Neural-Learning-Based Adaptive Sliding Mode Impedance Force Control of Robotic Microinjection Systems Interacting With Viscoelastic Cells

Robotic microinjection has been widely applied in biomedical engineering, but faces a great challenge on the precise force interaction with cells due to their inherently deformable, fragile, and nonlinear viscoelastic properties. This brief proposes a new neural-learning based adaptive sliding mode impedance force control scheme for the robotic microinjection system to improve the interaction performance. The key features of the developed method are as follows: i) Target impedance is derived by utilizing the nonlinear Hunt-Crossley model to match the microscale interaction with environmental cells; ii) An integral terminal sliding mode manifold based on the impedance error is designed to achieve finite-time convergence and accurate force tracking; iii) The proposed scheme relieves the burden of environmental model dependence by estimating the uncertain bound of external disturbances through an adaptive neural network compensator. The control system stability is analyzed by the Lyapunov theory, and the force tracking performance is also verified via a series of experiments.