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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hac, Aleksander |
| Copyright Year | 1990 |
| Description | Author affiliation: Department of Mechanical Engineering, State University of New York at Stony Brook, Stony Brook, N.Y. 11794 (Hac, Aleksander) |
| Abstract | This paper examines the properties of learning control algorithms for linear MIMO systems in the presence of measurement noise. The only information used about the measurement errors is the magnitude upper bounds on the errors. Several control algorithms that are convergent (that is result in asymptotically perfect tracking) under perfect measurements are considered. It is shown that despite the imperfect measurements the learning controller can improve the tracking performance by driving the tracking error asymptotically within certain bounds which depend on the upper bound on the disturbance and the system parameters. The results help the designer to assess the extend of performance deterioration of learning control systems under influence of measurement errors. |
| Starting Page | 2846 |
| Ending Page | 2851 |
| File Size | 484849 |
| Page Count | 6 |
| File Format | |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1990-05-23 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | American Automatic Control Council(AACC) |
| Subject Keyword | Noise measurement Control systems Error correction Measurement errors Upper bound System performance Pollution measurement Mechanical variables measurement MIMO Mechanical engineering |
| Content Type | Text |
| Resource Type | Article |
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