Robust Covariance Intersection Fusion Steady-State Kalman Filter with Uncertain Parameters

We present two time invariant models for Global Systems for Mobile (GSM) position tracking, which describe the movement inx-axis andy-axis simultaneously or separately. We present the time invariant filters as well as the steady state filters: the classical Kalman filter and Lainiotis Filter and the Join Kalman Lainiotis Filter, which consists of the parallel usage of the two classical filters. Various implementations are proposed and compared with respect to their behavior and to their computational burden: all time invariant and steady state filters have the same behavior using both proposed models but have different computational burden. Finally, we propose a Finite Impulse Response (FIR) implementation of the Steady State Kalman, and Lainiotis filters, which does not require previous estimations but requires a well-defined set of previous measurements.

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Estimation of Steady-State Optimal Filter Gain From Nonoptimal Kalman Filter Residuals

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.2899251 ◽

1994 ◽

Vol 116 (3) ◽

pp. 550-553 ◽

Cited By ~ 5

Author(s):

Chung-Wen Chen ◽

Jen-Kuang Huang

Keyword(s):

Kalman Filter ◽

Steady State ◽

Finite Number ◽

Discrete Time ◽

Stochastic System ◽

Moving Average ◽

Ar Model ◽

Numerical Example ◽

Measurement Noises ◽

Time Invariant

This paper proposes a new algorithm to estimate the optimal steady-state Kalman filter gain of a linear, discrete-time, time-invariant stochastic system from nonoptimal Kalman filter residuals. The system matrices are known, but the covariances of the white process and measurement noises are unknown. The algorithm first derives a moving average (MA) model which relates the optimal and nonoptimal residuals. The MA model is then approximated by inverting a long autoregressive (AR) model. From the MA parameters the Kalman filter gain is calculated. The estimated gain in general is suboptimal due to the approximations involved in the method and a finite number of data. However, the numerical example shows that the estimated gain could be near optimal.

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STEADY-STATE MODELLING OF PEM FUEL CELLS USING GRADIENT-BASED OPTIMIZER

DYNA INGENIERIA E INDUSTRIA ◽

10.6036/10099 ◽

2021 ◽

Vol DYNA-ACELERADO (0) ◽

pp. [ 8 pp.]-[ 8 pp.]

Author(s):

SALAH KAMAL ◽

ATTIA EL-FERGANY ◽

EHAB EHAB ELSAYED ELATTAR ◽

AHMED AGWA

Keyword(s):

Fuel Cells ◽

Steady State ◽

Fuel Cell ◽

Optimization Problem ◽

Pem Fuel Cells ◽

Test Cases ◽

Uncertain Parameters ◽

Gradient Based ◽

High Degree ◽

Exchange Membrane

The accuracy of fuel cell (FC) models is important for the further numerical simulations and analysis at several conditions. The electrical (I-V) characteristic of the polymer exchange membrane fuel cells (PEMFCs) has high degree of nonlinearity comprising uncertain seven parameters as they aren’t given in fabricator's datasheets. These seven parameters need to be obtained to have the PEMFC model in order. This research addresses an up-to-date application of the gradient-based optimizer (GBO) to generate the best estimated values of such uncertain parameters. The estimation of these uncertain parameters is adapted as optimization problem having a cost function (CF) subjects to set of self-constrained limits. Three test cases of widely used PEMFCs units; namely, SR-12, 250-W module and NedStack PS6 to appraise the performance of the GBO are demonstrated and analyzed. The best values of the CF are 0.000142, 0.33598, and 2.10025 V2 for SR-12, 250-W module and NedStack PS6; respectively. Furthermore, the assessment of the GBO-based model is made by comparing its obtained results with the experiential results of these typical PEMFCs plus comparisons to other methods. At a due stage, many scenarios as a result of operating variations in regard to inlet regulation pressures and unit temperatures are performed. The copped reported results of the studied scenarios indicate the effectiveness of the GBO in establishing an accurate PEMFC model.

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