Type-2 Fuzzy Hybrid Controller Network for Robotic Systems

Chao, Fei, Zhou, Dajun, Lin, Chih-Min, Yang, Longzhi, Zhou, Changle and Shang, Changjing (2020) Type-2 Fuzzy Hybrid Controller Network for Robotic Systems. IEEE Transactions on Cybernetics, 50 (8). pp. 3778-3792. ISSN 2168-2267

Text (Final published version)
08754689.pdf - Published Version

Download (2MB) | Preview
Text (Advance online version)
08754689.pdf - Published Version

Download (3MB) | Preview
T2BEL_EC_520_LY.pdf - Accepted Version

Download (4MB) | Preview
Official URL: https://doi.org/10.1109/TCYB.2019.2919128


Dynamic control, including robotic control, faces both the theoretical challenge of obtaining accurate system models and the practical difficulty of defining uncertain system bounds. To facilitate such challenges, this paper proposes a control system consisting of a novel type of fuzzy neural network and a robust compensator controller. The new fuzzy neural network is implemented by integrating a number of key components embedded in a Type-2 fuzzy cerebellar model articulation controller (CMAC) and a brain emotional learning controller (BELC) network, thereby mimicking an ideal sliding mode controller. The system inputs are fed into the neural network through a Type-2 fuzzy inference system (T2FIS), with the results subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the proposed network estimates the nonlinear equations representing the ideal sliding mode controllers using a powerful compensator controller with the support of T2FIS and BELC, guaranteeing robust tracking of the dynamics of the controlled systems. The adaptive dynamic tuning laws of the network are developed by exploiting the popular brain emotional learning rule and the Lyapunov function. The proposed system was applied to a robot manipulator and a mobile robot, demonstrating its efficacy and potential; and a comparative study with alternatives indicates a significant improvement by the proposed system in performing the intelligent dynamic control.

Item Type: Article
Uncontrolled Keywords: Adaptive control, brain emotional learning controller (BELC) network, robot dynamic control, Type-2 inference system
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 04 Jul 2019 08:28
Last Modified: 31 Jul 2021 13:17
URI: http://nrl.northumbria.ac.uk/id/eprint/39857

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics