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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ying-Chung Wang Chiang-Ju Chien |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Electronic Engineering, Huafan University, Taipei County, Taiwan (Ying-Chung Wang; Chiang-Ju Chien) |
| Abstract | In this paper, we propose a position based direct adaptive iterative learning control for robotic systems with repetitive tasks. As we assume that the joint velocities are not measurable, we introduce a sliding window of measurements to design the iterative learning controller without state observer. Based on a derived error model, a fuzzy neural network is applied to design a fuzzy neural learning component for compensation of the unknown certainty equivalent controller. Then, an averaging filter approach is applied to overcome the relative degree problem. Finally, the uncertainties due to approximation error and state estimation errors can be solved by introducing a robust learning component. Based on a Lyapunov like analysis, we show that the finiteness of control parameters and control input can be guaranteed for all the time interval during each iteration without using parameter projection. The norm of output tracking error will asymptotically converge to a tunable residual set as iteration goes to infinity. |
| Starting Page | 1270 |
| Ending Page | 1275 |
| File Size | 213677 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781612844879 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-05-15 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Asian Control Association |
| Subject Keyword | Transfer functions Robustness Mathematical model Velocity measurement Joints Robots Equations |
| Content Type | Text |
| Resource Type | Article |
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