Anson Lee

Misfire Detection Using a Dynamic Neural Network with Output Feedback

A misfire detection apparatus and method for detecting misfire of an engine. The engine has an engine speed and a manifold absolute pressure. An engine signal preprocessing system is provided for determining: a fluctuation associated with the engine speed; an average associated with the engine speed; and an average associated with the manifold absolute pressure. A neural network determines a firing event signal based upon: the determined engine speed fluctuation; the determined engine speed average; and the determined manifold absolute pressure average. A misfire decision determinator detects a misfire of the engine based upon the determined firing event signal. A preferred embodiment also includes a dynamic neural network system with global output feedback.

Modeling, parameters identification, and control of an electronic throttle control (ETC) system

The modeling, parameters identification, and linear and nonlinear feedback control designs of an electronic throttle control (ETC) system is considered. A commercially available ETC system made by Bosch is selected for our investigation. The unknown parameters identified are used in designing linear and nonlinear controllers. Simulations and extensive experiments were conducted. A real-time linear controller was implemented with the xPC Target. The ETC model is then extended to a separately excited nonlinear DC motor model. The nonlinear controller is designed using the input-output feedback linearization technique; the extension proves to be of solid theoretical value. The results presented in this paper can be considered as an interesting and important case study encompassing system modeling, parameters identification, linear and nonlinear controller designs, and real-time control. The techniques and methodology developed are applicable to similar and/or other types of systems.