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The tuning procedure is illustrated on mathematical models identified from patient data and the corresponding PID controllers.
In this contribution we present a joint robust state feedback control/input design procedure which guarantees stability and prescribed closed-loop performance using models identified from experimental data.
An MPC strategy is developed for this type of system based on linear models identified from data obtained from a first-principles model of the VCC.
We propose an improved fault detection (FD) scheme based on residual signals extracted on-line from system models identified from high-dimensional measurement data recorded in multi-sensor networks.
Furthermore, we compare the performance of Model Predictive Controllers, based on models identified from closed-loop data using the different subspace algorithms, in the control of the Wood Berry distillation column.
Experimental validation of the proposed procedure shows that models identified from the data measured using the optimized excitations are considerably more accurate than those identified from data obtained using conventional random phase multisine excitations.
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Perhaps as important as the simplified modeling framework is the fact that a model identified from data obtained from experiments on dogs and rabbits, is used to draw insight into human physiology and pathophysiology.
The state space model identified from experimental data provides the basis for a linear quadratic regulator (LQR) design.
Simulations results show the effectiveness of the estimation technique applied to the presented GLMAV model, identified from experimental results.
An emulator is a low-order, computationally efficient model identified from the original large model and then used to replace it for computationally intensive applications.
This paper presents a method for estimating the uncertainty in a state-space helicopter dynamical model identified from the subspace-based method using bootstrap techniques.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com