On the iterative learning control theory for robotic manipulators

Bondi, P.; Casalino, Giuseppe; Gambardella, Luca Maria

doi:10.1109/56.767

Cited by 344 publications

(113 citation statements)

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“…Therefore, in view of the internal model principle, [5], it might be expected that accommodation of these periodic signals can be achieved by duplicating this model inside a feedback loop. In the literature, two types of compensators can be found which accomplish this: the repetitive controller, see for example [10,8,20,19], and the iterative learning controller, see for example [3,4,12].…”

Section: Introductionmentioning

confidence: 99%

Internal-model-based design of repetitive and iterative learning controllers for linear multivariable systems

Roover

Bosgra

Steinbuch

2000

International Journal of Control

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Repetitive and iterative learning control are two modern control strategies for tracking systems in which the signals are periodic in nature. This paper discusses repetitive and iterative learning control from an internal model principle point of view. This allows the formulation of existence conditions for multivariable implementations of repetitive and learning control. It is shown that repetitive control can be realized by an implementation of a robust servomechanism controller that uses the appropriate internal model for periodic disturbances. The design of such controllers is discussed. Next it is shown that iterative learning control can be implemented in the format of a disturbance observer/compensator. It is shown that the resulting control stucture is dual to the repetitive controller, and that both constitute an implementation of the internal model principle. Consequently, the analysis and design of repetitive and iterative learning control can be generalized to the powerful analysis and design procedure of the internal model framework, allowing to trade-off the convergence speed for periodic-disturbance cancellation versus other control objectives, such as stochastic disturbance suppression.

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Section: Introductionmentioning

confidence: 99%

Internal-model-based design of repetitive and iterative learning controllers for linear multivariable systems

Roover

Bosgra

Steinbuch

2000

International Journal of Control

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“…Arimoto et al (1984), Casalino and Bartolini (1984), and Craig (1984) were independently describing a method that iteratively compensated for model errors and disturbances. The development of ILC stems originally from the robotics area, and examples of contributions where ILC is applied in robotics are Arimoto et al (1984), Bondi et al (1988), Guglielmo and Sadegh (1996), Horowitz et al (1991), Lange and Hirzinger (1999) and Elci et al (2002). Examples of surveys on ILC are Moore (1999), Chen and Wen (1999), Bien and Xu (1998) and Bristow et al (2006).…”

Section: Introductionmentioning

confidence: 99%

Arm-side evaluation of ILC applied to a six-degrees-of-freedom industrial robot

Wallén

Norrlöf

Gunnarsson

2008

IFAC Proceedings Volumes

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Experimental results from a first order P-type ILC algorithm applied to a large size six degrees of freedom commercial industrial robot are presented. The ILC algorithm is based on measurements of the motor angles, but in addition to the conventional evaluation of the ILC algorithm based on the control error on the motor side, the tool path error on the arm side is evaluated using a laser tracker. Experiments have been carried out in three different operating points using movements that represent typical paths in a laser cutting application, and different choices of algorithm design parameters have been studied. The motor angle error is reduced substantially in all experiments, and the tool path error is reduced in most of the cases. In one operating point, however, the error does not decrease as much and an oscillatory tool behaviour is observed. Changed filter variables can give worse error reduction in all operating points. To achieve even better performance, especially in difficult operating points, it is concluded that an arm side measurement, from for example an accelerometer, needs to be included in the learning. Abstract: Experimental results from a first order P-type ILC algorithm applied to a large size six degrees of freedom commercial industrial robot are presented. The ILC algorithm is based on measurements of the motor angles, but in addition to the conventional evaluation of the ILC algorithm based on the control error on the motor side, the tool path error on the arm side is evaluated using a laser tracker. Experiments have been carried out in three different operating points using movements that represent typical paths in a laser cutting application, and different choices of algorithm design parameters have been studied. The motor angle error is reduced substantially in all experiments, and the tool path error is reduced in most of the cases. In one operating point, however, the error does not decrease as much and an oscillatory tool behaviour is observed. Changed filter variables can give worse error reduction in all operating points. To achieve even better performance, especially in difficult operating points, it is concluded that an arm side measurement, from for example an accelerometer, needs to be included in the learning.

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“…In this context, iterative learning control (ILC) techniques can be applied in order to enhance the tracking performance from operation to operation. Since the early works of Arimoto et al (1984), Casalino and Bartolini (1984) and Craig (1984), several ILC schemes for robot manipulators have been proposed in the literature (see for instance Arimoto, 1996;Bondi et al, 1988;Luca et al, 1992;Horowitz, 1993;Kavli, 1992;Kawamura et al, 1988;Moon et al, 1997). These ILC algorithms, whether developed for the linearized model or the nonlinear model, are generally based upon the contraction mapping approach and require a certain a priori knowledge of the system dynamics.…”

Section: Introductionmentioning

confidence: 99%