Sparse identification of nonlinear dynamics for model predictive control in the low-data limit.

ABSTRACT: Data-driven discovery of dynamics via machine learning is pushing the frontiers of modelling and control efforts, providing a tremendous opportunity to extend the reach of model predictive control (MPC). However, many leading methods in machine learning, such as neural networks (NN), require large volumes of training data, may not be interpretable, do not easily include known constraints and symmetries, and may not generalize beyond the attractor where models are trained. These factors limit their use for the online identification of a model in the low-data limit, for example following an abrupt change to the system dynamics. In this work, we extend the recent sparse identification of nonlinear dynamics (SINDY) modelling procedure to include the effects of actuation and demonstrate the abi

SUBMITTER: Kaiser E 

PROVIDER: S-EPMC6283900 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature