3-D Path Following of Helical Microswimmers With an Adaptive Orientation Compensation Model

Controlling magnetic microswimmers toward 3-D manipulation tasks has received considerable attention. Although related studies on manipulating helical microswimmers have been developed, stable closed-loop controls and accuracy swimming models should be still investigated. This article addresses the problem of 3-D path following for magnetically driven helical microswimmers with an adaptive-compensation scheme. The orientation-compensation model in the global coordinate frame is learned by radial basis function (RBF) networks trained with backpropagation algorithms, which is used to express the motion of the helical microswimmer in the presence of the weight of the swimmer and lateral disturbances from the boundary effects. A proxy-based sliding-mode control (PSMC) approach is developed to design stable controllers based on the kinematic error model. The effects of variable parameters and boundary effects are also considered. Experimental results including different paths in 3-D space validated the path following with submillimeter accuracy using the helical microswimmer. Note to Practitioners —This article is motivated by the issue of the following predefined paths for magnetically driven helical microswimmers in 3-D space. The proposed closed-loop controller employs the error model in 3-D space to formulate the control law according to an orientation-compensation model learned by neural networks. It is demonstrated that the helical microswimmer is able to follow different paths in 3-D space with submillimeter accuracy using the proposed control scheme.