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We present a non‐Gaussian ensemble data assimilation method based on the maximum‐likelihood ensemble filter, which allows for any combination of Gaussian, lognormal, and reverse lognormal errors in both the background and the observations. The technique is fully nonlinear, does not require a tangent linear model, and uses a Hessian preconditioner to minimise the cost function efficiently in ensemble space. When the Gaussian assumption is relaxed, the results show significant improvements in the analysis skill within two atmospheric toy models, and the performance of data assimilation systems for (semi)bounded variables is expected to improve. [ABSTRACT FROM AUTHOR]

Magnetic resonance fingerprinting (MRF) is a novel quantitative MR technique that simultaneously provides multiple tissue property maps. When optimizing MRF scans, modeling undersampling errors and field imperfections in cost functions for direct measurement of quantitative errors will make the optimization results more practical and robust. However, optimizing such cost function is computationally expensive and impractical for MRF optimization with tens of thousands of iterations. Here, we introduce a fast MRF simulator to simulate aliased images from actual scan scenarios including undersampling and system imperfections, which substantially reduces computational time and allows for direct error estimation of the quantitative maps and efficient sequence optimization. We evaluate the performance and computational speed of the proposed approach by simulations and in vivo experiments. The simulations from the proposed method closely approximate the signals and MRF maps from in vivo scans, with 158 times shorter processing time than the conventional simulation method using Non-uniform Fourier transform. We also demonstrate the power of applying the fast MRF simulator in MRF sequence optimization. The optimized sequences are validated with in vivo scans to assess the image quality and accuracy. The optimized sequences produce artifact-free T1 and T2 maps in 2D and 3D scans with equivalent mapping accuracy as the human-designed sequence but at shorter scan times. Incorporating the proposed simulator in the MRF optimization framework makes direct estimation of undersampling errors during the optimization process feasible, and provide optimized MRF sequences that are robust against undersampling artifacts and field inhomogeneity. [ABSTRACT FROM AUTHOR]

A sampling error‐based finite‐set predictive current control (FS‐PCC) is proposed in this article for the open‐end winding induction motor (OEWIM) drive. The proposed scheme controls the zero‐sequence current (ZSC) alongside the stator currents. In a model‐free approach, this method predicts the future of ZSC and stator current components by the stator current and voltage sampling errors. In this way, the parameters of the OEWIM are not utilised in the prediction algorithm of the FS‐PCC. So, the proposed method is robust against the variation of the parameter. Moreover, this article presents a simple vector selection technique for the FS‐PCC of the OEWIM. The proposed technique has two cost functions and a simple algebraic equation to put the voltage vectors (VVs) in the prediction algorithm. The first cost function uses VVs that do not have the zero‐sequence voltage component. Then, the algebraic equation determines VVs that must be utilised in the second cost function. Finally, the optimum VV is selected by the second cost function. In the proposed scheme, the prediction algorithm is iterated 14 times instead of 27 iterations of the conventional predictive algorithm. So, besides establishing a novel model‐free prediction algorithm, the proposed method has almost 50% fewer calculations. The validity of the proposed sampling error‐based FS‐PCC and the simplified vector selection technique has been verified through experimental tests. [ABSTRACT FROM AUTHOR]

Summary: In this article, an event‐based intelligent critic algorithm is developed to address the optimal tracking control problem for a type of discrete‐time nonlinear systems. The nonlinear optimal tracking control design is replaced by solving the optimal regulation problem of the error system. Then, the generalized value iteration algorithm is employed to obtain the admissible tracking control law with off‐line learning. Next, a novel triggering condition is designed to reduce the update times of the controller and improve the resource utilization. It is emphasized that this triggering condition is established based on the iteration of the time‐triggered mechanism. Moreover, in order to realize the cost guarantee of the error system, the real cost function is proved to possess a predetermined upper bound. By analysis, it is shown that the error system is asymptotically stable while the tracking error and the sampling signal are uniformly ultimately bounded during the process of training neural networks. Finally, two examples are conducted to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A local data assimilation method, Local DA, is introduced. The proposed algorithm aims to perform hybrid and multiscale analyses simultaneously yet independently for each grid, vertical column or column group and aims to flexibly perform analyses with or without ensemble perturbations. To achieve these goals, an error sample matrix is constructed by explicitly computing the localized background error correlation matrix of model variables that are projected onto observation-associated grids (e.g., radar velocity) or columns (e.g., precipitable water vapor). This error sample matrix allows Local DA to apply the conjugate gradient (CG) method to solve the cost function and to perform localization in the model-variable space, the observation-variable space, or both spaces (double-space localization). To assess the Local DA performance, a typhoon case is simulated, and a multiscale observation network comprising sounding, wind profiler, precipitable water vapor, and radar data is built; additionally, a time-lagged ensemble is employed. The results show that experiments using the hybrid covariance and double-space localization yield smaller analysis errors than experiments without the static covariance and experiments without double-space localization. Moreover, the hybrid covariance plays a more important role than does localization when a poor time-lagged ensemble is used. The results further indicate that applying the CG method for each local analysis does not result in a discontinuity issue, and the wall clock time of Local DA implemented in parallel is halved as the number of cores doubles. [ABSTRACT FROM AUTHOR]