Adaptive Input Design for Identification of Output Error Model with Constrained Output
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Optimal input design for system identification is an area of intensive modern research. This paper considers the identification of output error (OE) model, for the case of constrained output variance. The constraint plays a very important role in the process industry, in the reduction of degradation of product quality. In this paper, it is shown, in the form of a theorem, that the optimal input signal, with constrained output, is achieved by a minimum variance controller together with a stochastic reference. The key problem is that the optimal input depends on the system parameters to be identified. In order to overcome this problem, a two-stage adaptive procedure is proposed: obtaining an initial model using PRBS as input signal; application of adaptive minimum variance controller together with the stochastic variable reference, in order to generate input signals for system identification. Theoretical results are illustrated by simulations.
KeywordsSystem identification Optimal input design Output error model Constrained output Two-stage adaptive procedure
The authors would like to express their gratitude to reviewers for their very useful comments and suggestions to improve this paper. This research has been supported by the Serbian Ministry of Education, Science and Technological Development through projects TR33026 and TR33027.
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