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Type-2 fuzzy cerebellar model articulation control system design for MIMO uncertain nonlinear systems

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Abstract

This paper aims to propose a more efficient neural network and applies it as an adaptive controller for the multi-input multi-output (MIMO) uncertain nonlinear systems. First, a more efficient fuzzy neural network named as fuzzy cerebellar model articulation controller (CMAC) is introduced, then an adaptive controller is proposed using a novel interval type-2 fuzzy CMAC (T2FCMAC). The T2FCMAC realizes an interval type-2 fuzzy logic system based on the structure of the CMAC. Due to the better ability of handling uncertainties provided by type-2 fuzzy sets, it can solve some complicated problems with outstanding effectiveness than type-1 fuzzy sets. In addition, an intelligent control system is proposed; this control system is comprised of a T2FCMAC and an auxiliary compensation controller. The T2FCMAC is utilized to approximate a perfect controller and the parameters of T2FCMAC are on-line tuned by the derived adaptive laws based on a Lyapunov function. The auxiliary compensation controller is designed to suppress the influence of residual approximation error between the perfect controller and the T2FCMAC. Finally, two MIMO uncertain nonlinear systems, a Chua’s chaotic circuit and a mass-spring-damper mechanical system, are performed to verify the effectiveness of the proposed control scheme. The simulation results confirm that the proposed intelligent adaptive control system can achieve favorable tracking performance with desired robustness.

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Acknowledgements

The authors appreciate the financial support in part from the Nation Science Council of Republic of China under grant NSC 101-2221-E-155-026-MY3.

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  1. Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan

    Chih-Min Lin & Ming-Shu Yang

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  1. Chih-Min Lin
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  2. Ming-Shu Yang
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Correspondence to Chih-Min Lin.

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Lin, CM., Yang, MS. Type-2 fuzzy cerebellar model articulation control system design for MIMO uncertain nonlinear systems. Int. J. Mach. Learn. & Cyber. 11, 269–286 (2020). https://doi.org/10.1007/s13042-019-00972-z

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  • DOI: https://doi.org/10.1007/s13042-019-00972-z

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