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

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

Magnetic microrobot control using an adaptive fuzzy sliding-mode method

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

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

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. ⓒ 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 -- 요 약 문(ABSTRACT IN KOREAN) 52이 논문은 경로 추적을 위한 컨트롤 법을 설계한 것이다. 목표는 혈관 내에서 정확하게 마이크로 로봇의 움직이는 것이다. 마이크로 로봇은 강자성체 물질로 만들어져 있고 자기장에 의해서 추진 된다. 컨트롤러는 시간지연제어기법(time delay control)과 terminal sliding 컨트롤을 함께 사용하였다. TDC는 플랜트에 대한 선행 지식 없이 적용할 수 있다. 시스템이 불확실함과 예상치 못한 외란을 포함하고 있는 비선형 일 때 TDC는 모델 기반의 컨트롤러에 비해 적은 노력이 드는 장정이 있다. 한편, TSM은 정확도를 더하여 TDC의 주정에러를 보상하고 또한 매개변수의 변화와 외란에 반한 견고함을 더한다. 게다가 안티 와인드 업은 TDC와 TSM의 적분 때문에 축적되는 에러를 제거하는 역할을 한다. 제안한 컨트롤러는 와인드업 현상에 의한 작동기의 포화현상을 피할 수 있다. 시뮬레이션은 실제 현상을 따라 시행되었다. 정확도와 견고함 평가는 전체적인 컨트롤러가 어떻게 수행하는가를 보는 것과 각각 컨트롤 방법이 주는 개선점을 보는 단계로 실시하였다. ⓒ 2014 DGISTMasterdCollectio

Modeling, simulation and control of microrobots for the microfactory.

Future assembly technologies will involve higher levels of automation in order to satisfy increased microscale or nanoscale precision requirements. Traditionally, assembly using a top-down robotic approach has been well-studied and applied to the microelectronics and MEMS industries, but less so in nanotechnology. With the boom of nanotechnology since the 1990s, newly designed products with new materials, coatings, and nanoparticles are gradually entering everyone’s lives, while the industry has grown into a billion-dollar volume worldwide. Traditionally, nanotechnology products are assembled using bottom-up methods, such as self-assembly, rather than top-down robotic assembly. This is due to considerations of volume handling of large quantities of components, and the high cost associated with top-down manipulation requiring precision. However, bottom-up manufacturing methods have certain limitations, such as components needing to have predefined shapes and surface coatings, and the number of assembly components being limited to very few. For example, in the case of self-assembly of nano-cubes with an origami design, post-assembly manipulation of cubes in large quantities and cost-efficiency is still challenging. In this thesis, we envision a new paradigm for nanoscale assembly, realized with the help of a wafer-scale microfactory containing large numbers of MEMS microrobots. These robots will work together to enhance the throughput of the factory, while their cost will be reduced when compared to conventional nanopositioners. To fulfill the microfactory vision, numerous challenges related to design, power, control, and nanoscale task completion by these microrobots must be overcome. In this work, we study two classes of microrobots for the microfactory: stationary microrobots and mobile microrobots. For the stationary microrobots in our microfactory application, we have designed and modeled two different types of microrobots, the AFAM (Articulated Four Axes Microrobot) and the SolarPede. The AFAM is a millimeter-size robotic arm working as a nanomanipulator for nanoparticles with four degrees of freedom, while the SolarPede is a light-powered centimeter-size robotic conveyor in the microfactory. For mobile microrobots, we have introduced the world’s first laser-driven micrometer-size locomotor in dry environments, called ChevBot to prove the concept of the motion mechanism. The ChevBot is fabricated using MEMS technology in the cleanroom, following a microassembly step. We showed that it can perform locomotion with pulsed laser energy on a dry surface. Based on the knowledge gained with the ChevBot, we refined tits fabrication process to remove the assembly step and increase its reliability. We designed and fabricated a steerable microrobot, the SerpenBot, in order to achieve controllable behavior with the guidance of a laser beam. Through modeling and experimental study of the characteristics of this type of microrobot, we proposed and validated a new type of deep learning controller, the PID-Bayes neural network controller. The experiments showed that the SerpenBot can achieve closed-loop autonomous operation on a dry substrate

Particle Computation: Complexity, Algorithms, and Logic

We investigate algorithmic control of a large swarm of mobile particles (such as robots, sensors, or building material) that move in a 2D workspace using a global input signal (such as gravity or a magnetic field). We show that a maze of obstacles to the environment can be used to create complex systems. We provide a wide range of results for a wide range of questions. These can be subdivided into external algorithmic problems, in which particle configurations serve as input for computations that are performed elsewhere, and internal logic problems, in which the particle configurations themselves are used for carrying out computations. For external algorithms, we give both negative and positive results. If we are given a set of stationary obstacles, we prove that it is NP-hard to decide whether a given initial configuration of unit-sized particles can be transformed into a desired target configuration. Moreover, we show that finding a control sequence of minimum length is PSPACE-complete. We also work on the inverse problem, providing constructive algorithms to design workspaces that efficiently implement arbitrary permutations between different configurations. For internal logic, we investigate how arbitrary computations can be implemented. We demonstrate how to encode dual-rail logic to build a universal logic gate that concurrently evaluates and, nand, nor, and or operations. Using many of these gates and appropriate interconnects, we can evaluate any logical expression. However, we establish that simulating the full range of complex interactions present in arbitrary digital circuits encounters a fundamental difficulty: a fan-out gate cannot be generated. We resolve this missing component with the help of 2x1 particles, which can create fan-out gates that produce multiple copies of the inputs. Using these gates we provide rules for replicating arbitrary digital circuits.Comment: 27 pages, 19 figures, full version that combines three previous conference article

An Input Saturation-Tolerant Position Control Method for Magnetic Microrobots Using Adaptive Fuzzy Sliding-Mode Method

The position control of magnetic medical microrobots is influenced by several environmental uncertainties including the unknown characteristics of the medium and the imaging precision. Furthermore, measuring the physical attributes of the microrobots is a challenging issue. To provide a model-free position control approach for the magnetic medical microrobots, a saturation-tolerant Adaptive Fuzzy Sliding-Mode Control (AFSMC) is designed in this study. In the proposed approach, each control input comprises a fuzzy inference term utilized to approximate an unknown nonlinear function including uncertain forces, a robust part derived to compensate for the fuzzy approximation error and disturbances, and a compensating gain for input saturation. By utilizing the second theorem of Lyapunov and Barbalat’s lemma, it is proved that the closed-loop system is asymptotically stable. The effectuality of the presented controller is assessed by means of two experimental scenarios. The results show that the magnitudes of the tracking errors corresponding to a spiral reference path are less than 0.2 mm at the end of the motion. Moreover, in the test conducted in a 3D printed Aorta phantom, the minimum and the maximum values of the tracking errors are $-$ 1.22 mm and 0.95 mm, respectively. Note to Practitioners —This article introduces an innovative, model-free technique specifically designed to tackle the complex challenges of position control in magnetic medical microrobots. Achieving precise control over these microrobots is a challenging task, compounded by the complexity of accurately measuring their physical properties and the characteristics of their surrounding medium. This challenge is further exacerbated by the issue of input saturation, which can compromise system stability. Our pioneering control method is designed to navigate these obstacles effectively. It functions under the assumption that both the lower and upper saturation limits are unknown, and it eliminates the necessity to model the forces acting on the microrobot. Experimental results confirm the method’s effectiveness in accurately tracking various reference trajectories. These findings suggest that our method holds significant promise for various medical applications

A review of modeling and control of remote-controlled capsule endoscopes.

INTRODUCTION: The significance of this review lies in addressing the limitations of passive locomotion in capsule endoscopes, hindering their widespread use in medical applications. The research focuses on evaluating existing miniature in vivo remote-controlled capsule endoscopes, examining their locomotion designs, and working theories to pave the way for overcoming challenges and enhancing their applicability in diagnostic and treatment settings. AREAS COVERED: This paper explores control methods and dynamic system modeling in the context of self-propelled remote-controlled capsule endoscopes with a two-mass arrangement. The literature search, conducted at Queen Mary University of London Library from 2000 to 2022, utilized a systematic approach starting with the broad keyword 'Capsule Endoscope' and progressively narrowing down to specific aspects such as 'Capsule Endoscope Control' and 'Self-propelled Capsule Endoscope' using various criteria. EXPERT OPINION: Efficiently driving and controlling remote-controlled capsule endoscopes have the potential to overcome the current limitations in medical technology, offering a viable solution for diagnosing and treating gastrointestinal diseases. Successful control of the remote-controlled capsule endoscope, as demonstrated in this review paper, will lead to a step change in medical engineering, establishing the remote-controlled capsule endoscope as a swift standard in the field

Magnetic motion control and planning of untethered soft grippers using ultrasound image feedback

Soft miniaturized untethered grippers can be used to manipulate and transport biological material in unstructured and tortuous environments. Previous studies on control of soft miniaturized grippers employed cameras and optical images as a feedback modality. However, the use of cameras might be unsuitable for localizing miniaturized agents that navigate within the human body. In this paper, we demonstrate the wireless magnetic motion control and planning of soft untethered grippers using feedback extracted from B-mode ultrasound images. Results show that our system employing ultrasound images can be used to control the miniaturized grippers with an average tracking error of 0.4±0.13 mm without payload and 0.36±0.05 mm when the agent performs a transportation task with a payload. The proposed ultrasound feedback magnetic control system demonstrates the ability to control miniaturized grippers in situations where visual feedback cannot be provided via cameras