| dc.description.abstract | The task of system identification is far from being a new one. It was initially proposed in the mid of the 20th century, and had then been extensively developed for linear systems, due to the demands of that time concerning computational power, systems complexity and control requirements. It has achieved excellent results in this approach. However, due to the rise of systems complexity and control requirements, linear models were no longer able to meet the desired accuracy and larger operating range, and therefore the usage nonlinear models were pursued. As all systems in nature are nonlinear to some extent, it is correct to state that nonlinear models can represent a whole lot more of systems’ dynamics than linear models. Nonlinear models were then studied, and several techniques were presented, being able to achieve very good results. In this work, two of the available nonlinear models were studied, namely NARMAX and Hammerstein-Wiener, applying these models in two simulated systems. Two algorithms were then derived to estimate parameters for NARMAX and Hammerstein-Wiener models using an orthogonal estimator, and also an algorithm for generating multi-level input signals. The models were then estimated to the simulated systems, and compared using the AIC, FPE, Lipschitz and high-order cross-correlation criteria. The best results were obtained for the Hammerstein-Wiener-OLS and NARMAX-OLS models, as opposed to the NARMAX-RLS model. However, due to divergent observed results between models, it can be concluded that precise methods for model comparison and validation still needs to be developed, as well as a method for nonlinearity quantification for the system in hand. | en |
