Title: Validation of uncertainty estimation in engine cold start

Authors: Selina Pan; Akhil Neti; Nikhil Neti; Andreas Hansen; J. Karl Hedrick

Addresses: Department of Mechanical Engineering, Stanford University, Stanford, CA, USA ' Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA, USA ' Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA, USA ' Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA, USA ' Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA, USA

Abstract: The engine cold start period produces the majority of harmful hydrocarbon emissions during engine operation, and, therefore, the reduction of such emissions is key to maintaining ultra low emission vehicle standards. Emissions reduction can be achieved by the design and implementation of controllers with effective tracking performance and estimation of uncertain parameters in the system. Effective tracking performance can be achieved by driving down error in the tracking of desired engine state trajectories. Estimation of uncertainty can be achieved through the use of an adaptive controller. In this work, an adaptive sliding controller is designed in order to achieve both goals, and implemented on an engine test cell. Experimental results show the reduction of the tracking error as well as estimation of model uncertainty in the engine test cell.

Keywords: uncertainty estimation; engine cold start; adaptive control.

DOI: 10.1504/IJPT.2017.087891

International Journal of Powertrains, 2017 Vol.6 No.3, pp.206 - 225

Available online: 30 Oct 2017 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article