Abstract | ||
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The design of controllers and observers often relies on first order models of the system in question. These models are often obtained either through step-response tests, through on-line or off-line identification, or through developing a mathematical model. When the system in question has unknown or uncertain parameters, the developed model also contains uncertainties and the controller/observer design may result in bad performance or even instability. In this paper, we present a combined design of a controller and an observer for scalar linear time-invariant systems with unknown parameters. We combine a model reference adaptive controller, which does not require a model of the system, with a Luenberger observer which uses the desired closed-loop dynamics as its model. The method is given the name MRACO. Our proposed method is similar to what is known as closed-loop reference model adaptive control, but the key difference is that our method does not use a closed-loop reference model. We show through Lyapunov theory and by application of Barbalat's lemma that all error states in the closed-loop system converge to zero and that all signals are bounded. Several simulations are performed to support the proofs. |
Year | DOI | Venue |
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2018 | 10.1145/3284516.3284517 | ICCMA 2018: PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON CONTROL, MECHATRONICS AND AUTOMATION |
Keywords | DocType | Citations |
Adaptive control,estimation,unknown systems | Conference | 1 |
PageRank | References | Authors |
0.63 | 1 | 2 |
Name | Order | Citations | PageRank |
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Sveinung Johan Ohrem | 1 | 1 | 0.63 |
Christian Holden | 2 | 4 | 3.90 |