A nested-loop iterative learning control for robot manipulators
Journal
IFAC-PapersOnLine
Journal Volume
52
Journal Issue
15
Pages
358-363
Date Issued
2019
Author(s)
Abstract
To improve the tracking performance of industrial robot manipulators, a nested-loop iterative learning control (ILC) structure is presented. It consists of an inner loop that deals with drive dynamics, and an outer loop which addresses impreciseness of kinematic parameters as well as joint static bias. A data-based frequency inversion technique with motion constraints is utilized for fast inner loop convergence. The outer loop measures the end effector deviation with a laser tracker and uses inverse Jacobian matrix for joint reference modification. Analysis of the algorithm is given, and is experimentally demonstrated on a six degree-of-freedom robot manipulator. It is shown that the proposed method mitigates the maximum dynamic tracking error by an order of magnitude, and is applicable to different payloads due to small system variation from torque shielding of gear reduction. ? 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Subjects
Degrees of freedom (mechanics)
Digital storage
End effectors
Flexible manipulators
Industrial manipulators
Industrial robots
Inverse problems
Iterative methods
Jacobian matrices
Learning algorithms
Modular robots
Robot applications
Dynamic inversion
Inversion techniques
Iterative learning control
Kinematic parameters
Robot manipulator
Six degree-of-freedom
Tracking performance
Trajectory tracking
Two term control systems
SDGs
Type
conference paper