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The switching controller is designed to eliminate the effect of the approximation error introduced by the ESRNN upon system stability.
The robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense.
A modified algebraic Riccati-like equation must be solved to compensate the effect of the approximation error via adaptive fuzzy neural system on the H∞ control.
Finally, simulation results for an inverted pendulum system show that the effect of the approximation error on the tracking error can be attenuated efficiently.
The terminal neural controller including a perturbed fuzzy neural network (PFNN) is the main controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the PFNN upon the system stability.
The neural controller utilizes a dynamic radial basis function (DRBF) network to online mimic an ideal controller and the smooth compensator is designed to eliminate the effect of the approximation error between the ideal controller and neural controller.
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The effect on the approximation of introducing subdomains is also studied.
To understand the dependence of L eq ∗ on ionic strength and to evaluate the effect of the approximations made in the Debye–Huckel treatment of electrostatics, we performed a number of simulations using the primitive model representation of electrostatics and explicit ions to represent neutralizing counterions and added salt (the 'Model potentials and parameters' section).
Data pertaining to an existing installation are used to verify quantitatively the effect of this approximation on the accuracy of flow calculations.
Furthermore, the robust control technique can easily reject the effects of the approximation errors of the FBFN and external disturbances.
Effects of the approximation error on system performance are also investigated in this paper.
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