29 research outputs found

    Control of a magnetic microrobot navigating in microfluidic arterial bifurcations through pulsatile and viscous flow

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    International audienceNavigating in bodily fluids to perform targeted diagnosis and therapy has recently raised the problem of robust control of magnetic microrobots under real endovascular conditions. Various control approaches have been proposed in the literature but few of them have been experimentally validated. In this paper, we point out the problem of navigation controllability of magnetic microrobots in high viscous fluids and under pulsatile flow for endovascular applications. We consider the experimental navigation along a desired trajectory, in a simplified millimeter-sized arterial bifurcation, operating in fluids at the low-Reynolds-number regime where viscous drag significantly dominates over inertia. Different viscosity environments are tested (ranging from 100\% water-to-100\% glycerol) under a systolic pulsatile flow compatible with heart beating. The control performances in terms tracking, robustness and stability are then experimentally demonstrated

    A Robust controller for micro-sized agents: The prescribed performance approach

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    Applications such as micromanipulation and minimally invasive surgery can be performed using micro-sized agents. For instance, drug-loaded magnetic micro-/nano- particles can enable targeted drug delivery. Their precise manipulation can be assured using a robust motion controller. In this paper, we design a closed-loop controller-observer pair for regulating the position of microagents. The prescribed performance technique is applied to control the microagents to follow desired motion trajectories. The position of the microagents are obtained using microscopic images and image processing. The velocities of the microagents are obtained using an iterative learning observer. The algorithm is tested experimentally on spherical magnetic microparticles that have an average diameter of 100 m. The steady-state errors obtained by the algorithm are 20 m. The errors converge to the steady-state in approximately 8 second

    Optimal trajectory for a microrobot navigating in blood vessels

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    Magnetic microrobot control using an adaptive fuzzy sliding-mode method

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    The magnetic medical microrobots are influenced by diverse factors such as the medium, the geometry of the microrobot, and the imaging procedure. It is worth noting that the size limitations make it difficult or even impossible to obtain reliable physical properties of the system. In this research, to achieve a precise microrobot control using minimum knowledge about the system, an Adaptive Fuzzy Sliding-Mode Control (AFSMC) scheme is designed for the motion control problem of the magnetically actuated microrobots in presence of input saturation constraint. The AFSMC input consists of a fuzzy system designed to approximate an unknown nonlinear dynamical system and a robust term considered for mismatch compensation. According to the designed adaptation laws, the asymptotic stability is proved based on the Lyapunov theorem and Barbalat's lemma. In order to evaluate the effectiveness of the proposed method, a comparative simulation study is conducted

    Nonlinear modeling and robust controller-observer for a magnetic microrobot in a fluidic environment using MRI gradients

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    International audienceThis paper reports the use of a MRI device to pull a magnetic microrobot inside a vessel and control its trajectory. The bead subjected to magnetic and hydrodynamic forces is first modeled as a nonlinear control system. Then, a backstepping approach is discussed in order to synthesize a feedback law ensuring the stability along the controlled trajectory. We show that this control law, combined with a high gain observer, provides good tracking performances and robustness to measurement noise as well as to some matched uncertainties

    Dynamic behavior investigation for trajectory control of a microrobot in blood vessels

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    Adaptive backstepping and MEMS force sensor for an MRI-guided microrobot in the vasculature

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    Magnetic Microrobot Locomotion in Vascular System Using A Combination of Time Delay Control and Terminal Sliding Mode Approach

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    This thesis deals with designing a control law for trajectory tracking. The target is to move a microrobot in a blood vessel accurately. The microrobot is made of a ferromagnetic material and is propelled by a magnetic gradient coil. The controller combines time delay control (TDC) and terminal sliding mode (TSM) control. TDC allows deriving a control law without prior knowledge of the plant. As the system is a nonlinear function which also includes uncertainties and unexpected disturbance, TDC gives a benefit of less effort needed compared to model-based controller. Meanwhile, TSM term adds accuracy which it compensates TDC estimation error and also adds robustness against parameter variation and disturbance. In addition, anti-windup scheme acts as a support by eliminating the accumulated error due to integral term by TDC and TSM. So, the proposed controller can avoid actuator saturation problem caused by windup phenomenon. Simulations are conducted by copying a realistic situation. Accuracy and robustness evaluations are done in stages to see how each terms in a control law give an improvement and to see how an overall controller performs. โ“’ 2014 DGISTI. INTRODUCTION 1 -- 1.1. BACKGROUND 1 -- 1.2. RELATED RESEARCH 3 -- 1.3. OBJECTIVE 4 -- 1.4. SPECIFICATION 4 -- 1.5. SCOPE 5 -- 1.6. OVERVIEW 5 -- II. METHOD 6 -- 2.1. TIME DELAY CONTROL 6 -- 2.2. TERMINAL SLIDING MODE 9 -- 2.3. ANTI-WINDUP SCHEME 11 -- 2.4. PRACTICAL APPROACH 14 -- 2.4.1. FEEDBACK SIGNAL 14 -- 2.4.2. CONTROLLER GAIN SELECTION 15 -- 2.4.3. MEASUREMENT NOISE 16 -- 2.5. ADVANTAGES AND DRAWBACKS 16 -- III. RESULTS 17 -- 3.1. SIMULATION SETUP 17 -- 3.1.1. PLANT MODELING 18 -- 3.1.2. ACTUATOR AND POSITION SENSOR MODELING 20 -- 3.1.3. TRAJECTORY 21 -- 3.1.4. SIMULATION PARAMETER 21 -- 3.1.5. CONTROLLER TARGET 24 -- 3.2. ACCURACY AND ROBUSTNESS EVALUATION 24 -- 3.3. ANTI-WINDUP SCHEME EVALUATION 32 -- 3.4. SOLUTION FOR MEASUREMENT NOISE 35 -- 3.5. 2D SIMULATION 46 -- CONCLUSION AND FUTURE WORK 49 -- REFERENCES 50 -- ์š” ์•ฝ ๋ฌธ(ABSTRACT IN KOREAN) 52์ด ๋…ผ๋ฌธ์€ ๊ฒฝ๋กœ ์ถ”์ ์„ ์œ„ํ•œ ์ปจํŠธ๋กค ๋ฒ•์„ ์„ค๊ณ„ํ•œ ๊ฒƒ์ด๋‹ค. ๋ชฉํ‘œ๋Š” ํ˜ˆ๊ด€ ๋‚ด์—์„œ ์ •ํ™•ํ•˜๊ฒŒ ๋งˆ์ดํฌ๋กœ ๋กœ๋ด‡์˜ ์›€์ง์ด๋Š” ๊ฒƒ์ด๋‹ค. ๋งˆ์ดํฌ๋กœ ๋กœ๋ด‡์€ ๊ฐ•์ž์„ฑ์ฒด ๋ฌผ์งˆ๋กœ ๋งŒ๋“ค์–ด์ ธ ์žˆ๊ณ  ์ž๊ธฐ์žฅ์— ์˜ํ•ด์„œ ์ถ”์ง„ ๋œ๋‹ค. ์ปจํŠธ๋กค๋Ÿฌ๋Š” ์‹œ๊ฐ„์ง€์—ฐ์ œ์–ด๊ธฐ๋ฒ•(time delay control)๊ณผ terminal sliding ์ปจํŠธ๋กค์„ ํ•จ๊ป˜ ์‚ฌ์šฉํ•˜์˜€๋‹ค. TDC๋Š” ํ”Œ๋žœํŠธ์— ๋Œ€ํ•œ ์„ ํ–‰ ์ง€์‹ ์—†์ด ์ ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ์‹œ์Šคํ…œ์ด ๋ถˆํ™•์‹คํ•จ๊ณผ ์˜ˆ์ƒ์น˜ ๋ชปํ•œ ์™ธ๋ž€์„ ํฌํ•จํ•˜๊ณ  ์žˆ๋Š” ๋น„์„ ํ˜• ์ผ ๋•Œ TDC๋Š” ๋ชจ๋ธ ๊ธฐ๋ฐ˜์˜ ์ปจํŠธ๋กค๋Ÿฌ์— ๋น„ํ•ด ์ ์€ ๋…ธ๋ ฅ์ด ๋“œ๋Š” ์žฅ์ •์ด ์žˆ๋‹ค. ํ•œํŽธ, TSM์€ ์ •ํ™•๋„๋ฅผ ๋”ํ•˜์—ฌ TDC์˜ ์ฃผ์ •์—๋Ÿฌ๋ฅผ ๋ณด์ƒํ•˜๊ณ  ๋˜ํ•œ ๋งค๊ฐœ๋ณ€์ˆ˜์˜ ๋ณ€ํ™”์™€ ์™ธ๋ž€์— ๋ฐ˜ํ•œ ๊ฒฌ๊ณ ํ•จ์„ ๋”ํ•œ๋‹ค. ๊ฒŒ๋‹ค๊ฐ€ ์•ˆํ‹ฐ ์™€์ธ๋“œ ์—…์€ TDC์™€ TSM์˜ ์ ๋ถ„ ๋•Œ๋ฌธ์— ์ถ•์ ๋˜๋Š” ์—๋Ÿฌ๋ฅผ ์ œ๊ฑฐํ•˜๋Š” ์—ญํ• ์„ ํ•œ๋‹ค. ์ œ์•ˆํ•œ ์ปจํŠธ๋กค๋Ÿฌ๋Š” ์™€์ธ๋“œ์—… ํ˜„์ƒ์— ์˜ํ•œ ์ž‘๋™๊ธฐ์˜ ํฌํ™”ํ˜„์ƒ์„ ํ”ผํ•  ์ˆ˜ ์žˆ๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜์€ ์‹ค์ œ ํ˜„์ƒ์„ ๋”ฐ๋ผ ์‹œํ–‰๋˜์—ˆ๋‹ค. ์ •ํ™•๋„์™€ ๊ฒฌ๊ณ ํ•จ ํ‰๊ฐ€๋Š” ์ „์ฒด์ ์ธ ์ปจํŠธ๋กค๋Ÿฌ๊ฐ€ ์–ด๋–ป๊ฒŒ ์ˆ˜ํ–‰ํ•˜๋Š”๊ฐ€๋ฅผ ๋ณด๋Š” ๊ฒƒ๊ณผ ๊ฐ๊ฐ ์ปจํŠธ๋กค ๋ฐฉ๋ฒ•์ด ์ฃผ๋Š” ๊ฐœ์„ ์ ์„ ๋ณด๋Š” ๋‹จ๊ณ„๋กœ ์‹ค์‹œํ•˜์˜€๋‹ค. โ“’ 2014 DGISTMasterdCollectio
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