Identification of dynamic linear models of automotive palm bio-diesel engine for real-time speed control applications

Abstract

This paper describes the black-box modeling of automotive palm bio-diesel engine based on real-time data. The models derived are for the development of real-time self tuning speed controller purposes. Assuming a discrete time form for the system model, an Autoregressive eXogenous (ARX) model structure was selected in this work. Real-time data obtained using a computer-based data acquisition system from a 2.0L automotive diesel engine test-bed unit were used for modeling. A Pseudo-Random Binary Sequence (PRBS) with maximum lengths sequence of 31 has been used as the input signal to determine the dynamic model of automotive palm diesel engine at low, medium and high speed range (around 1300, 2150 and 3250 rpm). The input and output signals were interfaced to the plant via Matlab programming. A recursive estimation algorithm, Recursive Least Squares (RLS) method, was used to estimate of the parameters of the models. Finally, model validation test was done by plotting the output predicted by the model and comparing it with the measured output.