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This paper introduces a practical implementation of a nonlinear controller obtained from the new design technique called hamiltonian-based control.
Therefore, a controller obtained from the standard models may not achieve the performance it is designed for under operational conditions where the nonlinearity of the components is not negligible.
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In this paper, it is shown how to realize the structured controllers obtained from the linear matrix inequalities in order to ensure this continuity property for the closed-loop system.
G C = N C,L C )is a simple graph (Figure 1d) referring to the controller network [10], where N C (=N W ) represents the set of Controllers in InPs and L C is the set of links between Controllers obtained from the multiset L W. Similar to substrate networks, we model VN requests as weighted undirected graphs and denote a VN request by G V =(N V,E V ).
This controller is obtained from the PI controller design in direct current.
In order to quickly recover the nominal behavior of the Linear Quadratic Gaussian (LQG) controller, a resilient LQG controller is obtained from an active version of the Generalized Likelihood Ratio (GLR) test designed to detect the disappearance of the malicious act and to increase the tracking ability of the Kalman filter at detection time.
The training patterns for the neuron controller are obtained from the conventional PI controller and the effectiveness of the proposed neuron controller is studied using simulation studies.
The states of the feedback stabilizing controller are obtained from the nonlinear observer and the performance of this observer-based controller is independent of the operating points of power systems.
Table 1 compares the feedback controller gains obtained from those two methods.
The parameters of this controller are obtained from a LMI (Linear Matrix Inequalities) minimization problem.
The controllers are obtained from general solutions of Diophantine equations in the ring of proper and stable rational functions and can be subsequently tuned via the only parameter.
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