Nonlinear Model Predictive Control Based on Second-Order NARX-Laguerre Model for Twin Rotor System Control

In this paper, we present an innovative strategy for nonlinear model predictive control by employing a discrete-time NARX-Laguerre model. This latter model is crafted through the expansion of discrete-time NARX model parameters using a set of five independent Laguerre bases. A notable benefit of this approach is a substantial reduction in the number of parameters compared to the classical NARX model. However, the realization of this reduction depends on the careful selection of optimal Laguerre poles that define these bases. The parameters of the NARX-Laguerre model are determined through a recursive methodology. This resulting model is subsequently applied in the implementation of nonlinear model predictive control. To formulate the optimization problem, we incorporate a performance criterion that takes into account both process input and output constraints. We assess the effectiveness of this novel approach to nonlinear model predictive control through experimentation on the Twin Rotor System.