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Using SCALE-LES, developed as described above, we exclude the effects of approximation in the governing equation system and implicit diffusion as far as possible, enabling a consideration of the effects of the target component (i.e., cloud microphysics in this study).
Based on sliding control theory, the proposed autopilot utilizes a fuzzy basis function network to approximate the unknown nonlinear functions of bank-to-.turn missile dynamics, enabling a mixed H2/H∞ controller with weight updating laws to be applied to attenuate the effects of approximation errors and external disturbances.
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The performance and robustness of controllers designed using the two methods and the effects of approximations in the uncertainty region definition are then described and compared.
The two figures also show that the effect of approximation of the numerical solution is good.
However, an average measure for the effect of approximation errors cannot reflect their effect on the correctness of the simulation.
The difference between the results is caused by the effect of approximation for the distribution of the covered members.
On the other hand, the B-spline-based compensator is introduced to dispel the effect of approximation error.
The results in Fig. 3 show that the effect of approximation was fairly independent of the noise type and target direction.
Using the differential training operation (widetilde {Q}_{m}(a -widetilde {Q}_{m}(a -widetildeH}})), the effect of approximation error can be eliminated.
The optimal H∞ controller ensures the robust global asymptotic stability of the closed-loop system and eliminates the effect of approximation errors, parametric uncertainties, and external disturbances.
Wavelet backstepping controller is the principal controller while robust controller is designed to achieve the desired performance by attenuating the effect of approximation error caused by wavelet identifier.
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