Format

Send to

Choose Destination
Springerplus. 2013 Apr 27;2(1):188. doi: 10.1186/2193-1801-2-188. Print 2013 Dec.

Neuro-fuzzy controller to navigate an unmanned vehicle.

Author information

1
Department of Computer Science, Faculty of Science, University of Science and Technology "Mohamed Boudiaf" USTO Oran, Oran, BP1505, Algeria.

Abstract

A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN).

KEYWORDS:

ANFIS; Control; Fuzzy logic; Neural network; Unmanned vehicle

Supplemental Content

Full text links

Icon for Springer Icon for PubMed Central
Loading ...
Support Center