TSM-Based Adaptive Fuzzy Control of Robotic Manipulators with Output Constraints | Zendy

Fei Yan | Zendy; Shubo Wang | Zendy

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TSM-Based Adaptive Fuzzy Control of Robotic Manipulators with Output Constraints

Author(s) -

Fei Yan,

Shubo Wang

Publication year - 2021

Publication title -

computational intelligence and neuroscience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.605

H-Index - 52

eISSN - 1687-5273

pISSN - 1687-5265

DOI - 10.1155/2021/5812584

Subject(s) - control theory (sociology) , controller (irrigation) , computer science , fuzzy logic , transformation (genetics) , lyapunov function , adaptive control , process (computing) , stability (learning theory) , fuzzy control system , control engineering , lyapunov stability , control (management) , artificial intelligence , nonlinear system , engineering , biochemistry , chemistry , physics , quantum mechanics , machine learning , gene , agronomy , biology , operating system

This paper proposes an adaptive control scheme based on terminal sliding mode (TSM) for robotic manipulators with output constraints and unknown disturbances. The fuzzy logic system (FLS) is developed to approximate unknown dynamics of robotic manipulators. An error transformation technique is used in the process of controller design to ensure that the output constraints are not violated. The advantage of the error transformation compared to traditional barrier Lyapunov functions (BLFs) is that there is no need to design a virtual controller. Thus, the design complexity of the controller is reduced. Through Lyapunov stability analysis, the system state can be proved to converge to the neighborhood near the balanced point in finite time. Extensive simulation results illustrated the validity of the proposed controller.

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