A recurrent TSK interval type-2 fuzzy neural networks control with online structure and parameter learning for mobile robot trajectory tracking

This paper focuses on the design of a recurrent Takagi-Sugeno-Kang interval type-2 fuzzy neural network RTSKIT2FNN for mobile robot trajectory tracking problem. The RTSKIT2FNN is incorporating the recurrent frame of internal-feedback loops into interval type-2 fuzzy neural network which uses simple...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2019-11, Vol.49 (11), p.3881-3893
Main Authors: Bencherif, Aissa, Chouireb, Fatima
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Artificial neural networks
Computer Science
Control theory
Distance learning
Feedback loops
Fuzzy control
Fuzzy logic
Fuzzy sets
Machine learning
Machines
Manufacturing
Mechanical Engineering
Neural networks
Parameters
Processes
Robot control
Robots
Tracking problem
Trajectories
Wireless networks
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Summary:This paper focuses on the design of a recurrent Takagi-Sugeno-Kang interval type-2 fuzzy neural network RTSKIT2FNN for mobile robot trajectory tracking problem. The RTSKIT2FNN is incorporating the recurrent frame of internal-feedback loops into interval type-2 fuzzy neural network which uses simple interval type-2 fuzzy sets in the antecedent part and the Takagi-Sugeno-Kang (TSK) type in the consequent part of the fuzzy rule. The antecedent part forms a local internal feedback loop by feeding the membership function of each node in the fuzzification layer to itself. Initially, the rule base in the RTSKIT2FNN is empty, after that, all rules are generated by online structure learning, and all the parameters of the RTSKIT2FNN are updated online using gradient descent algorithm with varied learning rates VLR. Through experimental results, we demonstrate the effectiveness of the proposed RTSKIT2FNN for mobile robot control.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-019-01439-y