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They argued that the change is adaptive based on a space-for-time comparison of trends in migration timing across a latitudinal gradient associated with temperature in many populations.
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Therefore, the adaptive control based on a linear parameterization model cannot be used in this dynamics.
An optimal adaptive controller based on a reset mechanism and a prescribed performance bound is devised.
A corresponding surface adaptive algorithm based on a progressive approximation method was designed to obtain surface topography.
RCMAC is utilized to approximate a perfect controller, and the parameters of RCMAC are on-line tuned by the derived adaptive laws based on a Lyapunov function.
To overcome this problem, a new adaptive controller based on a generalized predictive control (GPC) principle was designed to make the controller more robust.
In this paper, a feedforward adaptive controller based on a neural network (NN) is proposed to improve the DSS testing performance.
A new optimal adaptive approach based on a passive control scheme was designed bilaterally for both the master and slave sites.
A particular non-minimal state representation of the machine electrical model is derived and exploited to build the adaptive observer, based on a series parallel architecture.
The resulting controller, tuned with the proposed methodology, provides better performance than an adaptive controller based on a Recursive Least Squares Estimator (RLS) during sudden changes in model parameters.
An adaptive estimator based on a min max algorithm is then designed to estimate the unknown 3D Euclidean coordinates of an object feature relative to a fixed reference frame which facilitates the identification of range.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com