Over the last decades, model predictive control (MPC) has shown outstanding performance
for control tasks from various domains. This performance has further improved in recent …

Identification of nonlinear dynamic systems remains challenging nowadays. Although the
nonlinear autoregressive with exogenous input (NARX) model is flexible to describe …

Bayesian nonlinear system identification for one of the major classes of dynamic model, the
nonlinear autoregressive with exogenous input (NARX) model, has not been widely studied …

Severe geomagnetic disturbances can be hazardous for modern technological systems. The
reliable forecast of parameters related to the state of the magnetosphere can facilitate the …

In this contribution we derive a computational Bayesian approach to NARMAX model
identification. The identification algorithm exploits continuing advances in computational …

The present study proposes a multi-objective framework for structure selection of nonlinear
systems which are represented by polynomial NARX models. This framework integrates the …

The identification of polynomial Nonlinear Autoregressive [Moving Average] models with
eXogenous variables (NAR [MA] X) is typically carried out with incremental model building …

This brief presents a new framework for the identification of nonlinear autoregressive (AR)
models with exogenous inputs (NARX) model for design (NARX-M-for-D), which represents …

Abstract System identification and data-driven modeling techniques have seen ubiquitous
applications in the past decades. In particular, parametric modeling methodologies such as …

This research deals with developing an intelligent trajectory tracking control approach for an
aircraft in the presence of internal and external disturbances. Internal disturbances including …