Linear parameter variant modeling and parameter identification of a cable-driven micromanipulator for surgical robot

Abstract

This paper proposes a novel cable-driven micromanipulator for surgical robots. A single-joint principle prototype for surgical robot micromanipulator was manufactured to test the proposed design. Elasticity and friction were assessed to establish a joint angle estimator; estimator parameters were obtained by a combination of least square method and genetic algorithm. Angle closed-loop control was performed by considering the joint angle estimator output as the feedback signal. A nonlinear dynamic model was established in the state-space and described as a linear parameter variant model. The dynamic model parameters were determined via nonlinear modeling method, linear time invariant interpolator, and genetic algorithm. The angle estimator performs well and the linear parameter variant model efficiently estimates the micromanipulator’s behavior. The results presented here provide a workable foundation for surgical robot micromanipulator force estimation and control.