Adaptive Learning and Sliding Mode Control for a Magnetic Microrobot Precision Tracking With Uncertainties
Yueyue Liu, Haoyu Wang, Qigao Fan
Abstract
Magnetic microrobots have potential applications in many medical diagnosis and treatment due to the unique characteristics in biological fine manipulation, such as precise drug delivery and hyperthermia. Precise and robust trajectory tracking control of magnetic microrobots under complex disturbances and uncertain dynamic model is still one of the most important and challenging steps in achieving ideal applications. This letter develops a learning based adaptive sliding mode control (LBASMC) method to address the unknown dynamic parameters and complex external disturbances. A radial basis function neural network (RBFNN) based state feedback control strategy can effectively estimate the uncertainties online from the state and desired trajectories, rather than the model-based control. Besides, to overcome the nonparametric unknown disturbances, an adaptive sliding mode control strategy is designed. The controller require as little knowledge of system parameters as possible. Subsequently, according to the Lyapunov theory, the closed-loop finite time convergence stable system is guaranteed. Finally, the two experimental researches are conducted on our developed electromagnetic manipulation system to validate the effectiveness of the designed control approach.