![]() The insights of the proposed design lie in: i) the operation data can reflect the internal parameters of the model and provide a source for preliminary estimation ii) the error bounds of estimated parameters and states can be determined based on mathematical analysis of the regression procedure. In this paper, we aim to design a stable parameter and state estimator over the unknown linear system (i.e., without prior knowledge), via a suitable feedback gain and iterative adjustment procedure and determine the error bounds. Besides, the estimation error bounds are needed for most of the low-cost model-based controller design. However, in practical applications, the accuracy of prior knowledge degenerates over time, which causes identification failure. Most of the existing methods have promising results under the condition that the prior knowledge of the system model is known (system structure is given). Abstract: System model parameter and state estimations are classical problems for control theory. ![]()
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