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01-01-2015 | Original Research Paper | Issue 1/2015

Intelligent Service Robotics 1/2015

Intelligent nonlinear observer design for a class of nonlinear discrete-time flexible joint robot

Journal:
Intelligent Service Robotics > Issue 1/2015
Authors:
Mohammad Reza Rahimi Khoygani, Reza Ghasemi, Ahmad Reza Vali

Abstract

In this paper, a nonlinear intelligent observer design is applied for a class of nonlinear discrete-time flexible joint robot (DFJR) dynamic system based on artificial neural network (ANN). The DFJR system has a relatively complex nonlinear dynamic and internal states’ estimation of it poses a challenging robotic problem. Multilayer perceptron (MLP) is an important class of feed-forward ANNs that maps set of inputs onto a set of suitable outputs. The ANN under online learning is one of the artificial intelligence methods. Therefore, the MLP neural nonlinear observer is trained online and it is robust in the presence of external and internal uncertainties. The learning method of the intelligent observer is a simple back propagation (BP) algorithm and, furthermore, the learning method of estimation of the link positions and the velocities is BP-developed algorithm. Simulation results show promising performance of the proposed observer in the presence of measurement noise and parameters uncertainties.

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