Articles published in IJEER


Direct digital control scheme for controlling hybrid dynamic systems using ANN state estimator and inverse dynamics controller

An Estimator Based Inverse Dynamics Controller (EBIDC), which utilizes an Artificial Neural Network (ANN) based state estimation scheme for nonlinear autonomous hybrid systems which are subjected to state disturbances and measurement noises that are stochastic in nature, is proposed in this paper. A salient feature of the proposed scheme is that it offers better state estimates and hence a better control of non-measurable state variables with a nonlinear approach in correcting the a priori estimates by avoiding statistical linearization involved in existing approaches based on derivative free estimation methods. Simulation results guarantees significant reduction in Integral Square Error (ISE) and standard deviation (σ) of error, between the controlled variable and set point and control signal computation time when compared with best existing related work based on Unscented Kalman Filter (UKF) and Ensembeled Kalman Filter (EnKF).

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State Estimation based Inverse Dynamic Controller for Hybrid system using Artificial Neural Network

State Estimation Based Inverse Dynamics Controller (SEBIDC), which utilizes an Artificial Neural Network (ANN) based state estimation scheme for nonlinear autonomous hybrid systems which are subjected to state disturbances and measurement noises that are stochastic in nature. A salient feature of the proposed scheme is that it offers better state estimates and hence a better control of non-measurable state variables with a nonlinear approach in correcting the a priori estimates by avoiding statistical linearization involved in existing approaches based on derivative free estimation methods.

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Adjustable Spectral Characteristics to Design FIR Filter Using Two-Variable Window Function

This paper present a new dynamic window function having variable or adjustable spectral characteristics. A new dynamic window function proposed in this paper is a combination of hamming, Blackman-Harris, chebwin, and Kaiser Window function. Blackman-Harris, chebwin, and Kaiser Window functions have been used to compare with the suggested proposed window function i.e. a combination of hamming, Blackman-Harris, chebwin, and Kaiser Window function.

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