Cascade force control for autonomous beating heart motion compensation

Dominici, Michel; Cortesão, Rui

doi:10.1016/j.conengprac.2014.12.012

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…Much work has been done investigating components or approaches to such a system. Research groups have developed vision algorithms for tissue tracking and heart motion modelling [8]- [10], compensation devices in the form of epicardial crawling robots [11], active mechanical tissue stabilizers [12], custom heart motion cancellation systems [13]- [15], specific control paradigms for heart motion compensation systems [16]- [18], and custom 3D heart simulators [18]. These studies have made significant progress on heartbeat synchronization for ECABG, but none present a clinically feasible solutions for a robotic system to perform 3D heart motion compensation.…”

Section: Introductionmentioning

confidence: 99%

Bimanual teleoperation with heart motion compensation on the da Vinci® Research Kit: Implementation and preliminary experiments

Ruszkowski

Schneider

Mohareri

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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This paper describes the implementation of a heart motion compensation system on the da Vinci surgical system (Intuitive Surgical Inc.) with the da Vinci Research Kit, for the purpose of simulating minimally invasive coronary artery bypass surgery on the beating heart. A Novint Falcon device is used to simulate the motion of the coronary bypass site. The 3D position of the heart simulator is measured optically in real time using an NDI Optotrak Certus tracker. The NDI measurements are used to command the da Vinci patient side manipulators to track, from a fixed distance, the simulated heart surface. Visual stabilization is achieved by having a stereo endoscopic camera carried by another patient-side manipulator that is also tracking the heart surface. The system performance is evaluated and discussed. In a preliminary evaluation study, surgeons were asked to perform a bimanual teleoperation suturing task on the simulated heart, with the gold standard being a suturing task on a stationary surface. When motion compensation was enabled, the median completion time dropped from 1.24 to 1.07 of the gold standard, the number of errors was reduced, and subjective measures show higher preference for the use of motion compensation in the da Vinci controllers.

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Section: Introductionmentioning

confidence: 99%

Bimanual teleoperation with heart motion compensation on the da Vinci® Research Kit: Implementation and preliminary experiments

Ruszkowski

Schneider

Mohareri

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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“…To cope with non‐linearities, disturbances, integral wind‐up and other complex issues in process systems, a number of cascade control strategies have been developed to improve either the outer loop performance or the inner loop performance or both. Examples such as the linear quadratic (LQ) self‐tuning controller [20], neuro‐fuzzy generalised predictive controller [1], model predictive controllers [2, 6, 7, 19] and neural network (NN)‐based controllers [3, 18] were reported for the outer loop primary controller design. Control algorithms have also been developed for the design of the secondary controller in the inner loop, for example, model‐reference adaptive control based on Kalman active observer [19], predictive control [6, 7] and sliding mode control [9], to name a few.…”

Section: Introductionmentioning

confidence: 99%

“…Examples such as the LQ self-tuning controller [20], neuro-fuzzy generalized predictive controller [1], model predictive controllers [2; 6; 7; 19] and neural network (NN) based controllers [3; 18] were reported for the outer loop primary controller design. Control algorithms have also been developed for the design of the secondary controller in the inner loop, for example, model-reference adaptive control based on Kalman active 2 observer [19], predictive control [6; 7] and sliding mode control [9], to name a few. In addition, there are methods developed to improve both the primary and the secondary controllers.…”

Section: Introductionmentioning

confidence: 99%

“…Cascade controllers are widely used in various industrial systems, such as superheated temperature control systems in power plants [1][2][3], automatic generation control of multi-area thermal systems [4], electric motors [5][6][7], converters [8; 9], single-phase shunt active power filters [10], networked control systems [11][12][13], flow-level systems [14], brake-by-wire actuator of sport motorcycles [15], diesel engines [16], under-actuated ball-and-beam systems [17], air handling units [18], robotic systems [19], and many others. In a typical cascade control system, PID controllers are used in both the inner loop and the outer loop.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive neural network cascade control system with entropy‐based design

Zhang

Zhou

Ren

et al. 2016

IET Control Theory & Applications

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This version is available at https://strathprints.strath.ac.uk/55772/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Abstract: A neural network (NN) based cascade control system is developed, in which the primary PID controller is constructed by NN. A new entropy-based measure, named the centred error entropy (CEE) index, which is a weighted combination of the error cross correntropy (ECC) criterion and the error entropy criterion (EEC), is proposed to tune the NN-PID controller. The purpose of introducing CEE in controller design is to ensure that the uncertainty in the tracking error is minimised and also the peak value of the error probability density function (PDF) being controlled towards zero. The NN-controller design based on this new performance function is developed and the convergent conditions are. During the control process, the CEE index is estimated by a Gaussian kernel function. Adaptive rules are developed to update the kernel size in order to achieve more accurate estimation of the CEE index. This NN cascade control approach is applied to superheated steam temperature control of a simulated power plant system, from which the effectiveness and strength of the proposed strategy are discussed by comparison with NN-PID controllers tuned with EEC and ECC criterions.

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