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Genetic algorithms are indicated when the search space is extended, unknown or with an intricate geometry.
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Instead of designing a traditional unknown input decoupled filter, a Double-Model Adaptive Estimation approach is extended to solve the unknown input filtering problem.
In this paper, the continuous time self-tuning control algorithm is extended to systems with unknown or varying time delay.
Further, the method in [13] is extended to a class of unknown nonlinear systems with a new robust adaptive multiple model-based fuzzy control scheme.
In the present study, the use of transcriptomics is extended for the first time to unknown skeletal muscle samples, collected directly from beef cattle immediately after slaughtering.
When the parameters are unknown, the active control is extended and introduced to realize the synchronization.
Finally, output nonlinearities are studied and the design is extended to be adaptive in the presence of unknown parameters.
Finally, the approach is extended to the synchronization problem for the system with three unknown parameters.
The models were extended to unknown rat strains, as well.
Furthermore, the design results are extended to the case with unknown disturbance, and H∞ controller is designed to achieve the disturbance attenuation performance.
(1) The results in [11, 15, 16, 27, 31] are extended to multiple chaotic system with unknown parameters.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com