Robust Computed Torque Control for Uncertain Robotic Manipulators

Ahmed, Maryam Sadeq; Mary, Ali Hussien; Jasim, Hisham H.

doi:10.22153/kej.2021.09.002

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…Robotic manipulators are used successfully in many particular applications, and especially in industrial factories. Accuracy and precision are important features that encourage the use of robotic manipulators in plants that aim to enhance their products and manufacturing processes [1]. There are many challenges that make tracking accuracy in robotic manipulators difficult, including high nonlinearity, system uncertainties, and strong coupling between adjacent joints, hence, several control schemes have been proposed to solve these problems by designing a stable and robust controller.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Robust Tracking Control of Robotic Manipulator based on SMC and Fuzzy Control Strategy

Mary,

Al-Talabi,

Kara

et al. 2024

alkej

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In recent years, robotic systems have been widely used in different applications, and this has motivated researchers to develop different control methods. A model-free, intelligent, robust control method for a nonlinear robotic manipulator system is proposed in this work. This paper presents a novel solution for the major drawbacks of the sliding mode control scheme, which are chattering. Prior knowledge is needed about the dynamic model of the controlled system and the upper bound of uncertainty. In this paper, a fuzzy-like PD controller with SMC (FLPDSM) is proposed. The fuzzy-like PD controller was designed according to fuzzy rules and membership functions based on the nominal model of the robot manipulator. A robust control term was added to the control signal to compensate for the system uncertainty, and external disturbances are compensated by adding an auxiliary robust term to the SMC control law. Two methods for designing robust control terms are proposed. The first proposed method assumes that the upper bound of system uncertainty is known although it cannot be exactly determined due to external disturbances and uncertainty. Hence, a second method was proposed that assumes this bound to be unknown, and an adaptive gain based on Lyapunov theory was used to derive the adaptation law. The Lyapunov second method was used to ensure the stability of the closed loop system. Performance tests on the proposed methods were implemented through simulation studies for the two-link robotic manipulator, and the test results were compared with the standard SMC to verify the effectiveness of the proposed method. A good trajectory tracking with a high robustness against parameter variations and external disturbances was observed under the presented control scheme.

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Section: Introductionmentioning

confidence: 99%