Optimal iterative learning control of an extrusion plant

Buchheit, K.; Pandit, Μ.; Befort, M.

doi:10.1049/cp:19940209

Cited by 23 publications

(13 citation statements)

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“…Examples of this idea can for example be found in the publications by Arimoto et al (1984a,b), Furuta and Yamakita (1987), Padieu and Su (1990), Owens (1992Owens ( , 1993, Lee and Lee (1993), Moore (1993) and Buchheit et al (1994) and includes the general area of trajectory following in robotics. The specified task is regarded as the tracking of a given reference signal r(t) or output trajectory for an operation on a specified time interval 0 < t < T. It is important to note that feedback control cannot, by its very nature, achieve this exactly as a non-zero error is required to activate the feedback mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…The results presented in this paper represent an improvement on their algorithm with the added bonus that convergence is guaranteed without the need to choose any step length parameters. In the paper by Buchheit et al (1994), an optimization problem related to the problem in this paper is proposed. Because it is numerically more involved, it must be solved iteratively, leading to a different and more complicated scheme than proposed here.…”

Section: Introductionmentioning

confidence: 99%

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Iterative learning control using optimal feedback and feedforward actions

Amann

Owens

Rogers

1996

International Journal of Control

282

156

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative learning control using optimal feedback and feedforward actions

Amann

Owens

Rogers

1996

International Journal of Control

282

156

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“…This paper focuses on a well-established algorithmic framework termed norm optimal ILC (NOILC), which selects the next control input to minimize a cost function involving the predicted error and the change in control inputs between successive trials [16,26]. NOILC can be implemented using a purely feedforward structure [27], or through combination of state feedback and predictive feedforward action [28,29]. The framework has been applied to a range of systems including gantry robots [1], industrial robotic systems [20], rehabilitation platforms [30], lasers [12] and pneumatic muscle actuators [31].…”

Section: Norm Optimal Iterative Learning Controlmentioning

confidence: 99%

Experimentally verified point‐to‐point iterative learning control for highly coupled systems

Freeman

Dinh

2014

Adaptive Control & Signal

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Iterative learning control (ILC) is a well-established approach for precision tracking control of systems, which perform a repeated tracking task defined over a fixed time interval. Despite a rich theoretical framework accompanied by a wide array of application studies, comparatively little attention has been paid to the case of multiple input, multiple output (MIMO) systems. Here, the presence of interacting dynamics often correlates with reduced performance. This article focuses on a general class of linear ILC algorithms and establishes links between interaction dynamics and reduced robustness to modeling uncertainty, and slower convergence. It then shows how these and other limitations can be addressed by relaxing the tracking requirement to include only a subset of time points along the time duration. This is the first analysis to show how so-called 'point-to-point' ILC can address performance limitations associated with highly coupled systems. Theoretical observations are tested using a novel MIMO experimental test facility, which permits both exogenous disturbance injection and a variable level of coupling between input and output pairs. Results compare experimental observations with theoretical predictions over a wide range of interaction levels and with varying levels of injected noise.POINT-TO-POINT ITERATIVE LEARNING CONTROL 303Of the small number of practical studies that have been reported, interaction between dynamics has been assumed negligible and is generally not considered [1,19,20]. With mild interaction, one approach is to treat the coupling as an exogenous disturbance and design multiple SISO ILC loops. This has yielded satisfactory results when applied to control each joint of a six degree-offreedom industrial robot [21]. The approach has also been taken in stroke rehabilitation with ILC used to control the electrical stimulation applied to muscles in the upper limb [22]. However, in the foregoing cases, a robustness filter was required to prevent instability and the tracking accuracy was considerably larger than when controlling a single joint (with the remaining joints locked). In the case of more significant multivariable coupling, this approach may therefore be expected to lead to a further loss of tracking accuracy and the likelihood of instability. A similar approach is multiaxis inversion-based ILC, which assumes a square system matrix and employs a diagonal system inverse that contains only the dominant dynamics for each output [23]. Here, omission of non-dominant dynamics leads to lack of convergence at frequencies at which the omitted dynamics have an overly large norm.Other practical studies have employed MIMO ILC to tackle vibration suppression. For example, in [24], an ILC approach is applied to a six degree of freedom LCD substrate transfer robot to reduce end-effector vibration. The reference is modified off-line to reduce link vibration using redundant actuation degrees of freedom. Another MIMO vibration suppression approach is to develop ILC algorithms that have two separate...

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“…Another advanced optimization based procedure for ILC systems with good performance results was proposed by Buchheit et al (1994). A Newton-Raphson search technique is used at each trial in order to find an ideal input that minimizes the cost criterion described by (10).…”

Section: Optimization Based Ilc Algorithmsmentioning

confidence: 99%

“…In fact, over the years many researchers have proposed optimization based ILC algorithms providing good convergence properties (Furuta and Yamakita 1987, Buchheit et al 1994. A more detailed description of these algorithms will be given in the next section.…”

Section: Introductionmentioning

confidence: 99%