Hybrid Neuro-Fuzzy Based Improved Control Design of Electro-Pneumatic Clutch Actuation Control System for Heavy Duty Vehicles

Abstract

The application of hybrid Neuro-Fuzzy principles of clutch actuation control in enhancing the performance of electro-pneumatic clutch actuation system for heavy duty vehicles is the aim of this presentation. Neuro-Fuzzy is a hybrid design that accommodates the principles of fuzzy Logic and Neural Network in a manner that take advantage of their positive sides. The inability of some heavy-duty vehicles to operate optimally on hilly terrains and which often results in road mishaps are traceable to the inadequacies in conventional clutch actuation control mechanisms. These conventional actuation control techniques provide for routine calibration of clutch actuators after maintenance. Often times, this important requirement of calibration is neglected with attendant ugly consequences. To stem this tide of manual calibration and its obvious defects, an intelligent method of clutch actuation modelled in a hybrid Neuro-Fuzzy control is implemented. Conventional data obtained for errors, speed, torque and power from Mercedes Benz Actros Truck model MP 2, 2031 provided the reference points. These data were embedded into a Simulink block and cascaded into a designed Neuro-Fuzzy Simulink model. Both conventional and Neuro-Fuzzy Simulink model controllers were also simulated. Different percentages of improvements were recorded for piston error, angular speed, engine torque and power respectively. The level and percentage of improvements stood at 0.4821mm or 33.04% decrease for error, increases of 334.1 RPM or 33% for angular speed, 0.0594NM or 33.26% for torque and 2.79W or 16.53% for power respectively.