Iterative feedback tuning applied to robot joint controllers

Gunnarsson, Svante; Rousseaux, O.; Collignon, Vincent

doi:10.1016/s1474-6670(17)56797-x

Cited by 11 publications

(3 citation statements)

References 6 publications

(9 reference statements)

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“…Joint control of a PUMA 560 robot is considered in Reference [75] and References [76,77] contain results from joint control of a non-linear simulation model of a robot.…”

Section: Applicationsmentioning

confidence: 99%

Iterative feedback tuning—an overview

Hjalmarsson

2002

Adaptive Control & Signal

375

228

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Adaptive and iterative control algorithms based on explicit criterion minimization are briefly reviewed and an overview of one such algorithm, iterative feedback tuning (IFT) , is presented. The basic IFT algorithm is reviewed for both single-input/single-output and multi-input/multi-output systems. Subsequently the application to non-linear systems is discussed. Stability and robustness aspects are covered. A survey of existing extensions, applications and related methods is also provided. ; 16:373-395 H. HJALMARSSON Sensitivity modelsLater it was recognized that for linear time-invariant, processes, the sensitivity of any signal, sðtÞ say, in the system w.r.t. any scalar parameter r in the system, i.e. the partial derivative @sðtÞ=@r;

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“…Joint control of a PUMA 560 robot is considered in Reference [75] and References [76,77] contain results from joint control of a non-linear simulation model of a robot.…”

Section: Applicationsmentioning

confidence: 99%

Iterative feedback tuning—an overview

Hjalmarsson

2002

Adaptive Control & Signal

375

228

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“…They are either based on a reference model, e.g. Virtual Reference Feedback Tuning (VRFT) [5], Iterative Learning [6], or they exploit more recent model-free techniques, e.g. based on Reinforcement Learning [7].…”

Section: Introductionmentioning

confidence: 99%

Advanced control based on Recurrent Neural Networks learned using Virtual Reference Feedback Tuning and application to an Electronic Throttle Body (with supplementary material)

D'Amico¹,

Farina²,

Panzani³

2021

Preprint

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In this paper the application of Virtual Reference Feedback Tuning (VRFT) for control of nonlinear systems with regulators defined by Echo State Networks (ESN) and Long Short Term Memory (LSTM) networks is investigated. The capability of this class of regulators of constraining the control variable is pointed out and an advanced control scheme that allows to achieve zero steady-state error is presented. The developed algorithms are validated on a benchmark example that consists of an electronic throttle body (ETB).

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“…The optimal set of parameters corresponds to a minimum of a performance-relevant cost function in time domain. IFT has been successfully applied in many applications including process industry [9], [29], robotics [10], [23], [37], mechatronics [1], [28], and stage control [46]. Most of these works involve linear (time-invariant) systems.…”

Section: Introductionmentioning

confidence: 99%