Robust two-degree-of-freedom iterative learning control for flexibility compensation of industrial robot manipulators

Abstract

Most industrial robots are actuated using geared motors with no direct load side measurement. The flexibility introduced by the gear reducer causes transmission errors and vibrations, which limits the adoption of robot manipulators in many demanding applications. This paper presents a lean and efficient scheme of iterative learning control (ILC) to compensate for the joint flexibility of industrial robot manipulators. A two-degree-of-freedom ILC method is introduced. Compared with the dual-stage ILC that has been previously proposed for servo flexibility compensation, the method is more effective and also enables a leaner implementation. In addition, in order to handle system variation, a robust synthesis method is developed by using H∞ and µ techniques in an innovative way. The proposed method is analyzed using simulation studies as well as tested on an actual industrial robot manipulator.

Resonant Suppression Method Based on PI control for Serial Manipulator Servo Drive System

“…(10) of the system was written as Eq. (11). Equation (11) could be obtained through the change of Eq.…”

Analysis of Oscillation Frequency Deviation in Elastic Coupling Digital Drive System and Robust Notch Filter Strategy

“…2) Input shaping methods: correcting input reference and using iterative learning control in industrial assembly line with repeated tasks, which can suppress terminal vibration and make trajectory smooth [1], [9]. 3) State variable measurement and estimation: using sensors or observers to achieve additional feedback or feedforward from one or more selected state variables to improve system characteristics and performances [10]- [13].…”

On inversion-based approaches for feedforward and ILC

“…For example, ZPETC is often used for the design of ILC filters, but requires an additional robustness filter at the cost of performance [2,18]. Alternatively, infinite preview [19] or H ∞ based [11,12] techniques can be used. However, [11,12] lack the use of preview, and are recently extended in [20] towards preview/fixed lag smoothing situations.…”

“…Alternatively, infinite preview [19] or H ∞ based [11,12] techniques can be used. However, [11,12] lack the use of preview, and are recently extended in [20] towards preview/fixed lag smoothing situations. This paper provides guidelines on proper use of inversion techniques for both inverse model feedforward and learning control by addressing the application specific objective.…”

“…Successful approximate solutions include [6], ZPETC [7], ZMETC, NPZ-Ignore [8], and EBZPETC [9], see also [10] for an overview. Additionally, standard H ∞ without preview has been used [11,12] to design ILC filters, as well as H ∞ preview in feedforward [13,14]. Furthermore, optimization-based approaches include techniques based on LQ tracking control [15], also known as norm-optimal ILC [16], where in addition to inversion a weight on the input signal is imposed.…”