An improved optimal integral sliding mode control for uncertain robotic manipulators with reduced tracking error, chattering, and energy consumption
This paper proposes an improved optimal integral sliding mode control (IOISMC) for a robust and optimal tracking control of robotic manipulators under the presence of coupling effects, parameter variations, and external disturbances. By representing the system dynamics in the error coordinate and in...
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Main Authors: | , |
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Format: | Article |
Published: |
Academic Press Inc.
2020
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Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/86874/ https://dx.doi.org/10.1016/j.ymssp.2020.106747 |
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Summary: | This paper proposes an improved optimal integral sliding mode control (IOISMC) for a robust and optimal tracking control of robotic manipulators under the presence of coupling effects, parameter variations, and external disturbances. By representing the system dynamics in the error coordinate and introducing an additional optimal integral term in the nominal control part of IOISMC, the effects of unmeasured forces, reaching phase issue, chattering issue, and position offset associated with conventional optimal integral sliding mode control are significantly addressed. Hence, the proposed approach has several notable merits, namely fast recovery response, low chattering, high tracking performance, high energy efficiency, low computational complexity, and robustness against uncertainties. The idea has been proven in two ways: a strong theoretical framework using Lyapunov stability theory and extensive simulation verification. The superiority of the proposed method has been confirmed through significant reductions of chattering and energy consumption without significant performance loss. Furthermore, the proposed method is capable of sustaining high tracking performance despite the presence of the aforementioned uncertainties and eliminates the reaching phase. It is envisaged that the proposed method can be useful in designing a robust and optimal tracking control for all types of uncertain multivariate systems without compromising on the computational cost. |
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