Adaptive Passivity-Based Multi-Task Tracking Control for Robotic Manipulators

Garofalo, Gianluca; Wu, Xuwei; Ott, Christian

doi:10.1109/lra.2021.3095930

Cited by 14 publications

(9 citation statements)

References 27 publications

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“…. , n, where Πi ∈ S + (4) is the current estimate of Π i ; γ > 0 is the adaptation gain; L i ∈ S(4) is the unique solution of tr(Π i L i ) = π T i l i [7], [25]. The concrete expression of l i ∈ R 10 will be shown for the proposed controller in Section III.…”

Section: B Natural Adaptation Lawmentioning

confidence: 99%

“…The control design starts with replacing the ideal dynamic matrices in (7) by the respective estimates, denoted by ( •), leading to the control law τ = M (q)q r + Ĉ(q, q) qr + ĝ(q) − Hs…”

Section: A Combining Composite Adaptive Controller With Avamentioning

confidence: 99%

“…The prediction error is defined as ỹ = W π − y = W π with π = π − π. The matrix L i in ( 6) is then determined by solving [7]…”

Section: A Combining Composite Adaptive Controller With Avamentioning

confidence: 99%

“…The proposed framework is denoted by CA+eAVA in the following. AVA and CA+AVA are tested using the same control and auxiliary parameters 7 as CA+eAVA. An accompanying video shows all experiments.…”

Section: Experimental Validationmentioning

confidence: 99%

“…To attain high tracking accuracy under model uncertainties at run-time, e.g., due to manipulation of an unknown object, it is common to use model-based adaptive control, which performs online parameter adaptation driven by tracking errors [1], prediction errors [2], or both error sources in a composite manner [3]. Recently, Lee et al [4] proposed a natural adaptation law that guarantees the physical consistency [5], [6] of the estimated inertial parameters, with additional advantages of improved transient performance and less laborious gain selection; a recent application can be found in [7]. However, these adaptive control schemes only adapt the feedforward term but do not adjust the feedback term according to model uncertainties.…”

mentioning

confidence: 99%

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Adaptive Tracking Control With Uncertainty-Aware and State-Dependent Feedback Action Blending for Robot Manipulators

Kirner

Garofalo

et al. 2022

IEEE Robot. Autom. Lett.

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Adaptive control can significantly improve tracking performance of robot manipulators subject to modeling errors in dynamics. In this letter, we propose a new framework combining the composite adaptive controller using a natural adaptation law and an extension of the adaptive variance algorithm (AVA) for controller blending. The proposed approach not only automatically adjusts the feedback action to reduce the risk of violating actuator constraints but also anticipates substantial modeling errors by means of an uncertainty measure, thus preventing severe performance deterioration. A formal stability analysis of the closed-loop system is conducted. The control scheme is experimentally validated and directly compared with baseline methods on a torque-controlled KUKA LWR IV+.

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Section: B Natural Adaptation Lawmentioning

confidence: 99%

Section: A Combining Composite Adaptive Controller With Avamentioning

confidence: 99%

“…The prediction error is defined as ỹ = W π − y = W π with π = π − π. The matrix L i in ( 6) is then determined by solving [7]…”

Section: A Combining Composite Adaptive Controller With Avamentioning

confidence: 99%

Section: Experimental Validationmentioning

confidence: 99%