Abstract
This paper proposes an output feedback controller with a linear extended state observer (LESO) for an n-degree-of-freedom (n-DOF) manipulator under the presence of external disturbance, an input dead-zone, and time-varying output constraints. First, these issues are derived in mathematical equations accompanying an n-DOF manipulator. The proposed control is designed based on the backstepping technique with the barrier Lyapunov function (BLF) and a LESO. The LESO is used for estimating both the unmeasured states and the lumped uncertainties including the unknown frictions, external disturbances, and input dead-zone, in order to enhance the accuracy of the robotic manipulator. Additionally, the BLF helps to avoid violation of the output constraints. The stability and the output constraint satisfaction of the controlled manipulator are theoretically analyzed and proven by the Lyapunov theorem with a barrier Lyapunov function. Some comparative simulations are carried out on a 3-DOF planar manipulator. The simulation results prove the significant performance improvement of the proposed control over the previous methods.
Highlights
In recent years, robots have attracted the interest of many researchers in institutes, universities, and technology companies around the world [1]
In order to improve the effectiveness of the backstepping control, some advanced approximators, such as fuzzy logic systems (FLSs) [10,11,12], neural networks [13,14,15,16], and extended state observers [17,18], were applied to the backstepping control to compensate for the uncertainties
The results in this figure show that the backstepping control still. The results in this figure show that the backstepping control still transgresses the constraints and the linear extended state observer via backstepping control (LESOBC) begins breaking the output constraints because the accuracy of the LESOBC is improved by the linear extended state observer (LESO) and it does not depend on the output boundaries
Summary
Robots have attracted the interest of many researchers in institutes, universities, and technology companies around the world [1]. In Reference [30], an adaptive neural network control was proposed for a robotic manipulator under the presence of an input dead-zone and output constraint. In Reference [31], a BLF was combined with an adaptive neural network to design an advanced control for a two-DOF hydraulic robot with output constraints. This paper proposes an advanced output feedback control via a linear extended state observer for an n-DOF manipulator, regardless of the uncertainties and the time-varying output constraint. The uncertainties such as unknown frictions, external disturbances, and input dead-zone are taken into account in this study.
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