Robust Navigation Control of a Microrobot With Hysteresis Compensation

• Published in: IEEE transactions on automation science and engineering 2022-10, Vol.19 (4), p.3083-3092
• Main Authors: Jia, Yuanjun, Zheng, Liushuai, Dong, Dingran, Wang, Yong, Sun, Dong
• Format: Article
• Language: English
• Subjects: Adaptive control; Coils; Compensation; Control methods; Disturbance observers; Flow velocity; Genetic algorithms; Hysteresis; Hysteresis compensation; In vivo methods and tests; Microfluidics; microrobot; Microrobots; Motion planning; Motion stability; Navigation; Nonlinear control; Nonlinearity; Precision medicine; Robust control; Sliding mode control
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2021.3106022

Navigation control of microrobots in vivo has great potential in precision medicine and has attracted considerable attention in recent years. The control performance of the existing methods is considerably affected by hysteresis nonlinearity. This article presents a robust control method that can overcome hysteresis influence in navigating a microrobot actuated by an electromagnetic coil system. A motion planner that combines the breadth-first search (BFS) method and genetic algorithm (GA) is used to plan a reliable and flexible trajectory for the microrobot navigation. To compensate for hysteresis nonlinearity existing in the system, the Prandtl-Ishlinskii (PI) model is introduced. A robust controller that integrates adaptive sliding mode control (ASMC) and nonlinear disturbance observer is designed to guarantee the stability and accuracy of the microrobot in motion. Experiments have been performed to demonstrate the effectiveness of the proposed approach. The success of this research will advance the microrobot navigation for in vivo applications. Note to Practitioners-The motivation of this article is to eliminate the effect of hysteresis caused by the electromagnetic system on microrobot motion. Existing research on hysteresis compensation mostly uses inverse model or online model identification, which is not suitable for real-time control of the microrobot. In addition, the disturbance and uncertainty caused by the microfluidic environment at different flow rates will also affect the control performance of the microrobot. To solve the above problems, an adaptive robust control method is developed in this article. Experimental results have shown that the proposed method can successfully control the microrobot in the in vitro and in vivo environment.