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| Content Provider | Springer Nature Link |
|---|---|
| Author | Khoygani, Mohammad Reza Rahimi Ghasemi, Reza Vali, Ahmad Reza |
| Copyright Year | 2014 |
| 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. |
| Starting Page | 45 |
| Ending Page | 56 |
| Page Count | 12 |
| File Format | |
| ISSN | 18612776 |
| Journal | Intelligent Service Robotics |
| Volume Number | 8 |
| Issue Number | 1 |
| e-ISSN | 18612784 |
| Language | English |
| Publisher | Springer Berlin Heidelberg |
| Publisher Date | 2014-12-17 |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Intelligent observer Discrete-time flexible joint robot Neural network Multilayer perceptron BP algorithm Control, Robotics, Mechatronics Artificial Intelligence (incl. Robotics) User Interfaces and Human Computer Interaction Vibration, Dynamical Systems, Control Robotics and Automation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Mechanical Engineering Computational Mechanics |
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