Model-Free Control of Magnetic Microrobotic Swarm for On-Demand Pattern Spreading

Introducing collective control strategies to microrobots provides a promising tool for enhancing the capability of individual microrobots and promoting the manipulation performance. However, controlling numerous micro-agents brings challenges to the precise control and deployment. Herein, an on-demand collective spreading strategy for magnetic particle microrobots is proposed. Under the actuation of the modulated magnetic field, the particle swarm can spread outward from the gathered state and reach a stable state. During the process, an iterative learning control (ILC)-based method is implemented to realize spreading area control in a biological fluid environment (e.g., chicken blood). The ILC-based control reduces the control uncertainty and non-linearity by real-time connecting the input parameters to the spreading area. The proposed method is validated through experiments and analysis, demonstrating the effectiveness of the collective control method in adjusting the swarm state of the particle swarm. Results show that the specific spreading area of the microrobotic swarm can be controlled with the experimental error less than 1%, demonstrating an effective strategy for collectively controlling the microrobotic swarm in targeted delivery and manipulation tasks.