Enfoque robusto para identificación y control basado en modelo de volterra

Volterra models have been shown to adequately capture nonlinear behavior. In this work, a Volterra-type model named Modified Generalized Memory Polynomial (MGMP) model is dealt with. An uncertainty characterization is developed to account for the uncertain system dynamics. For this purpose, a bounded interval is determined for every parameter in the model. Then, a robust identification procedure is proposed to calculate the parameters bounds. This work presents a method for assessing the effect of uncertainty on closed-loop performance and for designing a robust nonlinear model predictive controller capable to deal with this error. Finally, a fuel cell is presented as a case study in order to evaluate the performance of the proposed identification and control methodology.