Robust adaptive identification of slowly time-varying parameters with bounded disturbances

Mathelin, M.F. de; Lozano, Rogelio

doi:10.23919/ecc.1997.7082448

Cited by 29 publications

(39 citation statements)

References 2 publications

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“…Indeed, the limitations of the recursive algorithms are underlined in [19]. The stability of these algorithms is not guaranteed without the introduction of a dead zone or a stabilization term when considering noises and disturbances.…”

Section: A With a Classical 2-d Probe 1) 3-d Filtermentioning

confidence: 99%

“…At the same time, the improvement brought by these modifications in terms of robustness is done at the expense of the algorithm efficiency. Finally, when dealing with noised signals, direct identification methods are shown to offer better robustness and results [19].…”

Section: A With a Classical 2-d Probe 1) 3-d Filtermentioning

confidence: 99%

“…This estimate is defined in [19] for the multidimensional case and can be written as follows for a monodimensional problem:θ…”

Section: A With a Classical 2-d Probe 1) 3-d Filtermentioning

confidence: 99%

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Intensity-Based Ultrasound Visual Servoing: Modeling and Validation With 2-D and 3-D Probes

Nadeau

Krupa

2013

IEEE Trans. Robot.

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Abstract-In this paper, we present an ultrasound (US) visual servoing to control a robotic system equipped with a US probe. To avoid the difficult and time-consuming image segmentation process, we develop a new approach taking as visual input directly the intensity of the image pixels. The analytic form of the interaction matrix that relates the variation of the intensity features to the motion of the probe is established and used to control the six degrees of freedom (dof) of the robotic system. Our approach is applied with a 2-D and a 3-D US probe, and the results that are obtained with both sensors are compared in simulation. The 2-D probe shows good performances for tracking tasks and the 3-D one, which ensures a larger domain of convergence, is more particularly used for positioning tasks. The intensity-based approach is validated through experimental results performed with a realistic abdominal phantom and with animal soft tissue.Index Terms-Intensity-based control, robotic system, ultrasound (US), visual servoing.

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Section: A With a Classical 2-d Probe 1) 3-d Filtermentioning

confidence: 99%

Section: A With a Classical 2-d Probe 1) 3-d Filtermentioning

confidence: 99%

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Intensity-Based Ultrasound Visual Servoing: Modeling and Validation With 2-D and 3-D Probes

Nadeau

Krupa

2013

IEEE Trans. Robot.

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“…In general, exponential convergence in the constant parameter case, guarantees some degree of tracking for a sufficiently slowly-varying signal [21], [22]. The topic of time-varying parameters has been the focus of several studies [23], [24], [25], [26].…”

Section: Introductionmentioning

confidence: 99%

Exponential convergence bounds in least squares estimation: Identification of viscoelastic properties in atomic force microscopy

Ragazzon

Gravdahl

Pettersen

2017

2017 IEEE Conference on Control Technology and Applications (CCTA)

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Abstract-Using atomic force microscopy (AFM) for studying soft, biological material has become increasingly popular in recent years. New approaches allow the use of recursive least squares estimation to identify the viscoelastic properties of a sample in AFM. As long as the regressor vector is persistently exciting (PE), exponential convergence of the parameters to be identified can be guaranteed. However, even exponential convergence can be slow. In this article, upper bounds on the parameter convergence is found, completely determined by the PE properties and least squares update law parameters. Furthermore, for a parameter vector which is piecewise constant at regular intervals, the time interval necessary for the error to converge to any specified upper limit is determined. For a soft sample in AFM, the viscoelastic properties can be spatially inhomogeneous. These properties can be spatially resolved by periodically tapping at discrete points along the sample. The results of this article then allows us to determine the time interval necessary at each tap in order to guarantee convergence to any specified fraction of the step-change in the parameters. Simulation results are presented, demonstrating the applicability of the approach.

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“…It was also suggested to learn the inverse jacobian directly, instead of the jacobian [9], although only an offline formulation was proposed in that work. A complete discussion about adaptive identification methods for slowly varying parameters is presented in [2]. It also presents a new method to improve the robustness of parameter identification, by combining directions with new information with those where the information had been lost and had to be recovered.…”

Section: Introductionmentioning

confidence: 99%

Jacobian Learning Methods for Tasks Sequencing in Visual Servoing

Mansard

Lopes

Santos-Victor

et al. 2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper, the coupling between Jacobian learning and task sequencing through the redundancy approach is studied.It is well known that visual servoing is robust to modeling errors in the jacobian matrices. This justifies why jacobian estimation does not usually degrade the system convergence. However, we show that this is not true anymore when the redundancy formalism is used. In this case the jacobian matrix is also necessary to compute projection operators for task decomposition, which is quite sensitive to errors. We show that learning improves the servoing performance, when task sequencing is used. Conversely, sequencing improves the convergence of learning, especially for tasks involving several degrees of freedom. Eye-in-hand and eye-to-hand experiments have been performed on two robots with six degrees of freedom.

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Robust adaptive identification of slowly time-varying parameters with bounded disturbances

Cited by 29 publications

References 2 publications

Intensity-Based Ultrasound Visual Servoing: Modeling and Validation With 2-D and 3-D Probes

Intensity-Based Ultrasound Visual Servoing: Modeling and Validation With 2-D and 3-D Probes

Exponential convergence bounds in least squares estimation: Identification of viscoelastic properties in atomic force microscopy

Jacobian Learning Methods for Tasks Sequencing in Visual Servoing

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