Parallel force control for a robot gripper based on grey prediction models

Abstract

Parallel PI force controllers based on grey prediction models are proposed to predict contact force for robotic manipulation of deformable objects. The prediction models are built by the signal of force acquired from sensors, and the gripper will use these models to predict behaviors of contact force. The force controller enforces a relationship between the position of each finger and the force on the manipulated objects, and the weights of the current and the future contract force errors are automatic adjusted in integrated error according to the model precision. The force controllers can employ the information of past, present and future force to calculate an appropriate control correction to pre-compensate the force errors, thus it can obtain lower overshoot and faster response. Simulation results are presented to demonstrate the efficacy of the proposed parallel force controllers.