Data-Based Modeling and Control of Dynamical Systems - Parameter Estimation

Abstract

Parameter estimation methods to provide data-based models to control complex dynamical systems are reviewed. Starting from least square minimization of the equation error, the tutorial provides an overview of how different perspectives of parameter estimation lead to various algorithms that are used in diverse contexts. Both statistical and deterministic approaches are discussed, and the utility of model inferences are explained. The discussions provide a context and review relevant background with respect to three application papers involving recent advances in Gaussian Process Regression (GPR), state estimation approaches and data-driven modeling.