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<p>This thesis proposes an integrated procedure for identifying the nominal models
of the deterministic part and the stochastic part of a system, as well as their model
error bounds in different uncertainty structures (e.g. $\mathcal{H}_2$-norm and $\mathcal{H}_{\infty}$-norm)
based on the measurement data. In particular, the deterministic part of a system is
firstly identified by closed-loop instrumental variable method in which a known external
signal sequence uncorrelated with the system noises is injected in the control input
for the identifiability of the system in closed loop. By exploiting the second-order
statistics of the noise-driven output components, the stochastic part of a system
is identified by the improved subspace approach in which a new and straightforward
linear-matrix-inequality-based optimization is proposed to obtain a valid model even
under insufficient measurement data.</p><p>To derive an explicit model error bound
on the identification model, we investigate a complete asymptotic analysis for identification
of the stochastic part of the system. We first derive the asymptotically normal distributions
of the empirical sample covariance and block-Hankel matrix of the outputs. Thanks
to these asymptotic distributions and the perturbation analysis of singular value
decomposition and discrete algebraic Riccati equation, several central limit theorems
for the identified controllability matrix, observability matrix, and the state-space
matrices in the associated covariance model are derived, as well as the norm bounds
of Kalman gain and the innovations covariance matrix in the innovations model. By
combining these asymptotic results, the explicit $\mathcal{H}_2$-norm and $\mathcal{H}_{\infty}$-norm
bounds of the model error are identified with a given confidence level. </p><p>Practical
applicability of the proposed combined deterministic-stochastic identification procedure
is illustrated by the application to indirect adaptive control of a multi-generator
wave energy harvesting system.</p>
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