A novel sensor-less system for induction motors is designed. This novel design is based on an adaptative fuzzy system, which is obtained by mixing openloop estimator response with steady-state estimator. This open-loop estimator is improved by means of using an adaptative fuzzy controlled filter that selects the optimised cut frequency. The results validate the entire work in not only transient...

let be a random sample from a normal distribution with unknown mean and known variance the usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible estimator. in many practical situations, is known in advance to lie in an interval, say for some in this case, the maximum likelihood estimator changes and d...

This paper concerns the use of the finite signalto-noise (FSN) model to design a state estimator. First, the sufficient conditions for the existence of the state estimator are provided. These conditions are expressed in terms of linear matrix inequalities (LMIs) and the parametrization of all the solutions is provided. Then an LMI based estimator design is addressed and the algorithm to solve t...

A model for nonlinear ltering is considered in which errors in state dynamics and observations are modelled deterministically. Mortensen's deterministic estimator and a minimax estimator are considered. A risk sensitive stochastic lter model with small state and observation noise intensities is also considered. The minimax estimator is obtained in the zero noise intensity limit, using asymptoti...

This paper studies the recovery and state compression of low-rank Markov chains from empirical trajectories. We propose a non-convex estimator based on rank-constrained likelihood maximization. Statistical upper bounds are provided for the Kullback-Leiber divergence and the `2 risk between the estimator and the true transition matrix. The estimator reveals a compressed state space of the Markov...

This paper intends to improve the accuracy of power system State Estimation (SE) by introducing a hybrid linear robust state estimator. To this end, automatic bad data rejection is accomplished through an M-estimator, i.e. Schweppe-type estimator with Huber loss function. The method Iteratively Reweighted Least Squares (IRLS) used maximize likelihood function in M-estimator. Leverage measuremen...

This paper describes a method for constrained state estimation based on receding horizon optimization. The case here studied corresponds to an optimization horizon of size two and a constraint horizon of size one. It is shown that, in this case, a simple closed-form solution can be obtained. The resulting estimator is called a Rolling Horizon Estimator with Constraint Horizon One. It is shown t...

This study presents a data-driven approach in conjunction with an adaptive extended Kalman filter (AEKF) to estimate lithium-ion batteries' state of charge (SOC) online. The Thevenin battery model is used evaluate the effects using voltage and current. advantages Lagrange multiplier method are utilized battery. continuously decreases error adjust gain AEKF for accurate SOC estimation. Various c...

In this paper the state estimator is developed for the multi area power system with SCADA and PMU measurements using WLS method. The measurement jacobian matrix has been modified for the PMU measurements. The behavior of the elements because of the current measurements in the measurement jacobian matrix is examined for any possible ill-conditioning of the state estimator gain matrix. State esti...