MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models

arXiv:2404.12097v1 Announce Type: cross
Abstract: In this paper, we consider the problem of reference tracking in uncertain nonlinear systems. A neural State-Space Model (NSSM) is used to approximate the nonlinear system, where a deep encoder network learns the nonlinearity from data, and a state-space component captures the temporal relationship. This transforms the nonlinear system into a linear system in a latent space, enabling the application of model predictive control (MPC) to determine effective control actions. Our objective is to design …

abstract arxiv cs.lg cs.sy data eess.sy encoder meta meta-learning mpc network paper predictive predictive models reference space state systems tracking type uncertain