scholarly journals Self-Tuning Distributed Fusion Filter for Multi-Sensor Networked Systems with Unknown Packet Receiving Rates, Noise Variances, and Model Parameters

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4436 ◽  
Author(s):  
Wang ◽  
Sun
Keyword(s):  
Optimal Estimation ◽  
Original System ◽  
Identification Problem ◽  
Sensor Data ◽  
Model Parameters ◽  
Packet Dropouts ◽  
Estimation Algorithms ◽  
Self Tuning ◽  
Time Systems ◽  
Distributed Fusion

In this study, we researched the problem of self-tuning (ST) distributed fusion state estimation for multi-sensor networked stochastic linear discrete-time systems with unknown packet receiving rates, noise variances (NVs), and model parameters (MPs). Packet dropouts may occur when sensor data are sent to a local processor. A Bernoulli distributed stochastic variable is adopted to depict phenomena of packet dropouts. By model transformation, the identification problem of packet receiving rates is transformed into that of unknown MPs for a new augmented system. The recursive extended least squares (RELS) algorithm is used to simultaneously identify packet receiving rates and MPs in the original system. Then, a correlation function method is used to identify unknown NVs. Further, a ST distributed fusion state filter is achieved by applying identified packet receiving rates, NVs, and MPs to the corresponding optimal estimation algorithms. It is strictly proven that ST algorithms converge to optimal algorithms under the condition that the identifiers for parameters are consistent. Two examples verify the effectiveness of the proposed algorithms.

Identification of Contact Dynamics Model Parameters From Constrained Robotic Operations

2005 ◽  
Vol 128 (2) ◽  
pp. 307-318 ◽  
Author(s):  
M. Weber ◽  
K. Patel ◽  
O. Ma ◽  
I. Sharf
Keyword(s):  
Identification Problem ◽  
Real Space ◽  
Contact Dynamics ◽  
Sensor Data ◽  
Model Parameters ◽  
End Effector ◽  
Surface Areas ◽  
Novel Strategy ◽  
Dynamics Simulations ◽  
Contact Tasks

With the fast advances in computing technology, contact dynamics simulations are playing a more important role in the design, verification, and operation support of space systems. The validity of computer simulation depends not only on the underlying mathematical models but also on the model parameters. This paper describes a novel strategy of identifying contact dynamics parameters based on the sensor data collected from a robot performing contact tasks. Unlike existing identification algorithms, this methodology is applicable to complex contact geometries where contact between mating objects occurs at multiple surface areas in a time-variant fashion. At the same time, the procedure requires only measurements of end-effector forces/moments and the kinematics information for the end-effector and the environment. Similarly to other methods, the solution is formulated as a linear identification problem, which can be solved with standard numerical techniques for overdetermined systems. Efficacy, precision, and sensitivity of the identification methodology are investigated in simulation with two examples: A cube sliding in a wedge and a payload/fixture combination modeled after a real space-manipulator task.

scholarly journals Finite-time estimation algorithms for LPV discrete-time systems with application to output feedback stabilization

Automatica ◽  
2021 ◽  
Vol 125 ◽  
pp. 109436
Author(s):  
Khadidja Chaib-Draa ◽  
Ali Zemouche ◽  
Fazia Bedouhene ◽  
Rajesh Rajamani ◽  
Yan Wang ◽  
...  
Keyword(s):  
Discrete Time ◽  
Output Feedback ◽  
Finite Time ◽  
Time Estimation ◽  
Feedback Stabilization ◽  
Estimation Algorithms ◽  
Output Feedback Stabilization ◽  
Discrete Time Systems ◽  
Time Systems

scholarly journals Industrial Plume Properties Retrieved by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1865
Author(s):  
Gabriel Calassou ◽  
Pierre-Yves Foucher ◽  
Jean-François Léon
Keyword(s):  
Matched Filter ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Model Parameters ◽  
Surface Reflectance ◽  
Sequential Method ◽  
Modal Radius ◽  
Sentinel 2

Stack emissions from the industrial sector are a subject of concern for air quality. However, the characterization of the stack emission plume properties from in situ observations remains a challenging task. This paper focuses on the characterization of the aerosol properties of a steel plant stack plume through the use of hyperspectral (HS) airborne remote sensing imagery. We propose a new method, based on the combination of HS airborne acquisition and surface reflectance imagery derived from the Sentinel-2 Multi-Spectral Instrument (MSI). The proposed method detects the plume footprint and estimates the surface reflectance under the plume, the aerosol optical thickness (AOT), and the modal radius of the plume. Hyperspectral surface reflectances are estimated using the coupled non-negative matrix factorization (CNMF) method combining HS and MSI data. The CNMF reduces the error associated with estimating the surface reflectance below the plume, particularly for heterogeneous classes. The AOT and modal radius are retrieved using an optimal estimation method (OEM), based on the forward model and allowing for uncertainties in the observations and in the model parameters. The a priori state vector is provided by a sequential method using the root mean square error (RMSE) metric, which outperforms the previously used cluster tuned matched filter (CTMF). The OEM degrees of freedom are then analysed, in order to refine the mask plume and to enhance the quality of the retrieval. The retrieved mean radii of aerosol particles in the plume is 0.125 μμm, with an uncertainty of 0.05 μμm. These results are close to the ultra-fine mode (modal radius around 0.1 μμm) observed from in situ measurements within metallurgical plant plumes from previous studies. The retrieved AOT values vary between 0.07 (near the source point) and 0.01, with uncertainties of 0.005 for the darkest surfaces and above 0.010 for the brightest surfaces.

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

scholarly journals Round-robin evaluation of nadir ozone profile retrievals: methodology and application to MetOp-A GOME-2

2014 ◽  
Vol 7 (11) ◽  
pp. 11481-11546 ◽  
Author(s):  
A. Keppens ◽  
J.-C. Lambert ◽  
J. Granville ◽  
G. Miles ◽  
R. Siddans ◽  
...  
Keyword(s):  
Information Content ◽  
Optimal Estimation ◽  
Round Robin ◽  
Atmospheric Composition ◽  
User Requirements ◽  
Satellite Measurements ◽  
Estimation Algorithms ◽  
Ozone Profile ◽  
Reference Measurements ◽  
Resolution Estimation

Abstract. A methodology for the round-robin evaluation and geophysical validation of ozone profile data retrieved from nadir UV backscatter satellite measurements is detailed and discussed, consisting of dataset content studies, information content studies, co-location studies, and comparisons with reference measurements. Within ESA's Climate Change Initiative on ozone (Ozone_cci project), the proposed round-robin procedure is applied to two nadir ozone profile datasets retrieved at KNMI and RAL, using their respective OPERA v1.26 and RAL v2.1 optimal estimation algorithms, from MetOp-A GOME-2 measurements taken in 2008. The ground-based comparisons use ozonesonde and lidar profiles as reference data, acquired by the Network for the Detection of Atmospheric Composition Change (NDACC), Southern Hemisphere Additional Ozonesonde programme (SHADOZ), and other stations of WMO's Global Atmosphere Watch. This direct illustration highlights practical issues that inevitably emerge from discrepancies in e.g. profile representation and vertical smoothing, for which different recipes are investigated and discussed. Several approaches for information content quantification, vertical resolution estimation, and reference profile resampling are compared and applied as well. The paper concludes with compliance estimates of the two GOME-2 ozone profile datasets with user requirements from GCOS and from climate modellers.

scholarly journals Finite-Time Bounded Tracking Control for Linear Discrete-Time Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Fucheng Liao ◽  
Yingxue Wu ◽  
Xiao Yu ◽  
Jiamei Deng
Keyword(s):  
Discrete Time ◽  
Finite Time ◽  
Tracking Control ◽  
Original System ◽  
Linear Matrix ◽  
Discrete Time Systems ◽  
Error System ◽  
Finite Time Boundedness ◽  
Boundedness Problem ◽  
Time Systems

A finite-time bounded tracking control problem for a class of linear discrete-time systems subject to disturbances is investigated. Firstly, by applying a difference method to constructing the error system, the problem is transformed into a finite-time boundedness problem of the output vector of the error system. In fact, this is a finite-time boundedness problem with respect to the partial variables. Secondly, based on the partial stability theory and the research methods of finite-time boundedness problem, a state feedback controller formulated in form of linear matrix inequality is proposed. Based on this, a finite-time bounded tracking controller of the original system is obtained. Finally, a numerical example is presented to illustrate the effectiveness of the controller.

scholarly journals Robust Guaranteed-Cost Preview Repetitive Control for Polytopic Uncertain Discrete-Time Systems

Algorithms ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 20 ◽  
Author(s):  
Yong-Hong Lan ◽  
Jun-Jun Xia ◽  
Yue-Xiang Shi
Keyword(s):  
Dynamic System ◽  
Control Problem ◽  
Discrete Time ◽  
Repetitive Control ◽  
Original System ◽  
Linear Matrix ◽  
Discrete Time Systems ◽  
Guaranteed Cost ◽  
Time Systems ◽  
Repetitive Controller

In this paper, a robust guaranteed-cost preview repetitive controller is proposed for a class of polytopic uncertain discrete-time systems. In order to improve the tracking performance, a repetitive controller, combined with preview compensator, is inserted in the forward channel. By using the L-order forward difference operator, an augmented dynamic system is constructed. Then, the guaranteed-cost preview repetitive control problem is transformed into a guaranteed-cost control problem for the augmented dynamic system. For a given performance index, the sufficient condition of asymptotic stability for the closed-loop system is derived by using a parameter-dependent Lyapunov function method and linear matrix inequality (LMI) techniques. Incorporating the controller obtained into the original system, the guaranteed-cost preview repetitive controller is derived. A numerical example is also included, to show the effectiveness of the proposed method.

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