An LPV Adaptive Observer for Updating a Map Applied to an MAF Sensor in a Diesel Engine

Sensors

Zhiyuan Liu²  Changhui Wang¹ 

[1] Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China; E-Mail

关键词: linear parameter varying (LPV) system;    adaptive observer;    sensor error compensation;    map (or lookup table) estimation;    mass air flow;    diesel engine;   

DOI  :  10.3390/s151027142

来源: mdpi

【 摘 要 】

In this paper, a new method for mass air flow (MAF) sensor error compensation and an online updating error map (or lookup table) due to installation and aging in a diesel engine is developed. Since the MAF sensor error is dependent on the engine operating point, the error model is represented as a two-dimensional (2D) map with two inputs, fuel mass injection quantity and engine speed. Meanwhile, the 2D map representing the MAF sensor error is described as a piecewise bilinear interpolation model, which can be written as a dot product between the regression vector and parameter vector using a membership function. With the combination of the 2D map regression model and the diesel engine air path system, an LPV adaptive observer with low computational load is designed to estimate states and parameters jointly. The convergence of the proposed algorithm is proven under the conditions of persistent excitation and given inequalities. The observer is validated against the simulation data from engine software enDYNA provided by Tesis. The results demonstrate that the operating point-dependent error of the MAF sensor can be approximated acceptably by the 2D map from the proposed method.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.