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First, the robust controller of the original uncertain system is derived by adding a feedback gain to the optimal controller of the nominal system.
We suggest two ways in which this tradeoff can be alleviated: (1) by introducing a nonlinearity in the production of a specific node in the network, and (2) by adding a feedback loop to the input.
To overcome this drawback, various active damping strategies are proposed to substitute the physical resistor by adding a feedback in the controller, which can be proportional to the capacitor current [4, 5, 6], or to the first-order differential of capacitor voltage [7], or to the second-order differential of the grid-side current [8, 9].
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The dynamical system is defined by adding a control feedback to the solution of the Hill Clohessy Wiltshire equations in order to track the desired velocities (as returned by the kinematic swarm model for equilibrium points).
Firstly, we formulate the optimal regulation control problem for the nominal system, and then, the robust controller for the original uncertain system is designed by adding a constant feedback gain to the optimal controller of the nominal system.
Such results confirm that the robustness and the tunability of the network can increase by adding a positive feedback loop.
Here, however, we slightly modified this PKN by adding an autocatalytic feedback loop on the phosphatase (ph) regulating the activation of SOS-1.
This choice yields a bistable system similar to that of model A. Model D is derived from model C by adding a NANOG autorepression feedback.
By adding a DCCII as a feedback path from output of the second and the third stage to the output of the first stage, feed-forward path and the right-half plane zero will be removed subsequently, which significantly improves phase margin and gain-bandwidth product.
For the fractional-order error system in the form (41), by adding a linear controller and letting the feedback gain matrix satisfy the conditions of Corollary 2, the error system can be stabilized to the equilibrium point, i.e. the drive system and response system are asymptotically synchronized.
The CNF control law serves to improve the transient performance of the closed-loop system by adding an additional nonlinear feedback.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com