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This means that the conventional approach to system order reduction, based on the theory of integral manifolds, cannot be applied.
We demonstrate the robustness of the derived controllers against system order reduction arising from the use of fractional integrators in the system models.
Calibration of nonlinear devices by this means still requires numerical analysis while the efficiency is largely improved owing to the system order reduction.
The modal decoupling of the equations of motion allows a system order reduction resulting in a simpler and more implementable controller addressing only specific vibration modes, which can cause problems.
In most papers focused on the system order reduction models, describing processes of heating, evaporation and ignition in fuel sprays, it is assumed that all functions in corresponding differential equations are sufficiently smooth and consequently Lipschitzian.
Contrary to the traditional system order reduction methods which, because of reducing the system order, also reduce the number of its parameters, the use of fractional models leads to models using only very few parameters, but of infinite dimension, because of their "long memory" characteristics.
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Finally, a model-free approach is given to directly identify parity space based or observer based residual generators from input and output data of descriptor systems and the order reduction of the identified residual generators is discussed.
More specifically, the analyses of vibrating complex water-pipe systems require efficient model order reduction strategies.
This work exploits the dissipative nature that many engineering systems exhibit, for model order reduction (MOR) purposes.
To this purpose, we will extend some classical results on the finite-dimensional robust control theory to RD systems by means of order reduction techniques, in particular the Proper Orthogonal Decomposition method.
Model order reduction and system identification techniques have been employed to transform complex models into equivalent reduced order models.
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