Neural network based adaptive control of a flexible link manipulator

Abstract

This paper presents a design methodology for an on-line self-tuning adaptive control (OLSTAC) of a single flexible link manipulator (FLM) using backpropagation neural networks (BPNN). The particular problem discussed is the on-line system identification of a FLM using BPNN and the OLSTAC of a FLM using a separate neural network as a controller. A finite-element model of a FLM is obtained using ANSYS. The pseudo-link concepts developed in (2) are used to determine on-line angular displacement of the end effector of the FLM. The illustrative simulation results are promising and show that the OLSTAC technique can be applied to flexible structures such as a FEM resulting reduced error and increased robustness. >