Left-Inverse System Dynamic Decoupling and Compensating Method Using Neural Networks
Abstract
A novel and practical left-inverse system based neural networks dynamic decoupling and compensating (LISNNDDC) method is proposed for improving dynamic performance of generic nonlinear muti-dimension sensors (e.g. muti-axis force sensors) instead of well-used ones. Consequently, the proposed method is not only of prime theoretical interest but also, in practical implementation, can obtain better dynamic performance. A six-axis wrist force sensor is illustrated as an example to validate that the proposed method can markedly improve dynamic performance of the muti-dimension sensors and is superior to previous methods.
