Simple learning control made practical by zero-phase filtering: applications to robotics

Elci, H.; Longman, Richard W.; Phan, Minh Q.; Juang, Jer‐Nan; Ugoletti, Roberto

doi:10.1109/tcsi.2002.1010031

Cited by 142 publications

(73 citation statements)

References 30 publications

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“…Analyses of the iterative behaviour of discrete systems is facilitated by the matrix description of system dynamics [4], [5], [12]. In the matrix description all time instances of a discrete time signal are concatenated into a single vector.…”

Section: A Ilc Algorithmmentioning

confidence: 99%

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Model-based iterative learning control applied to an industrial robot with elasticity

Hakvoort

Aarts

Dijk

et al. 2007

2007 46th IEEE Conference on Decision and Control

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Abstract-In this paper model-based Iterative Learning Control (ILC) is applied to improve the tracking accuracy of an industrial robot with elasticity. The ILC algorithm iteratively updates the reference trajectory for the robot such that the predicted tracking error in the next iteration is minimised. The tracking error is predicted by a model of the closed-loop dynamics of the robot. The model includes the servo resonance frequency, the first resonance frequency caused by elasticity in the mechanism and the variation of both frequencies along the trajectory. Experimental results show that the tracking error of the robot can be reduced, even at frequencies beyond the first elastic resonance frequency.

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Section: A Ilc Algorithmmentioning

confidence: 99%

“…The application of ILC to improve the accuracy of robotic manipulators has been studied extensively. Classically the focus is on improving the tracking accuracy of the robot's drives [1], [2], [3], [4], [5]. However, accurate motion of the drives does not necessarily imply accurate motion of a tool mounted to the robot [6].…”

Section: Introductionmentioning

confidence: 99%

Model-based iterative learning control applied to an industrial robot with elasticity

Hakvoort

Aarts

Dijk

et al. 2007

2007 46th IEEE Conference on Decision and Control

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“…where Q and L are non-causal zero-phase-shift low-pass filters [16][17][18]. Nonetheless, to stabilize the system in the infinite time horizon in the presence of every possible repetitive disturbance, L should have infinite attenuation in the stopband, which is unavailable for any practical digital filter.…”

Section: Introductionmentioning

confidence: 99%

Repetitive neurocontroller with disturbance feedforward path active in the pass-to-pass direction for a VSI inverter with an output LC filter

Ufnalski

Grzesiak

2016

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. An enhancement to the previously developed repetitive neurocontroller (RNC) is discussed and investigated in the paper. Originally, the time-base generator (TBG) has been used to produce the only input signal for the neural approximator. The resulting search space makes the dynamic optimization problem (DOP) of shaping the control signal solvable with the help of a function approximator such as the feed-forward neural network (FFNN). The plant under consideration, i.e. a constant-amplitude constant-frequency voltage-source inverter (CACF VSI) with an output LC filter, is assumed to be equipped with the disturbance load current sensor to enable implementation of the disturbance feed-forward (pDFF) path as a part of the non-repetitive subsystem acting in the along the pass p-direction. An investigation has been undertaken to explore potential benefits of using this signal also as an additional input for the RNC to augment the approximation space and potentially enhance the convergence rate of the real-time search process. It is numerically demonstrated in the paper that the disturbance feed-forward path active in the pass-to-pass k-direction (kDFF) improves the dynamics of the repetitive part as well indeed.

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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%

“…Examples of surveys on ILC are Moore (1999), Chen and Wen (1999), Bien and Xu (1998) and Bristow et al (2006). In Longman (2000) and Elci et al (2002) ILC is applied to a seven degrees of freedom robot arm, however smaller than the one used here, and the type of trajectories used in these experiments are not motivated by any particular application.…”

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.

show abstract

Simple learning control made practical by zero-phase filtering: applications to robotics

Cited by 142 publications

References 30 publications

Model-based iterative learning control applied to an industrial robot with elasticity

Model-based iterative learning control applied to an industrial robot with elasticity

Repetitive neurocontroller with disturbance feedforward path active in the pass-to-pass direction for a VSI inverter with an output LC filter

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

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