Decoupling algorithms for piezoelectric six-dimensional force sensor based on RBF neural network

For problems of poor linearity and too many inter-dimensional coupling errors of a four-point supporting piezoelectric six-dimensional force sensor, the decoupling algorithms based on Redial Basis Function (RBF) neural network were proposed. Main factors to produce coupling errors were analyzed and the RBF neural network was established. The six-dimensional force sensor was calibrated experimentally to obtain experimental data for decoupling, and the data were processed by the nonlinear decoupling algorithm based on RBF neural network. Then the mapping relation between input and output was acquired by decoupling and the decoupled data from the sensor was obtained. These data were analyzed, and the result shows that the biggest classIerror and classIIerror by the proposed nonlinear decoupling algorithm based on RBF neural network are 1.29% and 1.56% respectively. The experimental analysis shows that it will effectively reduce the classIerrors and the classIIerrors through nonlinear decoupling algorithm based on RBF neural network, and meets the requirements that the two kinds of error indicators of the sensor should be less than 2%.The proposed algorithm improves the measuring accuracy of sensors and overcomes the difficulty on decoupling. © 2017, Science Press. All right reserved.