scholarly journals Design of a decentralized detection of interacting LTI systems

2002 ◽  
Vol 8 (3) ◽  
pp. 233-248 ◽  
Author(s):  
Shamanth Shankar ◽  
Swaroop Darbha ◽  
Aniruddha Datta
Keyword(s):  
State Estimation ◽  
Error Propagation ◽  
Estimation Error ◽  
Design Procedure ◽  
The State ◽  
Decentralized Detection ◽  
Estimation Errors ◽  
Look Ahead ◽  
Important Concern ◽  
Detection Filter

In this paper, the problem of designing a decentralized detection filter for a large homogeneous collection of LTI systems is considered. The collection of systems considered here draws inspiration from platoons of vehicles, and the considered interactions amongst systems in the collection are banded and lower triangular, mimicking the typical “look-ahead” nature of interactions in a platoon of vehicles. A fault in a system propagates to other systems in the collection via such interactions.The decentralized detection filter for the collection is composed of interacting detection filters, one for each system. The feasibility of communicating the state estimates to other systems in the collection is assumed here. An important concern is the propagation of state estimation errors. In order that the state estimation errors not amplify as they propagate, aℋ∞constraint on the state estimation error propagation dynamics is imposed. A sufficient condition for constructing a decentralized detection filter for the collection is presented. An example is provided to illustrate the design procedure.

Design of a Decentralized Detection Filter for a Class of Interconnected Systems

2001 ◽  
Author(s):  
Shamanth Shankar ◽  
Darbha Swaroop ◽  
Aniruddha Datta
Keyword(s):  
Transfer Function ◽  
State Estimation ◽  
Interconnected Systems ◽  
Decentralized Detection ◽  
Estimation Errors ◽  
State Information ◽  
Identical Structure ◽  
Full State ◽  
Filtering Problem ◽  
Detection Filter

Abstract In this paper, we consider the problem of designing a decentralized detection filter for a class of homogeneous interconnected systems; in this class of systems, all subsystems have identical structure. A fault in a subsystem propagates via interactions with other subsystems in the collection. The decentralized detection filter is composed of interacting detection filters, one for each subsystem. We assume communication of state estimates amongst subsystems to be feasible. A concern, dealt with here, is that of propagation of state estimation errors. It is treated as a Ĥ∞ filtering problem with full state information, by requiring the transfer function from the propagated input of the ith subsystem to that of the (i + 1)st subsystem to have a magnitude less than unity at all frequencies.

scholarly journals State Uncertainty Normality Detection

2019 ◽  
Vol 67 (3) ◽  
pp. 1044-1062
Author(s):  
Sven K. Flegel ◽  
James C. Bennett
Keyword(s):  
State Estimation ◽  
Orbit Determination ◽  
Estimation Error ◽  
The State ◽  
Body Motion ◽  
Optical Observations ◽  
Test Cases ◽  
Unscented Transform ◽  
State Uncertainty ◽  
Random Particle

AbstractTwo fundamentally different approaches of determining normality of the probability density function of the state estimation error are compared by application to a range of test cases. The first method is the Henze-Zirkler test, which operates on a random particle sample. The variability of its result is quantified. Using this method, departure from normality has been found to occur in three stages which are detailed. The second test compares the offset in whitened space of the predicted state to the predicted covariance mean obtained from the unscented transform. This test is much more efficient than the random particle based approach and can be applied using any perturbations model. The comparison is performed on the state estimation error in Cartesian space and using two-body motion without process noise. The more efficient, unscented transform based approach shows excellent agreement with the Henze-Zirkler test for constructed test cases. Application to orbit determination results from passive optical observations assessed with a Batch-Least-Squares orbit determination however reveals some discrepancies which have yet to be understood and underline the importance of rigorous testing.

scholarly journals Macroscopic Traffic State Estimation: Understanding Traffic Sensing Data-Based Estimation Errors

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Paul B. C. van Erp ◽  
Victor L. Knoop ◽  
Serge P. Hoogendoorn
Keyword(s):  
State Estimation ◽  
Traffic Management ◽  
Estimation Error ◽  
Ground Truth ◽  
Traffic Information ◽  
Estimation Errors ◽  
Sensing Data ◽  
Traffic Conditions ◽  
Traffic State ◽  
Traffic State Estimation

Traffic state estimation is a crucial element in traffic management systems and in providing traffic information to road users. In this article, we evaluate traffic sensing data-based estimation error characteristics in macroscopic traffic state estimation. We consider two types of sensing data, that is, loop-detector data and probe speed data. These data are used to estimate the mean speed in a discrete space-time mesh. We assume that there are no errors in the sensing data. This allows us to study the errors resulting from the differences in characteristics between the sensing data and desired estimate together with the incomplete description of the relation between the two. The aim of the study is to evaluate the dependency of this estimation error on the traffic conditions and sensing data characteristics. For this purpose, we use microscopic traffic simulation, where we compare the estimates with the ground truth using Edie’s definitions. The study exposes a relation between the error distribution characteristics and traffic conditions. Furthermore, we find that it is important to account for the correlation between individual probe data-based estimation errors. Knowledge related to these estimation errors contributes to making better use of the available sensing data in traffic state estimation.

scholarly journals Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor

2016 ◽  
Vol 14 (1) ◽  
pp. 934-945
Author(s):  
Cenker Biçer ◽  
Levent Özbek ◽  
Hasan Erbay
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Stochastic Systems ◽  
Estimation Error ◽  
The State ◽  
Forgetting Factor ◽  
The Matrix ◽  
Stochastic Framework ◽  
Theoretical Results

AbstractIn this paper, the stability of the adaptive fading extended Kalman filter with the matrix forgetting factor when applied to the state estimation problem with noise terms in the non–linear discrete–time stochastic systems has been analysed. The analysis is conducted in a similar manner to the standard extended Kalman filter’s stability analysis based on stochastic framework. The theoretical results show that under certain conditions on the initial estimation error and the noise terms, the estimation error remains bounded and the state estimation is stable.The importance of the theoretical results and the contribution to estimation performance of the adaptation method are demonstrated interactively with the standard extended Kalman filter in the simulation part.

scholarly journals Parameter and State Estimation of Shear Buildings Using Spline Interpolation and Linear Integral Filters

2018 ◽  
Vol 2018 ◽  
pp. 1-21
Author(s):  
Antonio Concha ◽  
Luis Alvarez-Icaza
Keyword(s):  
State Estimation ◽  
Spline Interpolation ◽  
Estimation Error ◽  
Fast Response ◽  
High Gain ◽  
The State ◽  
Small Scale ◽  
Identification Method ◽  
Linear Integral ◽  
Shear Building

A parameter identification method and a high gain observer are proposed in order to identify the model and to recover the state of a seismically excited shear building using acceleration responses of the ground and instrumented floors levels, as well as the responses at noninstrumented floors, which are reconstructed by means of cubic spline shape functions. The identification method can be implemented online or offline and uses Linear Integral Filters, whose bandwidth must enclose the spectrum of a seismically excited building. On the other hand, the proposed state observer estimates the displacements and velocities of all the structure floors using the model estimated by the identification method. The observer allows obtaining a fast response and reducing the state estimation error, while depending on a single gain. The performance of the parameter and state estimators is verified through experiments carried out on a five-story small scale building.

scholarly journals Cooperative Localization Approach for Multi-Robot Systems Based on State Estimation Error Compensation

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3842 ◽  
Author(s):  
Zhang ◽  
Cao
Keyword(s):  
State Estimation ◽  
Error Compensation ◽  
Estimation Error ◽  
The State ◽  
Cooperative Localization ◽  
Communication Delays ◽  
Extended Kalman Filtering ◽  
Robot Systems ◽  
Localization Approach ◽  
Multi Robot

In order to improve the localization accuracy of multi-robot systems, a cooperative localization approach with communication delays was proposed in this paper. In the proposed method, the reason for the time delay of the robots’ cooperative localization approach was analyzed first, and then the state equation and measure equation were reconstructed by introducing the communication delays into the states and measurements. Furthermore, the cooperative localization algorithm using the extended Kalman filtering technique based on state estimation error compensation was proposed to reduce the state estimation error of delay filtering. Finally, the simulation and experiment results demonstrated that the proposed algorithm can achieve good performance in location in the presence of communication delay while having reduced computational and communicative cost.

State Estimation of an Advanced Rowing Machine Using Optimized Kalman Filtering

2017 ◽  
Author(s):  
Hanieh Mohammadi ◽  
Gholamreza Khademi ◽  
Dan Simon ◽  
Hanz Richter
Keyword(s):  
Standard Deviation ◽  
State Estimation ◽  
Estimation Error ◽  
Simultaneous Estimation ◽  
Force Estimation ◽  
Estimation Errors ◽  
Input Force ◽  
Load Cells ◽  
Friction Models ◽  
Active Impedance

This research addresses the problem of state estimation of an advanced rowing machine with energy regeneration. It is assumed that the states of the system, which are position, velocity, and capacitor charge, are measurable. The user force input to the system can be measured by load cells. It is shown that the need for load cells can be eliminated by estimating the force with an unknown-input Kalman filter. The estimated states and the unknown user force input are passed to the controller of the system, which is either an inversion-based controller or a semi-active impedance controller. Two friction models are considered for this system: Coulomb friction, and LuGre friction. The Kalman gains are tuned using an evolutionary algorithm to minimize the standard deviation of the estimation error. The results verify the effectiveness of the proposed approach for simultaneous estimation of the states and the input force. The standard deviation of the state estimation errors are only 10% of their measurement noise. The standard deviation of the input force estimation error is 0.1 N when using an optimized Kalman gain, which is only 25% of the value obtained when using manually tuned gains.

scholarly journals Distributed State Estimation Based Distributed Model Predictive Control

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1327
Author(s):  
Jing Zeng ◽  
Jinfeng Liu
Keyword(s):  
Model Predictive Control ◽  
State Estimation ◽  
Predictive Control ◽  
Estimation Error ◽  
The State ◽  
Distributed Model ◽  
Actual State ◽  
Output Measurement ◽  
Distributed Model Predictive Control ◽  
Distributed State Estimation

In this work, we consider output-feedback distributed model predictive control (DMPC) based on distributed state estimation with bounded process disturbances and output measurement noise. Specifically, a state estimation scheme based on observer-enhanced distributed moving horizon estimation (DMHE) is considered for distributed state estimation purposes. The observer-enhanced DMHE ensures that the state estimates of the system reach a small neighborhood of the actual state values quickly and then maintain within the neighborhood. This implies that the estimation error is bounded. Based on the state estimates provided by the DMHE, a DMPC algorithm is developed based on Lyapunov techniques. In the proposed design, the DMHE and the DMPC are evaluated synchronously every sampling time. The proposed output DMPC is applied to a simulated chemical process and the simulation results show the applicability and effectiveness of the proposed distributed estimation and control approach.

Adaptive estimation algorithm of boost-phase trajectory using binary asynchronous observation

2016 ◽  
Vol 230 (14) ◽  
pp. 2661-2672 ◽  
Author(s):  
Nan Wu ◽  
Lei Chen ◽  
Yongjun Lei ◽  
Fankun Meng
Keyword(s):  
State Estimation ◽  
Phase Trajectory ◽  
Prior Information ◽  
Estimation Error ◽  
The State ◽  
Estimation Accuracy ◽  
Unknown Input ◽  
Noise Variance ◽  
Process Noise ◽  
Variance Matrix

A kind of adaptive filter algorithm based on the estimation of the unknown input is proposed for studying the adaptive adjustment of process noise variance of boost phase trajectory. Polynomial model is used as the motion model of the boost trajectory, truncation error is regarded as an equivalent to the process noise and the unknown input and process noise variance matrix is constructed from the estimation value of unknown input according to the quantitative relationship among the unknown input, the state estimation error, and optimal process noise variance. The simulation results show that in the absence of prior information, the unknown input is estimated effectively in terms of magnitude, a positive definite matrix of process noise covariance which is close to the optimal value is constructed real-timely, and the state estimation error approximates the error lower bound of the optimal estimation. The estimation accuracy of the proposed algorithm is similar to that of the current statistical model algorithm using accurate prior information.

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