Engine Parameter Estimation in Test Cells Using Hybrid Physics/Empirical Models

2011 ◽  
Author(s):  
Jonathan A. DeCastro ◽  
Dean K. Frederick ◽  
Liang Tang
Keyword(s):  
Kalman Filter ◽  
Hybrid Model ◽  
Dynamic Performance ◽  
Health Condition ◽  
State Variables ◽  
Engine Model ◽  
Physical Plant ◽  
Highly Nonlinear ◽  
Test Cells ◽  
Estimation System

Estimation of engine parameters such as thrust in test cells is a difficult process due to the highly nonlinear nature of the engine dynamics, the complex interdependency of thrust and the engine’s health condition, and factors that corrupt thrust measurements due to test stand construction. Because the frequency content of the corrupting dynamics is close to the engine’s dynamics, filtering the thrust signal is not sufficient for extraction of the true dynamic content. A configurable thrust estimation system is developed for accurate data reduction which provides “virtual” measurements of thrust and other necessary parameters at steady state and during aggressive engine transients. The thrust estimation framework consists of a representative nonlinear engine model coupled with an adaptive structural dynamics model. To account for discrepancies between the physics-based model and the true engine, a hybrid model using a novel neural network (NN) enhancement to a physics-based engine model is presented that reduces certain modeling errors between the engine model and the physical plant. This includes engine-to-engine variation, engine degradation and any essential neglected dynamics. To fuse the model and sensor measurements, this hybrid model is used within a constant-gain extended Kalman filter batch estimator which is able to reconstruct the true dynamic performance of the engine using noisy or corrupted sensor measurements and control inputs. The Kalman filter estimates measured and unmeasured parameters and state variables such as engine component deterioration parameters and effective flow areas.

scholarly journals Parameters Estimation and Prediction of Water Movement and Solute Transport in Layered, Variably Saturated Soils Using the Ensemble Kalman Filter

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1520
Author(s):  
Zheng Jiang ◽  
Quanzhong Huang ◽  
Gendong Li ◽  
Guangyong Li
Keyword(s):  
Kalman Filter ◽  
Solute Transport ◽  
Ensemble Kalman Filter ◽  
Water Movement ◽  
Parameters Estimation ◽  
Saturated Soils ◽  
Hydrological Models ◽  
State Variables ◽  
Ensemble Size ◽  
Highly Nonlinear

The parameters of water movement and solute transport models are essential for the accurate simulation of soil moisture and salinity, particularly for layered soils in field conditions. Parameter estimation can be achieved using the inverse modeling method. However, this type of method cannot fully consider the uncertainties of measurements, boundary conditions, and parameters, resulting in inaccurate estimations of parameters and predictions of state variables. The ensemble Kalman filter (EnKF) is well-suited to data assimilation and parameter prediction in Situations with large numbers of variables and uncertainties. Thus, in this study, the EnKF was used to estimate the parameters of water movement and solute transport in layered, variably saturated soils. Our results indicate that when used in conjunction with the HYDRUS-1D software (University of California Riverside, California, CA, USA) the EnKF effectively estimates parameters and predicts state variables for layered, variably saturated soils. The assimilation of factors such as the initial perturbation and ensemble size significantly affected in the simulated results. A proposed ensemble size range of 50–100 was used when applying the EnKF to the highly nonlinear hydrological models of the present study. Although the simulation results for moisture did not exhibit substantial improvement with the assimilation, the simulation of the salinity was significantly improved through the assimilation of the salinity and relative solutetransport parameters. Reducing the uncertainties in measured data can improve the goodness-of-fit in the application of the EnKF method. Sparse field condition observation data also benefited from the accurate measurement of state variables in the case of EnKF assimilation. However, the application of the EnKF algorithm for layered, variably saturated soils with hydrological models requires further study, because it is a challenging and highly nonlinear problem.

scholarly journals Comparative Study of a Super-Twisting Sliding Mode Observer and an Extended Kalman Filter for a Freeway Traffic System

2015 ◽  
Vol 15 (2) ◽  
pp. 141-158 ◽  
Author(s):  
H. Majid ◽  
H. Abouaïssa
Keyword(s):  
Kalman Filter ◽  
Comparative Study ◽  
Extended Kalman Filter ◽  
Sliding Mode ◽  
Dynamic Performance ◽  
Sliding Mode Observer ◽  
State Variables ◽  
Highway Traffic ◽  
Traffic State ◽  
Geometric Conditions

Abstract Traffic state estimation represents one of the important ingredients for traffic prediction and forecasting. The work presented in this paper deals with the estimation of traffic state variables (density and speed), using the so called Super- Twisting Sliding Mode Observer (STSM). Several numerical simulations, using simulated and real data, show the relevance of the proposed approach. In addition, a comparative study with the Extended Kalman Filter (EKF) is carried-out. The comparison indices concern convergence and stability, dynamic performance and robustness. The design of the two observers is achieved using a nonlinear second order traffic flow model in the same highway traffic and geometric conditions.

An Iterative Ensemble Kalman Filter for Multiphase Fluid Flow Data Assimilation

SPE Journal ◽  
2007 ◽  
Vol 12 (04) ◽  
pp. 438-446 ◽  
Author(s):  
Yaqing Gu ◽  
Dean S. Oliver
Keyword(s):  
Porous Media ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Flow In Porous Media ◽  
Petroleum Reservoir ◽  
State Variables ◽  
Highly Nonlinear

Summary The dynamical equations for multiphase flow in porous media are highly nonlinear and the number of variables required to characterize the medium is usually large, often two or more variables per simulator gridblock. Neither the extended Kalman filter nor the ensemble Kalman filter is suitable for assimilating data or for characterizing uncertainty for this type of problem. Although the ensemble Kalman filter handles the nonlinear dynamics correctly during the forecast step, it sometimes fails badly in the analysis (or updating) of saturations. This paper focuses on the use of an iterative ensemble Kalman filter for data assimilation in nonlinear problems, especially of the type related to multiphase ow in porous media. Two issues are key:iteration to enforce constraints andensuring that the resulting ensemble is representative of the conditional pdf (i.e., that the uncertainty quantification is correct). The new algorithm is compared to the ensemble Kalman filter on several highly nonlinear example problems, and shown to be superior in the prediction of uncertainty. Introduction For linear problems, the Kalman filter is optimal for assimilating measurements to continuously update the estimate of state variables. Kalman filters have occasionally been applied to the problem of estimating values of petroleum reservoir variables (Eisenmann et al. 1994; Corser et al. 2000), but they are most appropriate when the problems are characterized by a small number of variables and when the variables to be estimated are linearly related to the observations. Most data assimilation problems in petroleum reservoir engineering are highly nonlinear and are characterized by many variables, often two or more variables per simulator gridblock. The problem of weather forecasting is in many respects similar to the problem of predicting future petroleum reservoir performance. The economic impact of inaccurate predictions is substantial in both cases, as is the difficulty of assimilating very large data sets and updating very large numerical models. One method that has been recently developed for assimilating data in weather forecasting is ensemble Kalman filtering (Evensen 1994; Houtekamer and Mitchell 1998; Anderson and Anderson 1999; Hamill et al. 2000; Houtekamer and Mitchell 2001; Evensen 2003). It has been demonstrated to be useful for weather prediction over the North Atlantic. The method is now beginning to be applied for data assimilation in groundwater hydrology (Reichle et al. 2002; Chen and Zhang 2006) and in petroleum engineering (Nævdal et al. 2002, 2005; Gu and Oliver 2005; Liu and Oliver 2005a; Wen and Chen 2006, 2007; Zafari and Reynolds 2007; Gao et al. 2006; Lorentzen et al. 2005; Skjervheim et al. 2007; Dong et al. 2006), but the applications to state variables whose density functions are bimodal has proved problematic (Gu and Oliver 2006). For applications to nonlinear assimilation problems, it is useful to think of the ensemble Kalman filter as a least squares method that obtains an averaged gradient for minimization not from a variational approach but from an empirical correlation between model variables (Anderson 2003; Zafari et al. 2006). In addition to providing a mean estimate of the variables, a Monte Carlo estimate of uncertainty can be obtained directly from the variability in the ensemble.

Data Assimilation Using the Constrained Ensemble Kalman Filter

SPE Journal ◽  
2010 ◽  
Vol 16 (02) ◽  
pp. 331-342 ◽  
Author(s):  
Hemant A. Phale ◽  
Dean S. Oliver
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
History Matching ◽  
Model Parameters ◽  
State Variables ◽  
Reservoir Properties ◽  
Compositional Model ◽  
Highly Nonlinear ◽  
Nonnegativity Constraints

Summary When the ensemble Kalman filter (EnKF) is used for history matching, the resulting updates to reservoir properties sometimes exceed physical bounds, especially when the problem is highly nonlinear. Problems of this type are often encountered during history matching compositional models using the EnKF. In this paper, we illustrate the problem using an example in which the updated molar density of CO2 in some regions is observed to take negative values while molar densities of the remaining components are increased. Standard truncation schemes avoid negative values of molar densities but do not address the problem of increased molar densities of other components. The results can include a spurious increase in reservoir pressure with a subsequent inability to maintain injection. In this paper, we present a method for constrained EnKF (CEnKF), which takes into account the physical constraints on the plausible values of state variables during data assimilation. In the proposed method, inequality constraints are converted to a small number of equality constraints, which are used as virtual observations for calibrating the model parameters within plausible ranges. The CEnKF method is tested on a 2D compositional model and on a highly heterogeneous three-phase-flow reservoir model. The effect of the constraints on mass conservation is illustrated using a 1D Buckley-Leverett flow example. Results show that the CEnKF technique is able to enforce the nonnegativity constraints on molar densities and the bound constraints on saturations (all phase saturations must be between 0 and 1) and achieve a better estimation of reservoir properties than is obtained using only truncation with the EnKF.

Robust active disturbance attenuation control of an uncertain quadrotor

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nigar Ahmed ◽  
Ajeet kumar Bhatia ◽  
Syed Awais Ali Shah
Keyword(s):  
Control Algorithm ◽  
Sliding Mode ◽  
The State ◽  
Disturbance Attenuation ◽  
State Variables ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Content Type ◽  
Highly Nonlinear ◽  
The Stability

PurposeThe aim of this research is to design a robust active disturbance attenuation control (RADAC) technique combined with an extended high gain observer (EHGO) and low pass filter (LPF).Design/methodology/approachFor designing a RADAC technique, the sliding mode control (SMC) method is used. Since the standard method of SMC exhibits a chattering phenomenon in the controller, a multilayer sliding mode surface is designed for avoiding the chattering. In addition, to attenuate the unwanted uncertainties and disturbances (UUDs), the techniques of EHGO and LPF are deployed. Besides acting as a patch for disturbance attenuation, the EHGO design estimates the state variables. To investigate the stability and effectiveness of the designed control algorithm, the stability analysis followed by the simulation study is presented.FindingsThe major findings include the design of a chattering-free RADAC controller based on the multilayer sliding mode surface. Furthermore, a criterion of integrating the LPF scheme within the EHGO scheme is also developed to attenuate matched and mismatched UUDs.Practical implicationsIn practice, the quadrotor flight is opposed by different kinds of the UUDs. And, the model of the quadrotor is a highly nonlinear underactuated model. Thus, the dynamics of the quadrotor model become more complex and uncertain due to the additional UUDs. Hence, it is necessary to design a robust disturbance attenuation technique with the ability to estimate the state variables and attenuate the UUDs and also achieve the desired control objectives.Originality/valueDesigning control methods to attenuate the disturbances while assuming that the state variables are known is a common practice. However, investigating the uncertain plants with unknown states along with the disturbances is rarely taken in consideration for the control design. Hence, this paper presents a control algorithm to address the issues of the UUDs as well as investigate a criterion to reduce the chattering incurred in the controller due to the standard SMC algorithm.

scholarly journals Implementation of Hybrid Model of Particle Filter and Kalman Filter based Real-Time Tracking for handling Occlusion on Beagleboard-xM

2014 ◽  
Vol 95 (7) ◽  
pp. 31-37 ◽  
Author(s):  
Jharna Majumdar ◽  
Parashar Dhakal ◽  
Nabin Sharma Rijal ◽  
Amar Mani Aryal ◽  
Nilesh Kumar Mishra
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Real Time ◽  
Hybrid Model ◽  
Real Time Tracking

scholarly journals Neural PD Controller for an Unmanned Aerial Vehicle Trained with Extended Kalman Filter

Algorithms ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 40 ◽  
Author(s):  
Javier Gomez-Avila ◽  
Carlos Villaseñor ◽  
Jesus Hernandez-Barragan ◽  
Nancy Arana-Daniel ◽  
Alma Y. Alanis ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Mobile Robotics ◽  
Pid Controllers ◽  
Pd Controller ◽  
The Neural Network ◽  
Control Scheme ◽  
Highly Nonlinear ◽  
Aerial Vehicle ◽  
Linear Controllers

Flying robots have gained great interest because of their numerous applications. For this reason, the control of Unmanned Aerial Vehicles (UAVs) is one of the most important challenges in mobile robotics. These kinds of robots are commonly controlled with Proportional-Integral-Derivative (PID) controllers; however, traditional linear controllers have limitations when controlling highly nonlinear and uncertain systems such as UAVs. In this paper, a control scheme for the pose of a quadrotor is presented. The scheme presented has the behavior of a PD controller and it is based on a Multilayer Perceptron trained with an Extended Kalman Filter. The Neural Network is trained online in order to ensure adaptation to changes in the presence of dynamics and uncertainties. The control scheme is tested in real time experiments in order to show its effectiveness.

scholarly journals Mixed-Degree Spherical Simplex-Radial Cubature Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Shiyuan Wang ◽  
Yali Feng ◽  
Shukai Duan ◽  
Lidan Wang
Keyword(s):  
Kalman Filter ◽  
Computational Complexity ◽  
Kalman Filters ◽  
Order Moment ◽  
Low Degree ◽  
Cubature Kalman Filter ◽  
Highly Nonlinear ◽  
Spherical Simplex ◽  
The Cost ◽  
High Degree

Conventional low degree spherical simplex-radial cubature Kalman filters often generate low filtering accuracy or even diverge for handling highly nonlinear systems. The high-degree Kalman filters can improve filtering accuracy at the cost of increasing computational complexity; nevertheless their stability will be influenced by the negative weights existing in the high-dimensional systems. To efficiently improve filtering accuracy and stability, a novel mixed-degree spherical simplex-radial cubature Kalman filter (MSSRCKF) is proposed in this paper. The accuracy analysis shows that the true posterior mean and covariance calculated by the proposed MSSRCKF can agree accurately with the third-order moment and the second-order moment, respectively. Simulation results show that, in comparison with the conventional spherical simplex-radial cubature Kalman filters that are based on the same degrees, the proposed MSSRCKF can perform superior results from the aspects of filtering accuracy and computational complexity.

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