RLS and Kalman Filter Identifiers Based Adaptive SVC Controller

Barnawi, A.; Albakkar, A.; Malik, O.P.

doi:10.1109/naps.2007.4402374

Cited by 9 publications

(7 citation statements)

References 13 publications

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“…RLS can also be described by a state-space model (Chun et al 1998) and can be recognized as a special case of the Kalman filter (Sayed 2008, Sayed andKailath 1994). The RLS method and Kalman filter showed comparable performances in several applications (Barnawi et al 2007, Chun et al 1998, Lanzi et al 2006. In addition, RLS is known to be more robust and to converge faster than NLMS (Haykin 2002).…”

Section: Relationship Between Algorithmsmentioning

confidence: 99%

Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry

et al. 2010

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A photoplethysmography (PPG) signal provides very useful information about a subject's hemodynamic status in a hospital or ubiquitous environment. However, PPG is very vulnerable to motion artifacts, which can significantly distort the information belonging to the PPG signal itself. Thus, the reduction of the effects of motion artifacts is an important issue when monitoring the cardiovascular system by PPG. There have been many adaptive techniques to reduce motion artifacts from PPG signals. In the present study, we compared a method based on the fixed-interval Kalman smoother with the usual adaptive filtering algorithms, e.g. the normalized least mean squares, recursive least squares and the conventional Kalman filter. We found that the fixed-interval Kalman smoother reduced motion artifacts from the PPG signal most effectively. Therefore, the use of the fixed-interval Kalman smoother can reduce motion artifacts in PPG, thus providing the most reliable information that can be deduced from the reconstructed PPG signals.

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Section: Relationship Between Algorithmsmentioning

confidence: 99%

Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry

et al. 2010

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“…a) If J (4) < J (3) , expanding is successful, and a new SPX is formed by peaks x (0) , x (1) , x (4) . b) If J (4) J (3) , expanding is unsuccessful.…”

Section: Improved Simplex Algorithmmentioning

confidence: 99%

“…SVC is an important reactive compensation device that has been used in power systems to maintain bus voltage at a constant level, improve the transient stability, dampen the systems and suppress voltage flicker. In fact, to dynamically support the voltage in power systems and enhance SVC capability, the design of SVC controllers has attracted much attention from scholars and engineers [3,4].…”

Section: Introductionmentioning

confidence: 99%

Voltage control for static var compensator using novel optimal nonlinear PI

Luo

Fang

et al. 2011

J. Control Theory Appl.

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Static var compensators (SVCs) are used for voltage and reactive power control in power systems. In this paper, we will consider the structure of SVCs that mainly consists of Y -connection of mechanically-switched capacitor (MSC) and delta connection of thyrister-controlled reactor (TCR). First, the control model of SVC was established in this paper. Then, a novel optimal nonlinear voltage controller for SVCs is proposed. The proposed SVC voltage controller consists of a nonlinear function and a conventional PI controller. The improved simplex method (SPX) is presented to adjust and optimize the parameters of the nonlinear PI controller in real-time, and to make transient state response procedure of SVC optimum. The integration of time multiplied absolute error (ITAE) is adopted as the optimized objective function of SPX. Simulation and engineering application results show that the proposed voltage control method is able to track reference voltage value of SVC immediately. It also demonstrates that the whole SVC control system can synthetically compensate reactive power.

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“…The least-mean-square (LMS) algorithm and the recursive least-squares (RLS) algorithms have established themselves as the principal tools for linear adaptive filtering. Lopes and Gerald used the LMS algorithm to get the faster convergence and a much higher noise immunity when the reference signal vector norm takes on a low value [2], while Barnawi to get much less convergence time [3]. Rosendo Macías and Expósito presented a method for self-tuning of the model error covariance to overcome the sudden changes of the input signal, so that they could properly track signal fluctuations in digital protection applications [4].…”

Section: Introductionmentioning

confidence: 99%

Melt Pressure Signature Tracking Using an Adaptive Kalman Filter in Microinjection Molding

Liu

Yung

et al. 2013

Advances in Mechanical Engineering

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In order to manufacture high quality microproducts, the precision control of injected plastic melt in the injection chamber during a microinjection process requires real-time tracking of the melt pressure when the melt passes through the nozzle. A novel type of adaptive Kalman filter algorithm based on F-distribution is proposed in this paper. This adaptive Kalman filter can switch the system between the steady state and transient state by comparing the differences of input data in F-distribution. By resetting the Kalman gain and other relevant parameters, the adaptive function guarantees the convergence of the filtered signal during the tracking process and tracks the moments which sudden changes occur in the pressure signature. The simulation experiment results show that the method can reduce the effect of measurement noise more quickly and effectively. The method is proven to be effective for microinjection molding applications.

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RLS and Kalman Filter Identifiers Based Adaptive SVC Controller

Cited by 9 publications

References 13 publications

Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry

Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry

Voltage control for static var compensator using novel optimal nonlinear PI

Melt Pressure Signature Tracking Using an Adaptive Kalman Filter in Microinjection Molding

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