Self-tuning NMPC of an engine air path

• Autores: Mendoza D., Schrangl P., Ipanaqué W., del Re L.
• Formato: artículo
• Fecha de Publicación: 2020
• Institución: Consejo Nacional de Ciencia Tecnología e Innovación
• Repositorio: CONCYTEC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.concytec.gob.pe:20.500.12390/2619
• Enlace del recurso: https://hdl.handle.net/20.500.12390/2619; https://doi.org/10.1016/j.ifacol.2020.12.899
• Nivel de acceso: acceso abierto
• Materia: System identification; Adaptive control; Automotive control; Predictive control; http://purl.org/pe-repo/ocde/ford#1.05.09

Many automotive systems such as engines have manufacturing tolerances or change over time. This limits the performance of controllers tuned for the nominal case. A robust controller can not always overcome this performance gap. Against this background, in this work, we propose a self-tuning control strategy for an engine air path model obtained from data of a real engine and show its benefits setting. The self-tuning control consists of an online parameter estimation algorithm for polynomial non-linear autoregressive with exogenous input (PNARX) models and a nonlinear model predictive controller (NMPC) implemented by the continuation/generalized minimum residual (C/GMRES) algorithm. In a first step design of experiments (DOE) is utilized to identify a PNARX model offline from measurements performed on an engine test bed. A tracking NMPC is designed for this model and applied in simulation on the identified model. The control performance is assessed for the case of a wrong initial guess. It is shown that the resulting performance gap can be overcome by the online parameter estimation of a k-step prediction model with directional forgetting. An improved closed loop control performance of the air path model confirms the approach. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license