Decoupling Research of a Three-dimensional Force Tactile Sensor Based on Radical Basis Function Neural Network

Abstract

A decoupling method based on radical basis function neural network (RBFNN) for a novel three-dimensional force flexible tactile sensor is presented in this paper. A numerical model of the tactile sensor is built through finite element analysis, which simulates the mapping between three-dimensional force applied on top surface of the sensor and deformation of the sensor. Furthermore, the RBFNN is applied to approach the nonlinear relationship between the deformation and the three-dimensional force. The row-column resistance values corresponding to the deformation are computed by a mathematical model. At last, the high dimensional nonlinear mapping relationship between resistance and three-dimensional force is also decoupled by RBFNN algorithm. Hence the decoupling system for the tactile sensor is implemented by using RBFNN twice. The decoupling results show that the RBFNN with high nonlinear approximation ability has good performance in decoupling three-dimensional force and satisfies both the decoupling accuracy and real-time requirements of the tactile sensor.

Keywords

• Tactile sensor
• Decoupling method
• Radical basis function
• Neural network
• Three-dimensional force.