Automatic lateral control for unmanned vehicles via genetic algorithms

Authors:
E. Onieva;J. E. Naranjo;V. Milanés;J. Alonso;R. García;J. Pérez
Affiliations:
Industrial Computer Science Department, Centro de Automática y Robótica (UPM-CSIC), La Poveda-Arganda del Rey, 28500 Madrid, Spain;Department of Intelligent Systems, Polytechnic University of Madrid (UPM), 28031 Madrid, Spain;Industrial Computer Science Department, Centro de Automática y Robótica (UPM-CSIC), La Poveda-Arganda del Rey, 28500 Madrid, Spain;Industrial Computer Science Department, Centro de Automática y Robótica (UPM-CSIC), La Poveda-Arganda del Rey, 28500 Madrid, Spain;Industrial Computer Science Department, Centro de Automática y Robótica (UPM-CSIC), La Poveda-Arganda del Rey, 28500 Madrid, Spain;Industrial Computer Science Department, Centro de Automática y Robótica (UPM-CSIC), La Poveda-Arganda del Rey, 28500 Madrid, Spain
Venue:
Applied Soft Computing
Year:
2011

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Abstract

It is known that the techniques under the topic of Soft Computing have a strong capability of learning and cognition, as well as a good tolerance to uncertainty and imprecision. Due to these properties they can be applied successfully to Intelligent Vehicle Systems; ITS is a broad range of technologies and techniques that hold answers to many transportation problems. The unmanned control of the steering wheel of a vehicle is one of the most important challenges facing researchers in this area. This paper presents a method to adjust automatically a fuzzy controller to manage the steering wheel of a mass-produced vehicle; to reach it, information about the car state while a human driver is handling the car is taken and used to adjust, via iterative genetic algorithms an appropriated fuzzy controller. To evaluate the obtained controllers, it will be considered the performance obtained in the track following task, as well as the smoothness of the driving carried out.