This paper presents a new observer design methodology for a time varying actuator fault Estimation. A new linear matrix inequality (LMI) design algorithm is developed to tackle the limitations (e.g. equality constraint and Robustness problems) of the well known so called fast adaptive fault Estimation observer (FAFE). The FAFE is capable of estimating a wide range of time-varying actuator fault signals via augmenting the Luenberger-observer by a proportional integral fault estimator feedback. Within this framework, the main contribution of this paper is the proposal of new LMI formulation that incorporates the use of L2 norm minimization: (a) to obviate the FAFE equality constraint in order to relax the design algorithm, (b) to ensure Robustness against external disturbances, (c) to provide additional degrees of freedom to solve the infeasible optimization problem via assigning different proportional and integral fault estimator gains. Finally, a VTOL aircraft simulation example is used to illustrate the effectiveness of the proposed FAFE.

In this paper, a novel filter is provided that estimates the states of any nonlinear system, both in the presence and absence of uncertainty with high accuracy. It is well understood that a Robust filter design is a compromise between the Robustness and the Estimation accuracy. In fact, a Robust filter is designed to obtain an accurate and suitable performance in presence of modelling errors. So in the absence of any unknown or time-varying uncertainties, the Robust filter does not provide the desired performance. The new method provided in this paper, which is named hybrid Robust cubature Kalman filter (CKF), is constructed by combining a traditional CKF and a novel Robust CKF. The novel Robust CKF is designed by merging a traditional CKF with an uncertainty estimator so that it can provide the desired performance in the presence of uncertainty. Since the presence of uncertainty results in a large innovation value, the hybrid Robust CKF adapts itself according to the value of the normalized innovation. The CKF and Robust CKF filters are run in parallel and at any time, a suitable decision is taken to choose the estimated state of either the CKF or the Robust CKF as the final state Estimation. To validate the performance of the proposed filters, two examples are given that demonstrate their promising performance.

In this research, the Master Surgical Scheduling (MSS) problem at the tactical level of hospital planning and scheduling is studied. Before constructing the MSS, a strategic level problem, i. e., Case Mix Planning Problem (CMPP), shall be solved to allocate the capacity of Operating Room (OR) to each surgical specialty. In order to make an e ective coordination between CMPP and MSS, the results obtained from solving the CMPP are used as an input for the respective MSS. In the MSS, frequently performed elective surgeries are planned in a cyclic manner for a pre-de ned planning period. As a part of the planning process, it is required to adjust downstream limited resources, such as Intensive Care Unit (ICU) and ward beds, with patient ow. In this study, a mathematical model is developed to construct an MSS. The proposed model is based on a lexicographic goal programming approach, which is aimed at minimizing the OR spare time while considering the results of the CMPP. In this paper, the data required to solve MSS are collected from a medium-sized Iranian hospital. Hence, a Robust Estimation method is applied to reduce the e ect of outliers on the decision-making process. The results show the performance of the proposed method against the solution put in practice in the hospital.

Robust adaptive Estimation of unknown parameter has been an important issue in recent years for reliable operation in the distributed networks. The conventional adaptive Estimation algorithms that rely on mean square error (MSE) criterion exhibit good performance in the presence of Gaussian noise, but their performance drastically decreases under impulsive noise. In this paper, we propose a Robust adaptive Estimation algorithm for networks with cyclic cooperation. We model the impulsive noise as the realization of alpha-stable distribution. Here, we move beyond MSE criterion and define the Estimation problem in terms of a modified cost function which exploits higher order moments of the error. To derive a distributed and adaptive solution, we first recast the problem as an equivalent form amenable to distributed implementation. Then, we resort to the steepest-descent and statistical approximation to obtain the proposed algorithm. We present some simulations results which reveal the superior performance of the proposed algorithm than the incremental least mean square (ILMS) algorithm in impulsive noise environments.

In the Bayesian framework, Robust Bayesian methods concern on Estimation of unknown parameters, or prediction of future observation, by specifying a class of priors instead of a single prior. Robust Bayesian methods have been used extensively in actuarial sciences for Estimation of premium and prediction of future claim size. In this paper we consider Robust Bayes Estimation of premium and prediction of future claim size under two classes of prior distribution and under the scale invariant squared error loss function. Finally, by a simulation study and using prequential analysis, we compare the obtained Robust Bayes estimators of future claim size.

In the presence of outliers in the data set, the utilization of Robust regression tools for mean Estimationis a widely established practice in survey sampling with single auxiliary variable. Abid et al. (2018),with the aid of some non-conventional location measures and traditional OLS, proposed a class of meanestimators using information on two supplementary variates under a simple random sampling framework. The utilization of non-traditional measures of location, especially in the presence of outliers,performed better than existing conventional estimators. In this study, we have proposed a new class ofestimators of mean utilizing quantile regression. The general forms of MSE and MMSE are also derived.The theoretical findings are being reinforced by different real-life data sets and simulation study.

Let S be the matrix of residual sum of square in linear modelY=Ab+e, where the matrix of errors is distributed as elliptically contoured with unknown scale matrix S. For Stein loss function, L1 (S ,S) =tr (SˆS-1) -log |(SˆS-1|-p, and squared loss function, L2 ((SˆS-1) =tr ((SˆS-1-I) 2, we offer empirical Bayes estimators of S, which dominate any scalar multiple of S, i.e., aS, by an effective amount. In fact, this study somehow shows that improvement of the empirical Bayes estimators obtained under the normality assumption remains Robust under elliptically contoured model.

Parameter Estimation is the first step in constructing any control chart. Most estimators of mean and dispersion are sensitive to the presence of outliers. The data may be contaminated by outliers either locally or globally. The exciting Robust estimators deal only with global contamination. In this paper a Robust estimator for dispersion is proposed to reduce the effect of local contamination when estimating the parameters. The results have shown that the introduced estimator is more precise in estimating the dispersion when there are outliers within the subgroups. Simulation results indicate that Robustness and efficiency of the proposed dispersion estimator is considerably high and its sensitivity to the changes in mean and standard deviation of any subgroup is roughly lower than the other estimators being compared.