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Figure 10 Convergence of position RMSE vs. iterations of the array orientation procedure.
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The convergence of the position of the first manipulation point to its desired position on the first path is guaranteed by linear control theory, and the convergence of the position of the second manipulation point to its desired position on the second path is guaranteed by Lyapunov's second method.
Lyapunov stability analysis is provided to show asymptotical convergence of image position and velocity tracking errors.
The convergence of atomic position is judged by the force working on each atom is less than 1.0 mRy/a.u.u
The proposed control law guarantees the exponential stability of the closed-loop system and ensures the convergence of the position and the orientation of the robot to their desired fixed values.
The convergence of atomic position is judged by the force working on each atom that is less than 1.0 mRy/a.u. Figure 2 shows the energy band dispersion of Tl2Mo2O7, Sn2Nb2O7 and Bi2Ti2O7 from first principles.
The plots show the convergence of the position RMSE as a function of the number of iterations, averaged over 104 Monte Carlo simulations for a given sensor configuration, with ρ = 0 dB, N = 100 samples, m=6 antennas per sensor, and M = 6 sensors.
Yet for great thinkers like Galileo and Lincoln, accelerating the convergence of opposing positions would exact a heavy toll.
Then, the considered model being non linear, we used Takagi-Sugeno fuzzy modeling to synthesis the non linear observer and to prove the convergence of the state (position, velocities and torques) estimation errors.
The model stopped after convergence of the QTL positions.
The convergence of the force and position tracking errors is proved based on Lyapunov stability analysis.
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