A supervisor-based neural-adaptive shift controller for automatic transmissions considering throttle opening and driving load

Abstract

Recently, many passenger cars have adopted automatic transmissions for shifting gears, and thus the smooth and precise control of gear shifts of passenger car automatic transmissions has become more and more essential for the riding comfort of vehicles equipped with automatic transmissions. In this article, a neural network-based supervisor for an automotive shift controller considering the throttle opening, variations in throttle opening, and the driving load is presented. For using the driving load information, an, observer-based driving load estimation algorithm is proposed. A proportional-integral-derivative controller along with an open loop controller is used as a low level controller for controlling the gear shifts, and a supervisory controller for properly adapting the shift control parameters of the low level shift controller is designed using ANFIS. To evaluate the control performance of the proposed supervisor-based shift controller, both simulation studies and experimental studies are performed for various shifting situations.