TY - JOUR
T1 - Type-2 Fuzzy Hybrid Controller Network for Robotic Systems
AU - Chao, Fei
AU - Zhou, Dajun
AU - Lin, Chih-Min
AU - Yang, Longzhi
AU - Zhou, Changle
AU - Shang, Changjing
N1 - Funding Information:
Manuscript received April 21, 2019; revised May 19, 2019 and May 22, 2019; accepted May 22, 2019. Date of publication July 3, 2019; date of current version July 10, 2020. This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 20720190142, in part by the National Natural Science Foundation of China under Grant 61673322 and Grant 61673326, in part by the Natural Science Foundation of Fujian Province of China under Grant 2017J01129, and in part by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie under Grant 663830. This paper was recommended by Associate Editor J. Q. Gan. (Corresponding author: Fei Chao.) F. Chao is with the Cognitive Science Department, School of Information Science and Engineering, Xiamen University, Xiamen 361005, China, and also with the Department of Computer Science, Institute of Mathematics, Physics and Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, U.K. (e-mail: fchao@xmu.edu.cn).
Publisher Copyright:
© 2013 IEEE.
PY - 2020/8/31
Y1 - 2020/8/31
N2 - 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.
AB - 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.
KW - Adaptive control
KW - Type-2 inference system
KW - brain emotional learning controller (BELC) network
KW - robot dynamic control
UR - http://www.scopus.com/inward/record.url?scp=85070501632&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2019.2919128
DO - 10.1109/TCYB.2019.2919128
M3 - Article
C2 - 31283516
SN - 2168-2267
VL - 50
SP - 3778
EP - 3792
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 8
M1 - 8754689
ER -