Enhanced Zeroing Neural Network for Kinematic Control of Surgical Manipulator Under RCM Constraints

In minimally invasive surgery, surgical instruments are typically inserted through small incisions in the patient’s body, which serve as the remote center of motion (RCM). In robot-assisted minimally invasive surgery, developing control algorithms that comply with RCM constraints is highly challenging due to the nonlinear nature of robot motion models and the stringent precision requirements necessary for patient safety. This article introduces an enhanced zeroing neural network (EZNN) model for controlling redundant manipulators while maintaining RCM constraints. The proposed model eliminates the need for pseudoinverse matrix computations and features an explicit dynamic form. It guarantees finite-time convergence and demonstrates robustness through the application of nonlinear activation functions (AFs). These properties are rigorously validated using Lyapunov theory. Simulation and experimental results indicate that the EZNN model surpasses Jacobian-based algorithms and recurrent neural networks (RNNs) in terms of efficiency and stability, all while ensuring adherence to RCM constraints.