Model Predictive Control (MPC) based on Discrete Space Vector Modulation (DSVM) has the advantages of simple mathematical model and fast dynamic response. It is widely used in permanent magnet synchronous motor (PMSM). Additionally, control performance DSVM-MPC influenced by accuracy parameters select speed optimal voltage vector. In order to identify accurately, predictive for PMSM discrete sp...

In this paper we concentrate on MPSK carrier frequency estmation based on random data modulation. We present a fast, open-loop frequency estimation and tracking techinque, which combines a feedforward estimator stucture and a recursive least square (RLS) predictor. It is suitable for the frequency estimation and large frequency acquisition and tracking required of burst mode satellite modems op...

The Data rates and spectrum efficiency of Wireless Mobile Communication have been significantly improved over the last decade or so. Recently, the advanced systems such as 3GPP LTE and terrestrial digital TV broadcasting have been sophisticatedly developed using OFDM and CDMA technology. In general, most mobile communication systems transmit bits of information in the radio space to receiver. T...

A new insight into vanadium redox flow batteries (VRFB) parameter estimation is presented. Driven by the electric vehicles proliferation, a hybrid fast-charging station with grid and renewable energy connection particularly considered. In this stationary application, VRFB operating as buffering module. This topology could contribute to reduce cost of charging station. However, make viable techn...

A new multichannel filtered-x recursive least square algorithm for active noise control systems is proposed. It is shown that the use of the filtered-x structure, instead of the commonly used modified filtered-x structure lead to a more efficient implementation and similar convergence performance and stability. The paper is also focused on examining the benefits of auxiliary normal equations so...

A new non-linear Recursive Least Squares (RLS) algorithm is presented in the context of pattern classification problems. The algorithm incorporates the non-linearity of the filter’s output in the updating rules of the classical RLS algorithm. The proposed method yields lower stationary error levels when compared to the standard LMS and RLS algorithms in a classical application of pattern classi...

System identification and parameter estimation are important to obtain information from systems which are difficult to model and that are usually presented as BlackBox models. This work presents a point to point parameter estimation of a generalized non-deterministic system, whose results are variable through time, by using an exponential Forgetting Factor (FF). An average approximation is used...

A least-mean-square (LMS) and a recursive-least-square (RLS) algorithm are derived for estimation of the symbol period in communication signals. The algorithms are based on measurements of the time elapsed between two consecutive transitions detected in noisy signals. Number of symbol periods between the transitions is estimated too. In order to have the number equal to the true one, the initia...

This paper presents distributed conjugate gradient (CG) algorithms for distributed parameter estimation and spectrum estimation over wireless sensor networks. In particular, distributed conventional conjugate gradient (CCG) and modified conjugate gradient (MCG) algorithms are developed with incremental and diffusion adaptive cooperation strategies. The distributed CCG and MCG algorithms have an...

The paper aims to identify and control the coupled mass-spring-damper system. A nonlinear discrete polynomial structure is elaborated. Its parameters are estimated using Recursive Least Squares (RLS) algorithm. Moreover, a feedback stabilizing control law based on Kronecker power is designed. Finally, simulations are presented to illustrate the effectiveness of the proposed structure. Keywords—...