Adaptive Tracking Controller for an Alginate Artificial Cell

Abstract

This paper presents an adaptive backstepping controller for the reference tracking of an alginate artificial cell. An adaptive controller was implemented to precisely manipulate a magnetic artificial cell actuated by rotating magnetic fields. The rolling motion of a small-scale robot in a fluidic environment is challenging, especially when the fluid imparts an unknown response at low Reynolds number. In order to compensate for this uncertainty, an unknown tuning parameter encapsulating these effects was added to the governing equations of motion. A controller with an update law was then designed to estimate the unknown parameter and force the artificial cell to produce the desired response. The stability of the proposed controller was established by a candidate Lyapunov function. Real-time experiments were conducted to demonstrate the effectiveness of the designed controller at guiding an artificial cell to an arbitrary target position. Alginate cells were guided through a maze using the controller and was later combined with wall constraints to allow multiple alginate cells to reach the same target location. This controller can be applied to both surface motion and swimming-based small-scale robots in future applications for micro-assembly and targeted drug delivery.