Michel de Mathelin

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

In this paper, the robustness limitations of current recursive identification algorithms are highlighted. Then, it is shown how a particular nonrecursive identification algorithm dramatically improves the robustness to bounded disturbances, noise and slowly time-varying parameters. only at the expense of performing an on-line eigenvalue decomposition on a symmetric semidefinite positive matrix. Furthermore, this algorithm do not require any a priori knowledge of a bound on the disturbance and noise and of a bound on the parameter values.

Toward robotized beating heart TECABG: assessment of the heart dynamics using high-speed vision

Active robotic filtering is a promising solution for beating heart Totally Endoscopic Coronary Artery Bypass Grafting (TECABG). n this work, we assess the heart motion dynamics using simultaneously igh speed imaging of optical markers attached to the heart, ECG signals and ventilator airflow acquisitions. Our goal is to make an assessment of the heart motion (shape, velocity, acceleration) in order to be able to make more accurate specifications for a dedicated robot that could follow this motion in real-time. Furthermore, using the 2 additional inputs (ECG, airflow), we propose a prediction algorithm of the motion that could be used with a predictive control algorithm to improve the tracking accuracy.

CONTINUOUS-TIME MODEL IDENTIFICATION OF ROBOT FLEXIBILITIES FOR FAST VISUAL SERVOING

This paper presents a general methodology for identifying the dynamical part of the continuous-time model of an articulated arm including flexibilities dedicated to visual servoing. Based on this model, a H-infinity control law is designed and implemented, allowing to reach high dynamics.

Model-less visual servoing using modified simplex optimization

This article describes a robot positioning task with respect to a static target by visual servoing. The vision system is uncalibrated, and the kinematic model of the robot may be totally unknown. The displacements of the robot at joint level are generated in real time in order to minimize the objective function. The objective function includes the quadratic error between the current and the desired target images. A simplex method is used to minimize the objective function, and a Newton-like method is also used near convergence. We successfully validated this method with simulations under the graphic library OpenGL.

ADAPTIVE REJECTION OF ECCENTRICITY TENSION DISTURBANCES IN WEB TRANSPORT SYSTEMS

This paper presents adaptive algorithms to compensate roll eccentricity perturbations in web transport systems. Roll eccentricity creates tension disturbances whose frequencies may be slowly varying due to the change of radius of the roll. The magnitude and phase of these perturbations are estimated through an adaptive algorithm that cancels the effect of these perturbations. The ability of the algorithm to reject eccentricity perturbations with slowly varying frequencies is shown through simulations on the physical model, as well as experimental results.

Model predictive control for cancellation of repetitive organ motions in robotized laparoscopic surgery

Periodic deformations of organs which are due to respiratory movements may be critical disturbances for surgeons manipulating robotic control systems during laparoscopic interventions or tele-surgery. Indeed, the surgeon has to manually compensate for these motions if accurate gestures are needed, like, e.g., during suturing. This work presents a model predictive control scheme that is applied to the problem of maintaining a constant distance in the endoscopic images from a surgical tools tip to the organs surface. A new optimization criterion is developed for an unconstrained generalized predictive controller based on a repetitive input-output model, where contributions of the control input to reference tracking and to disturbance rejection are split and computed separately. Thanks to this approach, mechanical filtering of the repetitive disturbances and teleoperation by the surgeon can run simultaneously and independently on the robot arm. The system is tested on both an endotraining box with a surgical robot and in in vivo conditions on a living pig. Results are shown to validate the control scheme and its application.

Canonical H∞ state-space parametrization

Abstract In this paper, a new parametrization of all stable multivariable systems satisfying a given bound on their H ∞ norm is presented. This parametrization extends previous results in the sense that it uses a minimum amount of parameters and that it comprises also proper systems. The parametrization has a number of parameters similar to that of classical multivariable state-space forms. The construction of the parametrization is based on the bounded real lemma and the reduction to Hessenberg form by unitary similarity transformations. The parametrization can be used to select the free parameters in a class of all H ∞ controllers in order to include regulation constraints.

ACCOUNTING FOR FLEXIBILITIES IN VISUAL SERVOING

Abstract In this paper is considered visual servoing of multi-link robotic arms including flexibilities. It is shown that even slight flexibilities in the structure make the classical strategy unproper. A H ∞ controller is designed for a given working point that allows to handle properly the flexibilities. It is shown that the control strategy allows to operate within a working domain that can be sufficient for some applications. As a demonstrative example, the case of a two-links arm is considered. The model is obtained thanks to the DynaFlex toolbox.

H ∞ and LPV controller for visual servoing of a robotic arm with flexibility

Abstract This paper addresses visual servoing of an articulated arm including flexibility. The issue is to design a controller allowing good behaviour of the system on the working domain. Indeed, the system behaviour depends on the operating point. A linear parameter-varying model is developed for control synthesis, partly based on modelling and partly based on identification. It handles the dynamics and the dependence of the visual servoing loop with respect to the operating point. A H∞ controller, based on a nominal model is first considered. In order to improve the behaviour over the whole domain, a LPV controller is then experimented.

Model predictive control to cancel repetitive deformations of organs in robotized laparoscopic surgery

Abstract Periodic deformations of organs which are due to respiratory movements may be critical disturbances for surgeons manipulating robotic control systems during laparoscopic interventions or tele-surgery. Indeed, the surgeon has to manually compensate for these motions if accurate gestures are needed, like, e. g., during suturing. This work presents a model predictive control scheme that is applied to the problem of maintaining a constant distance in the endoscopic images from the surgical tools tip to the organs surface. A new optimization criterion is developed for an unconstrained generalized predictive controller based on a repetitive input-output model, where contributions of the control input to reference tracking and to disturbance rejection are split and computed separately. Thanks to this approach, mechanical filtering of the repetitive disturbances and teleoperation by the surgeon can run simultaneously and independently on the robot arm. Results from laboratory experiments carried out on an endo-training box with a surgical robot are shown to validate the control scheme and its application.