Paper Info
Title
Neuro-fuzzy controller for autonomous navigation of mobile robots
Abstract
Recent works in fuzzy logic systems (FLSs) have demonstrated the superiority of the higher order FLSs (type-2 FLs) compared to their traditional counterparts (Type-1 FLS) in terms of handling uncertainties. However, due to the complexity and the huge computational time of the type-reduction process, their applications in real-time is limited to simple cases. In this paper, we propose a hybrid approach to decrease the computational time of the type-reduction process. A modified interval type-2 fuzzy neural network (MIT2FNN) is developed for the navigation of mobile robots in unstructured and dynamic environments. The MIT2FNN includes a type-2 fuzzy linguistic process as the antecedent part, and a two-layer neural network as the consequent part. The back propagation algorithm is utilized to adjust the parameters of the MIT2FNN controller. The experimental results obtained using an omnidrive mobile robot named Robotino validate the effectiveness and reliability of the proposed approach.
Year
DOI
Venue
2014
10.1109/CCA.2014.6981474
Control Applications
Keywords
DocType
ISSN
backpropagation,fuzzy control,fuzzy logic,mobile robots,neurocontrollers,path planning,MIT2FNN controller,Robotino omnidrive mobile robot,backpropagation algorithm,fuzzy logic systems,higher order FLS,mobile robot autonomous navigation,modified interval type-2 fuzzy neural network,neurofuzzy controller,two-layer neural network,type-1 FLS,type-2 FLS,type-2 fuzzy linguistic process,type-reduction process
Conference
1085-1992
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Achille Melingui100.34
Rochdi Merzouki211725.83
Jean Bosco Mbede331.13