: cross
Abstract: We consider the problem of designing a machine learning-based model of an unknown dynamical system from a finite number of (state-input)-successor state data points, such that the model obtained is also suitable for optimal control design. We propose a specific neural network (NN) architecture that yields a hybrid system with piecewise-affine dynamics that is differentiable with respect to the network's parameters, thereby enabling the use of derivative-based training procedures. We show that a careful choice …

abstract architecture arxiv control cs.lg cs.sy data design designing eess.sy hybrid identification machine machine learning math.oc network neural network state systems type