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However, because suitable initial guesses can be found for both the state variable and parameter variables (see Figure 1), relatively efficient convergence can be achieved.
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For SSR analysis, dynamic representation of the network is required, whereas both the state variables selection and the state equations formation for the network are difficult to process especially when the system configuration is subjected to variations.
This allows us to compute a numerical solution to the whole set of equations, thus simultaneously obtaining both the state variables and the associated sensitivity coefficients.
This allows computing a numerical solution to the whole set of equations, thus simultaneously obtaining both the state variables and the associated sensitivity coefficients.
Both the noise variables and the state variable are i.i.d.i.d
The state variable of the master system synchronizes with an alternate state variable of the slave system.
The disturbance observer estimated the disturbance and the state variable.
The current is dependent on the state variable itself.
We then index the state variable γ with time.
In this setting, the controlled variable –the state variable– is x t) and Eq. (1) is the state equation linking both variables.
A direct method is applied where both the design functions and the state variables are discretized.
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