Analysis on dynamics of underwater robot manipulators based on iterative learning control and time-scale transformation

Kawamura, Sadao; Sakagami, Norimitsu

doi:10.1109/robot.2002.1014688

Cited by 30 publications

(20 citation statements)

References 8 publications

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“…In the great majority of cases, the reference trajectory used does not change between trials, and little work has been conducted to derive ILC algorithms in which the repeated operation may consist of a more general objective, which need not comprise the tracking of a static predefined [1]. Those which do exist generally deal with specialized cases and are linked with specific applications (such as gas metal arc welding [2], underwater robotics [3], or liquid slosh in a packaging machine [4]). …”

Section: Terative Learning Control (Ilc) Is a Techniquementioning

confidence: 99%

“…To achieve the tracking task an ILC algorithm of the general form (2) will be considered, where is a suitable linear operator which may be non-causal (this encompasses a large number of algorithms appearing in the literature, see [1] and [15]). However, at the end of the th trial, is taken and instead of calculating , with a fixed reference, , for use in the update (2), the reference is allowed to change, and is replaced with , leading to (3) So the ILC law (2) becomes (4) This update replaces (2) and will be considered in the analysis which follows. The time domain relationships which arise using (4) are (5) (6) (7) where is the error prior to updating the reference, is the identity operator, and is the lifted plant matrix.…”

Section: Objective-driven Ilc Developmentmentioning

confidence: 99%

See 1 more Smart Citation

Iterative Learning Control for Multiple Point-to-Point Tracking Application

Freeman

Cai

Rogers

et al. 2011

IEEE Trans. Contr. Syst. Technol.

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Abstract-This paper considers a general class of linear iterative learning control (ILC) algorithm applied to tracking tasks which require the plant output to reach given points at predetermined time instants, without the specification of intervening reference points. A framework is developed in the frequency-domain in which the reference is updated between trials. It is shown that superior convergence and robustness properties are obtained compared with those associated with using the original class of ILC algorithm to track a prescribed arbitrary reference trajectory satisfying the point-to-point output constraints. Experimental results using a non-minimum phase test facility are presented to illustrate the theoretical findings.

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Section: Terative Learning Control (Ilc) Is a Techniquementioning

confidence: 99%

Section: Objective-driven Ilc Developmentmentioning

confidence: 99%

Iterative Learning Control for Multiple Point-to-Point Tracking Application

Freeman

Cai

Rogers

et al. 2011

IEEE Trans. Contr. Syst. Technol.

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“…Note that j represents the Although ILC was originally developed for robotics [15], it has been shown to improve the performance of a much broader range of manufacturing and chemical systems. Examples included CNC machine tools, wafer stage motion, injection mold-ing machines, aluminum extruders, cold rolling mills, induction motors, rapid thermal processing, and semibatch chemical reactors (see references in [37][38][39] [40], identification procedure for system dynamics [41], non-identical repetitive applications [42]), research in the area has continued to increase. Many of the current key investigators, along with their respective areas of focus, are listed in Figure 2.10.…”

Section: Iterative Learning Controlmentioning

confidence: 99%

Precision coordination and motion control of multiple systems via iterative learning control

Barton

Alleyne

2010

Proceedings of the 2010 American Control Conference

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In today's engineering world, many emerging applications ranging from manufacturing to the autonomous vehicle industry require coordination of multiple systems. Traditional approaches for controlling these systems often neglect the underlying coupling in the application. To stay at the forefront of these fields requires the development of innovative approaches to new challenges. The research in this dissertation focuses on designing novel control strategies for coordinated applications. Electrohydrodynamic jet (E-jet) printing, an example of an emerging micro/nano-manufacturing process with applications in biotechnology and flexible electronics, requires multiple systems to work in a coordinated manner to achieve a desired objective. Repetitive execution of processes such as E-jet can be harnessed to achieve high performance. Iterative learning control (ILC) is an adaptive control technique for improving process performance in systems that execute a task repetitively. This research simultaneously exploits the repetitiveness and inherent coupling of the desired outcome by applying a coordinated ILC approach to processes such as E-jet. The versatility of this approach will be demonstrated through applications ranging from robotics to emerging manufacturing processes.ii To Alex and our little family iii

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“…At the core of the proposed framework is the concept of spatial projection which is closely related to appropriate temporal rescaling. To the best of authors' knowledge the only reference that utilizes a similar idea in the ILC setting is reported in Kawamura and Sakagami (2002). Namely, in Kawamura and Sakagami (2002), ILC and time-scale transformation are used to identify added mass, drag and buoyancy in the dynamics of the underwater robots.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of authors' knowledge the only reference that utilizes a similar idea in the ILC setting is reported in Kawamura and Sakagami (2002). Namely, in Kawamura and Sakagami (2002), ILC and time-scale transformation are used to identify added mass, drag and buoyancy in the dynamics of the underwater robots. The considered application is very specific and moreover, no general analysis and design framework is provided.…”

Section: Introductionmentioning

confidence: 99%

Spatial Iterative Learning Control: Output Tracking

et al. 2017

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This paper focuses on tracking spatially repeatable tasks. In addition, these tasks are not necessarily temporally repeatable in the sense that the finite length of the corresponding time interval may change with each repetition. Because of that, the standard Iterative Learning Control (ILC) framework is not directly applicable. Namely, the standing assumption that the finite length of the time interval is fixed with each repetition, is violated. Motivated by human motor learning, this paper proposes a Spatial ILC (SILC) framework which leverages the spatial repeatability. In particular, the concept of spatial projection, closely related to temporal rescaling, is proposed. This allows to spatially relate the relevant information from the past repetition to the present repetition. To demonstrate the proposed framework, a class of nonlinear time-varying systems with relative degree zero is selected. In particular, using contraction mapping technique, it is shown that under appropriate assumptions, the corresponding tracking error converges under the proposed SILC control law. Finally, simulation results support the obtained result.

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Analysis on dynamics of underwater robot manipulators based on iterative learning control and time-scale transformation

Cited by 30 publications

References 8 publications

Iterative Learning Control for Multiple Point-to-Point Tracking Application

Iterative Learning Control for Multiple Point-to-Point Tracking Application

Precision coordination and motion control of multiple systems via iterative learning control

Spatial Iterative Learning Control: Output Tracking

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