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Adaptive friction compensation of servo mechanisms
| Content Provider | Scilit |
|---|---|
| Author | Ge, S. S. Lee, T. H. Ren, S. X. |
| Copyright Year | 2001 |
| Description | In this paper, adaptive friction compensation is investigated using both model-based and neural network (non-model-based) parametrization techniques. After a comprehensive list of commonly used models for friction is presented, model-based and non-modelbased adaptive friction controllers are developed with guaranteed closed-loop stability. Intensive computer simulations are carried out to show the effectiveness of the proposed control techniques, and to illustrate the effects of certain system parameters on the performance of the closed-loop system. It is observed that as the friction models become complex and capture the dominate dynamic behaviours, higher feedback gains for model-based control can be used and the speed of adaptation can also be increased for better control performance. It is also found that neural networks are suitable candidate for friction modelling and adaptive controller design for friction compensation. |
| Related Links | http://robotics.nus.edu.sg/sge/journal/bookchapter/bookchapter-AdaptiveFrictionCompensation.pdf |
| Ending Page | 532 |
| Page Count | 10 |
| Starting Page | 523 |
| ISSN | 00207721 |
| e-ISSN | 14645319 |
| DOI | 10.1080/002077201300080974 |
| Journal | International Journal of Systems Science |
| Issue Number | 4 |
| Volume Number | 32 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2001-04-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: International Journal of Systems Science Automotive Engineering Neural Networks Motion Control Parametric Statistics Adaptive Control Computer Simulation Neural Network Friction |
| Content Type | Text |
| Resource Type | Article |
| Subject | Theoretical Computer Science Control and Systems Engineering Computer Science Applications |
