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Linear-extended-state-observer-based prescribed performance control for trajectory tracking of a robotic manipulator

Linear-extended-state-observer-based prescribed performance control for trajectory tracking of a... Trajectory tracking error of robotic manipulator has limited its applications in trajectory tracking control systems. This paper aims to improve the trajectory tracking accuracy of robotic manipulator, so a linear-extended-state-observer (LESO)-based prescribed performance controller is proposed.Design/methodology/approachA prescribed performance function with the convergence rate, maximum overshoot and steady-state error is derived for the output error transformation, whose stability can guarantee trajectory tracking accuracy of the original robotic system. A LESO is designed to estimate and eliminate the total disturbance, which neither requires a detailed system model nor a heavy computation load. The stability of the system is proved via the Lyapunov theory.FindingsComparative experimental results show that the proposed controller can achieve better trajectory tracking accuracy than proportional-integral-differential control and linear active disturbance rejection control.Originality/valueIn the LESO-based prescribed performance control (PPC), the LESO was incorporated into the PPC design, it solved the problem of stabilizing the complex transformed system and avoided the costly offline identification of dynamic model and estimated and eliminated the total disturbance in real-time with light computational burden. LESO-based PPC further improved control accuracy on the basis of linear-active-disturbance-rejection-control. The new proposed method can reduce the trajectory tracking error of the robotic manipulators effectively on the basis of simplicity and stability. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Industrial Robot: The International Journal of Robotics Research and Application Emerald Publishing

Linear-extended-state-observer-based prescribed performance control for trajectory tracking of a robotic manipulator

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0143-991X
DOI
10.1108/ir-07-2020-0150
Publisher site
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Abstract

Trajectory tracking error of robotic manipulator has limited its applications in trajectory tracking control systems. This paper aims to improve the trajectory tracking accuracy of robotic manipulator, so a linear-extended-state-observer (LESO)-based prescribed performance controller is proposed.Design/methodology/approachA prescribed performance function with the convergence rate, maximum overshoot and steady-state error is derived for the output error transformation, whose stability can guarantee trajectory tracking accuracy of the original robotic system. A LESO is designed to estimate and eliminate the total disturbance, which neither requires a detailed system model nor a heavy computation load. The stability of the system is proved via the Lyapunov theory.FindingsComparative experimental results show that the proposed controller can achieve better trajectory tracking accuracy than proportional-integral-differential control and linear active disturbance rejection control.Originality/valueIn the LESO-based prescribed performance control (PPC), the LESO was incorporated into the PPC design, it solved the problem of stabilizing the complex transformed system and avoided the costly offline identification of dynamic model and estimated and eliminated the total disturbance in real-time with light computational burden. LESO-based PPC further improved control accuracy on the basis of linear-active-disturbance-rejection-control. The new proposed method can reduce the trajectory tracking error of the robotic manipulators effectively on the basis of simplicity and stability.

Journal

Industrial Robot: The International Journal of Robotics Research and Application – Emerald Publishing

Published: Aug 19, 2021

Keywords: Control; Robotics; Path following; Prescribed performance control; Linear-extended-state-observer; Trajectory tracking control

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