Type-2 Fuzzy Hybrid Controller Network for Robotic Systems

Fei Chao, Dajun Zhou, Chih-Min Lin, Longzhi Yang, Changle Zhou, Changjing Shang

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)
39 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)3778-3792
Number of pages15
JournalIEEE Transactions on Cybernetics
Issue number8
Early online date3 Jul 2019
Publication statusPublished - 1 Aug 2020


Dive into the research topics of 'Type-2 Fuzzy Hybrid Controller Network for Robotic Systems'. Together they form a unique fingerprint.

Cite this