Abstract
This paper presents recent research results for feedback control design of motion systems. Two model-free approaches are investigated, that exploit the ease of experimentation which is typical for motion systems. One approach is data-based design of a linear feedback controller which realizes desired closed-loop sensitivity and complementary sensitivity transfer functions. These transfer functions are specified via a data-based performance cost. The designer can prescribe both the controller structure and the complexity. Experimental results obtained in a direct-drive robot motion control problem confirm the effectiveness of the design. A second line of research is unfalsified control where a set of controllers is iteratively tested against measured data. Experimental results for the well-known fourth order benchmark motion system show feasibility of the approach. Finally, we implemented a nonlinear SPAN filter on the same system, which outperforms a linear feedback design
| Original language | English |
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| Title of host publication | Proceedings of the 2005 IEEE Conference on Control Applications (CCA 2005), 28-31 August 2005, Toronto, Canada |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 529-534 |
| ISBN (Print) | 0-7803-9354-6 |
| DOIs | |
| Publication status | Published - 2005 |