Abstract
The potential of microrobots to bring about revolutionary changes over micro-operation demands is increasing day to day. This paper presents a controller to provide 5 degrees of freedom for an underactuated bio-inspired helical swimming microrobot. The considered system is a helical swimming microrobot with three flagella in a low Reynolds performance environment. Control of the considered system is performed to reach any desired location, roll angle and pitch angle. The proposed controlling error definition extracted from the system geometry is general for similar actuation configurations. An error detection method for multi-propulsion-unit systems is utilized for 5-DOF micromanipulation of an underactuated bio-inspired helical swimming microrobot by fuzzy-PI controller. A fuzzy-PI controller is proposed to use modified experimental data of PI controller debugging to maintain a suitable efficient control. The comparison of two other possible controllers and the proposed fuzzy-PI controller is discussed, and the performance of trajectory tracking is evaluated by simulations.
Highlights
IntroductionThere are different tools for controlling complex systems with uncertainties
Biomedical microrobots can be used for drug delivery [1], brachytherapy [2], stem cells implantation [3], artery ablation, biopsy, and so on [4]
The proposed controlling error definition extracted from system geometry is general
Summary
There are different tools for controlling complex systems with uncertainties Among these controllers, fuzzy logic controllers have shown good performance and efficiency. Fuzzy logic can be used in control systems which cannot be or accurately modeled [23] to eliminate the uncertainties of the system [24] or to encompass the nonlinearities of the system [25]. The most straightforward way to increase robustness of systems with determined systematic feedback is to use a fuzzy logic supervisor for coefficients of a simple proportional–integral–derivative (PID) controller [26]. One of the significant approaches to control the nonlinear systems in the presence of model uncertainties is combining the fuzzy logic with gain scheduling and sliding mode technique. The resistive forces against the propulsion of the microrobot in low Reynolds number were considered, feasible modeling assumptions were established, and the system’s governing equations were elicited.
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