Neural Adaptive Trajectory Tracking Control for a Magnetic Microrobot

In response to challenges posed by complex environments and uncertain dynamic models, an adaptive neural network control algorithm is proposed, with stability of the closed-loop system ensured based on the Lyapunov theory.Compared to model-based control strategies that necessitate precise knowledge of the microrobot dynamics and surrounding environment, a state feedback control strategy based on a radial basis function neural network(RBFNN) is introduced, which can effectively estimate system model uncertainties online from both states and desired trajectories. Ultimately, 2 experiments are carried out and compared on the developed magnetic-field-driven microrobot system to validate the effectiveness of the proposed controller. The results demonstrate that the root mean square errors(RMSEs) for trajectory tracking in curved and straight paths achieve 6.2204 pixels and 6.4279 pixels, respectively, significantly surpassing the performance of the traditional PID(proportional-integral-derivative) algorithm.