scholarly journals One-Step Predictive H2 FIR Tracking under Persistent Disturbances and Data Errors

2021 ◽  
Vol 17 ◽  
pp. 87-92
Author(s):  
OSCAR IBARRA-MANZANO ◽  
JOSE ANDRADE-LUCIO ◽  
YURIY S. SHMALIY ◽  
YUAN XU
Keyword(s):  
Measurement Errors ◽  
Finite Impulse Response ◽  
Information Loss ◽  
Industrial Processes ◽  
Tracking Problem ◽  
Data Errors ◽  
Initial Errors ◽  
Robot Tracking ◽  
One Step ◽  
Persistent Disturbances

Information loss often occurs in industrial processes under unspecified impacts and data errors. Therefore robust predictors are required to assure the performance. We design a one-step H2 optimal finite impulse response (H2-OFIR) predictor under persistent disturbances, measurement errors, and initial errors by minimizing the squared weighted Frobenius norms for each error. The H2-OFIR predictive tracker is tested by simulations assuming Gauss-Markov disturbances and data errors. It is shown that the H2-OFIR predictor has a better robustness than the Kalman and unbiased FIR predictor. An experimental verification is provided based on the moving robot tracking problem

Determining Pipeline Depth of Cover at River Crossings by Data Integration

2018 ◽  
Author(s):  
Peter Chan ◽  
Jin Wei
Keyword(s):  
Data Integration ◽  
Survey Data ◽  
Measurement Errors ◽  
Regulatory Requirement ◽  
Dry Land ◽  
Data Errors ◽  
Survey Results ◽  
Inspection Data

Having sufficient depth of cover ensures pipeline protection and is a regulatory requirement. Confirming the pipeline depth of cover on dry land is generally easy and produces accurate results. However, determining the pipeline depth of cover at a river crossing can be problematic because of accessibility difficulties and the increased measurement errors from aboveground surveys. The difficulty of determining the pipeline depth of cover at river crossings can be resolved by integrating both the aboveground survey data and the inline inspection data. By comparing both sets of data, errors from both above survey data and inline inspection data can be detected. This paper describes watercourse management, aboveground DOC surveys, and a spreadsheet based tool developed for both the quick verification of aboveground survey results, and the calculation of the true DOC at water crossings without needing to set new GPS tie-points on both banks of the crossing and running a new ILI.

Information Flow in Chaotic Symbolic Dynamics for Finite and Infinitesimal Resolution

1997 ◽  
Vol 07 (01) ◽  
pp. 97-105 ◽  
Author(s):  
Gustavo Deco ◽  
Christian Schittenkopf ◽  
Bernd Schürmann
Keyword(s):  
Mutual Information ◽  
Information Flow ◽  
Symbolic Dynamics ◽  
Initial Conditions ◽  
Deterministic Chaos ◽  
Conditional Entropy ◽  
Information Loss ◽  
Statistical Correlations ◽  
The Future ◽  
One Step

We introduce an information-theory-based concept for the characterization of the information flow in chaotic systems in the framework of symbolic dynamics for finite and infinitesimal measurement resolutions. The information flow characterizes the loss of information about the initial conditions, i.e. the decay of statistical correlations (i.e. nonlinear and non-Gaussian) between the entire past and a point p steps into the future as a function of p. In the case where the partition generating the symbolic dynamics is finite, the information loss is measured by the mutual information that measures the statistical correlations between the entire past and a point p steps into the future. When the partition used is a generator and only one step ahead is observed (p = 1), our definition includes the Kolmogorov–Sinai entropy concept. The profiles in p of the mutual information describe the short- and long-range forecasting possibilities for the given partition resolution. For chaos it is more relevant to study the information loss for the case of infinitesimal partitions which characterizes the intrinsic behavior of the dynamics on an extremely fine scale. Due to the divergence of the mutual information for infinitesimal partitions, the "intrinsic" information flow is characterized by the conditional entropy which generalizes the Kolmogorov–Sinai entropy for the case of observing the uncertainty more than one step into the future. The intrinsic information flow offers an instrument for characterizing deterministic chaos by the transmission of information from the past to the future.

Adaptive backstepping control for parallel robot with uncertainties in dynamics and kinematics

Robotica ◽  
2014 ◽  
Vol 32 (6) ◽  
Author(s):  
Jing Zou ◽  
John K. Schueller
Keyword(s):  
Measurement Errors ◽  
Robot Manipulator ◽  
Kinematic Model ◽  
Parallel Robot ◽  
Joint Space ◽  
Backstepping Control ◽  
Adaptive Backstepping ◽  
Robot Tracking ◽  
Robot Systems ◽  
Backstepping Controller

SUMMARYIt is common in robot tracking control that controllers are designed based on the exact kinematic model of the robot manipulator. However, because of measurement errors and changes of states in practice, the original kinematic model is often no longer accurate and will degrade the control result. An adaptive backstepping controller is designed here for parallel robot systems with kinematics and dynamics uncertainties. Backstepping control is used to manage the transformation between the errors in task space and joint space. Adaptive control is utilized to compensate for uncertainties in both dynamics and kinematics. The controller demonstrated good performance in simulation.

scholarly journals Theoretical limitations to englacial velocity calculations

1994 ◽  
Vol 40 (136) ◽  
pp. 509-518 ◽  
Author(s):  
David B. Bahr ◽  
W. Tad Pfeffer ◽  
Mark F. Meier
Keyword(s):  
Lyapunov Exponent ◽  
Measurement Errors ◽  
Ice Sheets ◽  
Information Loss ◽  
Ice Thickness ◽  
Error Growth ◽  
Numerical Techniques ◽  
Linear And Nonlinear ◽  
Using Data

Abstract To study the dynamics of ice sheets and glaciers, velocities at the bed of a glacier must be measured directly or calculated using data gathered from boreholes and surface surveys. Boreholes to the bed are expensive and time-consuming to drill, so the determination of basal velocity is almost exclusively by numerical inversion of velocities observed at the surface. For non-linearly viscous glaciers, a perturbation analysis demonstrates that inversions for englacial velocities will magnify measurement errors at an exponential rate with depth. The rate at which calculation errors grow is proportional to a Lyapunov exponent, a measure of “information loss” which is shown to be a simple linear function of spatial frequency with a coefficient depending on Glen’s flow-law exponent, n. The coefficient decreases with increasing non-linearity, demonstrating that inversions with non-linearly viscous ice have smaller calculation errors than inversions with linearly viscous ice. In both the linear and nonlinear cases, the Lyapunov exponent (and rate of error growth) increases with decreasing wavelength, which limits velocity calculations at the bed to wavelengths on the order of one ice thickness or greater. This limitation is theoretical and cannot be countered by more accurate survey data or special numerical techniques.

scholarly journals UFIR Filtering Under Uncertain One-Step Delayed and Missing Data

2021 ◽  
Vol 16 ◽  
pp. 191-196
Author(s):  
Karen Uribe-Murcia ◽  
Yuriy S. Shmaliy
Keyword(s):  
Game Theory ◽  
Wireless Sensor Network ◽  
Missing Data ◽  
Finite Impulse Response ◽  
Measurement Data ◽  
Transmission Probability ◽  
Wireless Sensor ◽  
Packet Dropout ◽  
Bernoulli Distribution ◽  
One Step

This paper develops the unbiased finite impulse response (UFIR) filter for wireless sensor network (WSN) systems whose measurements are affected by random delays and packet dropout due to inescapable failures in the transmission and sensors. The Bernoulli distribution is used to model delays in arrived measurement data with known transmission probability. The effectiveness of the UFIR filter is compared experimentally to the KF and game theory recursive H1 filter in terms of accuracy and robustness employing the GPS-measured vehicle coordinates transmitted with latency over WSN.

scholarly journals Theoretical limitations to englacial velocity calculations

1994 ◽  
Vol 40 (136) ◽  
pp. 509-518 ◽  
Author(s):  
David B. Bahr ◽  
W. Tad Pfeffer ◽  
Mark F. Meier
Keyword(s):  
Lyapunov Exponent ◽  
Measurement Errors ◽  
Ice Sheets ◽  
Information Loss ◽  
Ice Thickness ◽  
Error Growth ◽  
Numerical Techniques ◽  
Linear And Nonlinear ◽  
Using Data

AbstractTo study the dynamics of ice sheets and glaciers, velocities at the bed of a glacier must be measured directly or calculated using data gathered from boreholes and surface surveys. Boreholes to the bed are expensive and time-consuming to drill, so the determination of basal velocity is almost exclusively by numerical inversion of velocities observed at the surface. For non-linearly viscous glaciers, a perturbation analysis demonstrates that inversions for englacial velocities will magnify measurement errors at an exponential rate with depth. The rate at which calculation errors grow is proportional to a Lyapunov exponent, a measure of “information loss” which is shown to be a simple linear function of spatial frequency with a coefficient depending on Glen’s flow-law exponent, n. The coefficient decreases with increasing non-linearity, demonstrating that inversions with non-linearly viscous ice have smaller calculation errors than inversions with linearly viscous ice. In both the linear and nonlinear cases, the Lyapunov exponent (and rate of error growth) increases with decreasing wavelength, which limits velocity calculations at the bed to wavelengths on the order of one ice thickness or greater. This limitation is theoretical and cannot be countered by more accurate survey data or special numerical techniques.

scholarly journals Real-Time Estimation of Temperature Time Derivative in Inertial Measurement Unit by Finite-Impulse-Response Exponential Regression on Updates

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1299
Author(s):  
Alexander Kozlov ◽  
Ilya Tarygin
Keyword(s):  
Real Time ◽  
Impulse Response ◽  
Measurement Errors ◽  
Finite Impulse Response ◽  
Time Derivative ◽  
Time Estimation ◽  
Measurement Unit ◽  
Model Parameters ◽  
Navigation Systems ◽  
Sensor Output

We present a filtering technique that allows estimating the time derivative of slowly changing temperature measured via quantized sensor output in real time. Due to quantization, the output may appear constant for several minutes in a row with the temperature actually changing over time. Another issue is that measurement errors do not represent any kind of white noise. Being typically the case in high-grade inertial navigation systems, these phenomena amid slow variations of temperature prevent any kind of straightforward assessment of its time derivative, which is required for compensating hysteresis-like thermal effects in inertial sensors. The method is based on a short-term temperature prediction represented by an exponentially decaying function, and on the finite-impulse-response Kalman filtering in its numerically stable square-root form, employed for estimating model parameters in real time. Instead of using all of the measurements, the estimation involves only those received when quantized sensor output is updated. We compare the technique against both an ordinary averaging numerical differentiator and a conventional Kalman filter, over a set of real samples recorded from the inertial unit.

scholarly journals Formation of the Convergence Functions of Errors of Input Data of Measurement Systems Computing Components on the Basis of the Finite Automatics Theory

2019 ◽  
pp. 37-40
Author(s):  
O. Krychevets
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Input Data ◽  
Transformation Function ◽  
Measurement Systems ◽  
Data Errors ◽  
Measurement Results ◽  
Data Error ◽  
Iterative Procedures ◽  
Number Of Iterations

This paper presents the results of an investigation into the behavior of the functions of transforming the input data errors for different types of measurement systems’ computing components in order to use their generalized models developed on the basis of the finite automata theory. It is shown that, depending on the kind and value of an input data error transformation function (metrological condition of computing components), the errors of measurement results obtained with the systems’ measuring channels have a determinate character of changes in both static and dynamic regimes of computing components. Determined are the basic dependences of the errors of measurement results upon the input data errors, and upon the types of input data transformation functions; given are the results of their calculation. The investigation results demonstrate a linear character of the dependence of measurement result errors upon the input data errors ΔХ{(tn). In addition, the transformation function calculation f = ΔY{(tn)/ΔХ{(tn) gives its steady state value f = 1,0, i.e. a computing component does not transform the input data error, and does not reverse its sign. For the iterative procedures, the input data errors do not affect the final measurement result, and its accuracy. The measurement error values Δуn depend on the iteration number, and decrease with the increasing number. Of particular interest is the behavior of the function of transforming the input data errors: first, its values are dependent upon the number of iterations; second, f < 1, which clearly shows that the input data errors decrease with the increa­sing number of iterations; and third, the availability of values f = 0 indicates that the function of transforming the input data errors is able to «swallow up» the input data error at the end of the computational procedure. For the linear-chain structures, data have been obtained for a predominantly linear dependence of the measurement error Δs on the input data error Δх, and for the absence of the chain’s transformation function f dependence on the input data errors Δх. For the computing components having a cyclic structure, typi­cal is the same dependence of measurement errors Δt on the input data errors and on the behavior of transformation function ft/x which are specific to the above mentioned computing components that rea­lize iterative procedures. The difference is that the computing components having a cyclic structure realize the so-called (sub)space iteration as opposed to the time iteration specific to the computing components considered. The computing components having a complicated structure (e.g. serial-cyclic, serial-parallel, etc.) demonstrate the dependence of measurement errors on the input data errors which is specific to the linear link that, with such a structure, is determinative for eva­luating the measurement error. Also the function of transforming the input data errors behaves similarly.

Research on a new algorithm for the robot tracking problem

2017 ◽  
pp. 123-128
Author(s):  
Yanfei Liu ◽  
Tongming Zhang ◽  
Jingjing Yang ◽  
Qi Tian
Keyword(s):  
Tracking Problem ◽  
Robot Tracking
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