ILC for a fast linear axis driven by pneumatic muscle actuators

Schindele, Dominik; Aschemann, Harald

doi:10.1109/icmech.2011.5971256

Cited by 15 publications

(12 citation statements)

References 13 publications

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“…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]. It is shown in [32] that NOILC is actually a generalization of 'gradient ILC', also termed 'adjoint ILC', which has been studied by many groups, including [33][34][35][36].…”

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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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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“…However, the implementation of ILC on PM systems is rare. The norm-optimal ILC (NOILC) is introduced for PM tracking by minimizing an iterationdependent quadratic function [15]. Since the computation of matrix gain requires explicit system knowledge, parametric uncertainties can not be handled.…”

Section: Introductionmentioning

confidence: 99%

Robust Iterative Learning Control for Pneumatic Muscle With Uncertainties and State Constraints

Qian

Chakrabarty

et al. 2023

IEEE Trans. Ind. Electron.

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“…As a specific class of modelbased ILC, gradient based algorithms have drawn considerable attention in both theoretical and application domains due to their well-defined convergence and robustness properties [30]. This class includes gradient ILC [34,31], inverse ILC [32] and norm optimal ILC [9,1], which have been applied to diverse applications including gantry robots [35], multi-axis robotic testbeds [5], rehabilitation platforms [36] and pneumatic muscle actuators [39].…”

Section: Introductionmentioning

confidence: 99%

Data-driven gradient-based point-to-point iterative learning control for nonlinear systems

2020

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Iterative learning control (ILC) is a wellestablished methodology which has proven successful in achieving accurate tracking control for repeated tasks. However, the majority of ILC algorithms require a nominal plant model and are sensitive to modelling mismatch. This paper focuses on the class of gradientbased ILC algorithms and proposes a data-driven ILC implementation applicable to a general class of nonlinear systems, in which an explicit model of the plant dynamics is not required. The update of the control signal is generated by an additional experiment executed between ILC trials. The framework is further extended by allowing only specific reference points to be tracked, thereby enabling faster convergence. Necessary convergence conditions and corresponding convergence rates for both approaches are derived theoretically. The proposed data-driven approaches are demonstrated through application to a stroke rehabilitation problem requiring accurate control of nonlinear artificiallystimulated muscle dynamics.

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ILC for a fast linear axis driven by pneumatic muscle actuators

Cited by 15 publications

References 13 publications

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

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

Robust Iterative Learning Control for Pneumatic Muscle With Uncertainties and State Constraints

Data-driven gradient-based point-to-point iterative learning control for nonlinear systems

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