System Identification of an Actuated Inclined Ball Mechanism Via Causation Entropy

Abstract

Greybox and blackbox dynamic models are routinely used to model the behavior of realworld dynamic systems. When creating such models, the identification of an accurate model structure (often referred to as covariate selection, feature selection, or sparsity identification) is a critical step required to achieve suitable predictive performance by minimizing the effects of overfitting. Recently, causation entropy has been shown to be quite useful in datadriven covariate selection as it provides a mechanism to measure the causal relationships between the set of covariates and the state dynamics. This work extends previous results by applying the causation entropy covariate selection technique to data from an experimental nonlinear system consisting of a ball rolling on an actuated inclined ramp. Data collected from the system is processed by the causation entropybased algorithm and covariate selection is performed on a blackbox dynamic model. The resulting optimized model is shown to provide better predictive performance than an optimized blackbox model which includes extraneous covariates. This study represents the first application of causation entropybased covariate selection to realworld experimental data, illustrating its use as a practical system identification method.