Akiyuki Iemura

An application study of online reference governor to boost pressure control for automotive diesel engines

In this paper, we consider an application of an online reference governor (RG) to boost pressure control of automotive diesel engines. In order to treat the system nonlinearity, the plant is expressed as a piecewise affine model through system idenfication for each fixed operating condition of engine speed and fuelling. The plant model is utilized for the future prediction in the online RG. Then, an online iterative RG algorithm based on the gradient descent method is applied to generate the optimal modified reference so that the constraint on the maximal turbocharger rotation speed is satisfied. We show the effectiveness of the present method through an experiment with a production vehicle.

Reference Management for Aftertreatment Temperature Control of Automotive Diesel Engines

Abstract This paper describes aftertreatment temperature control of automotive diesel engines based on reference governor (RG). The RG algorithm computes a modified reference of catalyst temperature so that the predicted output of the closed-loop system on a finite prediction horizon does not exceed the constraint of its maximal temperature. We apply a bisectional search algorithm to the RG where the search region is extended in order to take account of model uncertainties. We show the effectiveness of the present method through an experiment with a production vehicle.