Sentence examples for modeling errors including from inspiring English sources

In this approach, nonlinearities are canceled by output feedback signal; and modeling errors, including parametric uncertainties and uncertain nonlinearities, are dealt with adaptive control and robust control respectively.

We allow the term "misspecification" to cover a broad range of modeling errors including measurement errors, discretizing continuous explanatory variables, and completely excluding covariates from the model.

Here, the term "disturbance" lumps all the internal and external errors, including the modeling errors and coupling effects.

In this way, no explicit modeling errors are included in the computations (however, two types of noise are included), and also we know a priori approximately where the optimal position x QTL is located.

Common errors include: Illegible handwriting.

We include the modeling errors in covariance matrix C D following approach by Hallo and Gallovič (2016).

Within this scheme, dynamic surface control (DSC) technique, robust adaptation laws and adaptive nonlinear filters are combined for designing an adaptive dynamic surface control (ADSC -based FTC system in order to compensADSC -based eFTCctsystemensor faults and system uncertainties (including external disturbances, morderng errors and unexpectod nonlinear funcompensatesed by sensor forlthe.

Equipment may have modeling errors or disturbances that are not included in the kinematic model given by Eq. (1), for example, the friction and the delay of motors.

No a priori knowledge of an upper limits on the uncertainties including optimal parameters and modeling errors is required.

Then considering that disturbances including modeling error, sensor noise and thrust uncertainty may induce undesired constraint violations, a robust technique is developed and it is attached to the above M-MPC framework to form a robust M-MPC approach.

The serial parallel estimation model is given and a filter error included tracking error and modeling error is constructed to design the weights updating law of neural networks (NNs).