Self-tuning NMPC of an Engine Air Path

2020 IFAC World Congress, series 2020 IFAC World Congress, 2020
Author(s):Mendoza E., Schrangl P., Ipanaque W., Del Re L.
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 bene?ts 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 ?rst step design of experiments (DOE) is utilized to identify a PNARX model o?ine from measurements performed on an engine test bed. A tracking NMPC is designed for this model and applied in simulation on the identi?ed 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 con?rms the approach.
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