Abstract
For a time-varying plant operating in closed-loop with a stabilising controller, rapid changes in system dynamics can be detected online using recursive subspace identification methods to estimate the open-loop system behaviour. However, these methods usually involve a speed-accuracy trade-off: accurate identification can often only be achieved by slow updates, which increases the lag in the detection of changes in system dynamics. In this paper, a closed-loop, recursive subspace identification algorithm is extended with a convex cost function based on the nuclear norm. The nuclear norm heuristic exploits structure in the algorithm by enforcing a low-rank condition on the state predictor matrix. This condition reduces the variance of the estimates at the price of introducing a bias. The new algorithm is demonstrated for a system where the damping changes from positive to negative, and it is shown to successfully and consistently estimate the onset of open-loop instability, outperforming conventional recursive identification. Further, by tuning the forgetting factor in the estimation algorithm, a favourable speed-accuracy trade-off can be achieved.
Original language | English |
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Title of host publication | Proceedings of the 2016 American Control Conference (ACC 2016) |
Editors | George Chiu, Katie Johnson, Danny Abramovitch |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Pages | 936-941 |
ISBN (Print) | 978-1-4673-8682-1 |
DOIs | |
Publication status | Published - 2016 |
Event | American Control Conference (ACC), 2016 - Boston, MA, United States Duration: 6 Jul 2016 → 8 Jul 2016 |
Conference
Conference | American Control Conference (ACC), 2016 |
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Abbreviated title | ACC 2016 |
Country/Territory | United States |
City | Boston, MA |
Period | 6/07/16 → 8/07/16 |
Keywords
- Cost function
- Markov processes
- Estimation
- System dynamics
- Heuristic algorithms
- Minimization