Dual-EKF System Identification and Model Predictive Path Integral Control of a Retinal Microsurgical Robot

This study presents a modeling and control framework for a retinal microsurgical robot, the Improved Integrated Robotic Intraocular Snake (I²RIS), which accounts for the coupling between pitch and yaw degrees of freedom. The system is modeled using a multi-input multi-output (MIMO) mass-spring-damping (MSD) formulation, with parameters and states jointly estimated through a dual-extended Kalman filter (dual-EKF) approach. Unlike analytical methods such as Cosserat-based models, the proposed approach is computationally efficient, requires minimal training data, and enables real-time control. The coupled MSD model captures the interdependence between pitch and yaw dynamics using experimentally optimized parameters. An optimal stochastic controller, the Model Predictive Path Integral (MPPI), is then designed and compared with a Linear Quadratic Regulator (LQR) in a trajectory-tracking control problem. Experimental results demonstrate that MPPI achieves superior performance and robustness in controlling the coupled dynamics of the I²RIS robot, offering a promising solution for efficient real-time control of snake-like robotic systems.