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The setpoints are generated by a position controller which is designed based on a dynamical model of the LIM.
In this paper I first shed light on the genesis of this notion, based on a dynamical systems approach and on considerations of structural stability.
The control laws are based on a dynamical extension of the kinematic model of unicycles considering the midpoint of the wheels axis of the robots as output function.
The calculations are based on a dynamical diffraction theory in which the electrons are scattered on a potential, which is periodic in the direction perpendicular to the surface.
The design of the state observer is based on a dynamical system of equations which is generated from the increasing-order differentiation of a n-th order Fourier series.
A model based on a dynamical competition between recoil mixing and thermal spike diffusion is proposed to explain these various results; it is implemented in kinetic Monte Carlo simulations.
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These are generated at a slower sampling rate by a hybrid MPC controller at the upper control layer, based on a hybrid dynamical model of the UAV and of its surrounding environment, with the overall goal of controlling the vehicle to a target set-point while avoiding obstacles.
In this work, the observability properties of a nonlinear dynamic model of these animal cell cultures is assessed using a method based on a natural dynamical interpretation of the observability/detectability concepts, leading to the description of the indistinguishable dynamics of the system.
Here we hypothesize that the robust properties of differentiation are rather the outcome of the interaction of very many genes, so our model is based on a simplified dynamical model of genetic regulatory networks, namely noisy random Boolean networks (NRBNs for short), which actually allow simulations of large networks [12].
It is based on a simple dynamical model of mRNA synthesis and degradation, where transcriptional activity can 'switch' between an arbitrary number of states.
Our inference method is based on a linear dynamical system, so it follows that it is easier for it to recover a network from data created with a linear model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com