Current teleoperated robotic systems for retinal surgery cannot effectively control subtle tool-to-tissue interaction forces. This limitation may lead to patient injury caused by the surgeon’s mistakes. To improve the safety of retinal surgery, this paper proposes a haptic shared control framework for teleoperation based on a force-constrained supervisory controller. The supervisory controller leverages Control Barrier Functions (CBFs) and the interaction model to modify teleoperated inputs when they are deemed unsafe. This method ensures that the interaction forces at the slave robot’s end-effector remain within the safe range without the robot’s dynamic model and the safety margin. Additionally, the master robot provides haptic feedback to enhance the surgeon’s situational awareness during surgery, reducing the risk of misjudgment. Finally, simulated membrane peeling experiments are conducted in a controlled intraocular surgical environment using a teleoperated robotic system controlled by a non-expert. The experimental results demonstrate that the proposed control framework significantly reduces the rate of force constraint violation.

This paper proposes an adaptive force-tracking controller for nonlinear systems with time-varying parameters, based on the practical task of peeling a film from the surface of a rigid body. The precise regulation of the peeling force by a force controller during robotic film peeling is critical for enhancing the safety and stability of the operation. Existing adaptive controllers for systems with time-varying parameters either result in large tracking errors or can only achieve closed-loop system stability, failing to track the desired force. The proposed controller employs the congelation of variables and robust adaptive control. The stability of the closed-loop system demonstrated via the corresponding Lyapunov function. Subsequent simulation results show that the tracking error of the closed-loop system with the proposed controller asymptotically converges to zero, validating the theoretical approach. Finally, experiments not only confirm the feasibility and stability of the proposed controller but also successfully demonstrate its potential for application in robotics.