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These imply that LPV system discretization is an important issue for both system identification and controller implementation.
In this paper, two standard adaptive feedback controllers, featuring both system identification and control in real time, are compared for active sound and vibration attenuation.
Multivariate high purity distillation columns present a number of challenging problems for both system identification and control due to their nonlinear and ill-conditioned nature.
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Emphasis will be placed on both recent theoretical developments eg, system identification and controllability in neuronal networks—and real-world constraints in applications such as clinical deep brain stimulation, or optical stimulation in experimental neuroscience.
Fig. 1 Signal model for system identification and AEC.
Analytic modeling, system identification, and robust controller design are presented.
A procedure is tested on linear and nonlinear systems for system identification and multivariable autotuning.
Examples include material sciences, mechanics, wave propagation, signal processing, system identification, and so on.
It is widely applied in target tracking, system identification, and automatic control, etc. [28 31].
In addition, guidelines on system identification and the controller and actuator setups are suggested.
The substructuring methods have been popularly used in model updating, system identification and damage assessment.
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