Control of magnetotactic bacterium in a micro-fabricated maze

We demonstrate the closed-loop control of a magnetotactic bacterium (MTB), i.e., Magnetospirillum magnetotacticum, within a micro-fabricated maze using a magneticbased manipulation system. The effect of the channel wall on the motion of the MTB is experimentally analyzed. This analysis is done by comparing the characteristics of the transient- and steady-states of the controlled MTB inside and outside a microfabricated maze. In this analysis, the magnetic dipole moment of our MTB is characterized using a motile technique (the u-turn technique), then used in the realization of a closed-loop control system. This control system allows the MTB to reach reference positions within a micro-fabricated maze with a channel width of 10 μm, at a velocity of 8 μm/s. Further, the control system positions the MTB within a region-of-convergence of 10 μm in diameter. Due to the effect of the channel wall, we observe that the velocity and the positioning accuracy of the MTB are decreased and increased by 71% and 44%, respectively

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

Design and Optimization of PID Controller using Various Algorithms for Micro-Robotics System

Microparticles have the potentials to be used for many medical purposes in-side the human body such as drug delivery and other operations. This paper attempts to provide a thorough comparison between five meta-heuristic search algorithms:  Sparrow Search Algorithm (SSA), Flower Pollination Algorithm (FPA), Slime Mould Algorithm (SMA), Marine Predator Algorithm (MPA), and Multi-Verse Optimizer (MVO). These approaches were used to calculate the PID controller optimal indicators with the application of different functions, including Integral Absolute Error (IAE), Integral of Time Multiplied by Square Error (ITSE), Integral Square Time multiplied square Error (ISTES), Integral Square Error (ISE), Integral of Square Time multiplied by square Error (ISTSE), and Integral of Time multiplied by Absolute Error (ITAE). Every method of controlling was presented in a MATLAB Simulink numerical model, and LABVIEW software was used to run the experimental tests. It is observed that the MPA technique achieves the highest values of settling error for both simulation and experimental results among other control approaches, while the SSA approach reduces the settling error by 50% compared to former experiments. The results indicate that SSA is the best method among all approaches and that ISTES is the best choice of PID for optimizing the controlling parameters

Design and Fabrication of a Magnetic Manipulator with Five Degrees of Freedom

Magnetic manipulation has the potential to recast the medical field both from an operational and drug delivery point of view as it can provide wireless controlled navigation over surgical devices and drug containers inside a human body. The accuracy and precision of controlled navigation will provide access to delicate organs and decrease the rehabilitation time. The advantages of achieving such a task have absorbed engineers' and researchers' attention and effort in the electromagnetic, imaging, mechanical, and robotic fields to implement the principles and make a functional magnetic manipulator. The main idea behind magnetic manipulators is to regulate electrical currents fed to the coils to precisely position and orient an agent- also known as a robot or a magnetic tool- inside a working space. The presented system in this research is formed with nine coils, also known as electromagnets, placed normal to the spherical volume. The radius of this space is directly correlated with the dimensions and the number of coils, which can be utilized to parameterize the spatial constraints. Extending the number of coils forming a spherical volume, also known as spherical workspace, has led to developing a unique geometrical constraint to optimize the coil placement. The determination of the constraints resulted in a specific outer diameter for each coil. In order to design a coil that produces the maximum axial force with the least power combustion with a given outer radius, Fabry Factor equation and Finite Element Method (FEM) were adopted. Fabry Factor relates the dimensions of the coils to each other such that the power consumption is minimized. Therefore, various iron-core coils were simulated using this method, and then the axial force of each coil at the furthest operational point in the working space was measured using FEM. The optimization result led to a cylindrical iron-core coil with an inner diameter of 20.5 mm, an outer diameter of 66 mm, and a length of 124 mm. The FEM results in 3D for a complex system is mostly associated with errors between actual and simulated values of the magnetic field, around 17 percent less than the actual values in this project. In order to eliminate this error, the magnetic field of the manufactured coil had been predicted using Artificial Intelligence (AI) techniques for experimental purposes. Regression models of Artificial Neural Network (ANN), a hybrid method called Artificial Neural Network with Simulated Annealing (ANN/SA), and Gene Expression Programming (GEP) had been built individually. ANN/SA has shown outstanding performance with an R-squared equal to 0.99 and root mean square error of 0.0028; hence, it has been used in the actuation process for magnetic field prediction. Finally, to indicate the functionality of the system, a simple 1D PI actuation logic with = 3.25 and =0.01 using a laser sensor had been successfully investigated. It first predicts the magnetic field using ANN/SA at the agent's current position provided by the laser sensor; then, regulates the current flowing through each coil till the agent settles at the final destination

Image-based magnetic control of paramagnetic microparticles in water

This paper describes the design of a system for controlling the position of spherical paramagnetic microparticles that have an average diameter of 100 µm. The focus of this study lies in designing and implementing a system that uses microscopic images and electromagnets. Preliminary experiments have been done to verify the feasibility of the system to track and control the position of these particles. A vibrating sample magnetometer was used to determine the magnetic moment of the particles. Finite element method simulations were used to verify the magnetic behavior of the designed setup. The system was used to position the particles within 8.4µm of a setpoint, achieving speeds of up to 235µm s−1. We also demonstrated that the particle could follow a circular and a figure-eight path