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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wagie, D.A. Skelton, R.E. |
| Copyright Year | 1984 |
| Description | Author affiliation: Purdue University, School of Aeronautics and Astronautics, West Lafayette, Indiana (Wagie, D.A.; Skelton, R.E.) |
| Abstract | Covariance equivalent realizations have been used recently to produce reduced-order models that match a specified number of output covariances and Markov parameters of the original model. This paper extends this theory to models with uncertain parameters. The approach is to take an nth order nominal system with h uncertain parameters, form its (n+nh) sensitivity model, then reduce the sensitivity model to size (n+¿lh), where l is the number of outputs and ¿ is an integer chosen by the designer. The reduced-order model then matches (¿+l) output covariances and ¿ Markov parameters of the original sensitivity system. This method leaves the nominal system unchanged, and hence 1) retains all dynamical information of the nominal system, 2) maintains the correct cross-correlation between nominal outputs and sensitivity outputs, and 3) preserves the distinction between plant and sensitivity states in the reduced model. This last property enables one to use the reduced model to generate a controller which minimizes a cost function that includes output (trajectory) sensitivity and input (control) sensitivity terms. |
| Starting Page | 135 |
| Ending Page | 140 |
| File Size | 513440 |
| Page Count | 6 |
| File Format | |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1984-06-06 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | American Automatic Control Council(AACC) |
| Subject Keyword | Reduced order systems Uncertain systems Control design Impedance matching Cost function Linear systems State-space methods Uncertainty Covariance matrix White noise |
| Content Type | Text |
| Resource Type | Article |
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