Parameter Error Identification Research Articles

Strategic Placement of Phasor Measurements for Parameter Error Identification

Previous work on parameter error identification based on normalized Lagrange multipliers has shown that the errors in parameters of branches constituting single- or double-edge cutsets could not be detected or identified without incorporating phasor measurements. In order to address this deficiency, a computationally efficient algorithm has been developed to identify all such cutsets and documented in a recent paper. This paper builds on that work and formulates a strategic phasor measurement placement problem whose objective is to ensure detectability and identifiability of all network parameters in a given system. A practical solution is developed for this problem and simulation results for IEEE 14-, 30- and 57-bus test systems are provided to illustrate its implementation.

Substructure identification for shear structures: cross-power spectral density method

In this paper, a substructure identification method for shear structures is proposed. A shear structure is divided into many small substructures; utilizing the dynamic equilibrium of a one-floor substructure, an inductive identification problem is formulated, using the cross-power spectral densities between structural floor accelerations and a reference response, to estimate the parameters of that one story. Repeating this procedure, all story parameters of the shear structure are identified from top to bottom recursively. An identification error analysis is performed for the proposed substructure method, revealing how uncertain factors (e.g. measurement noise) in the identification process affect the identification accuracy. According to the error analysis, a smart reference selection rule is designed to choose the optimal reference response that further enhances the identification accuracy. Moreover, based on the identification error analysis, explicit formulae are developed to calculate the variances of the parameter identification errors. A ten-story shear structure is used to illustrate the effectiveness of the proposed substructure method. The simulation results show that the method, combined with the reference selection rule, can very accurately identify structural parameters despite large measurement noise. Furthermore, the proposed formulae provide good predictions for the variances of the parameter identification errors, which are vital for providing accurate warnings of structural damage.

Single and Double Edge Cutset Identification in Large Scale Power Networks

Previous work on parameter error identification that is based on calculation of Lagrange multipliers has shown that the errors on single edge cutsets could not be detected and errors on double edge cutsets could not be identified without incorporating phasor measurement units (PMUs). Hence, the first step to address such deficiencies is identification of all single and double edge cutsets for large scale power networks. This paper presents a numerically efficient method which relies on factorization of reduced incidence matrix of the network. Results obtained by implementing the method in IEEE 14-, 30-, and 57-bus test systems and a 4520-bus transmission system are presented to illustrate its computational performance.

An inverse method for bounded error parameter identification

Abstract In this paper we present an algorithm to bound parameters estimated from very small data sets and use this method to suggest optimal times for data collection. The method is deterministic in that we assume that one has error bars on the data. The basic idea is based on bounded error parameter identification methods and we use this framework to assign a measure of quality to the estimated parameter. Exploiting the dynamics of the underlying mathematical model allows us to describe the quality of the estimated parameter in a way that, due to the small number of data points, is not appropriate for traditional statistical techniques. The algorithm in this paper differs from traditional bounded error parameter identification techniques in that one computes the membership set by solving an inverse problem rather than a forward interval problem.

Offline Detection, Identification, and Correction of Branch Parameter Errors Based on Several Measurement Snapshots

This paper proposes a three-stage offline approach to detect, identify, and correct series and shunt branch parameter errors. In Stage 1 the branches suspected of having parameter errors are identified through an Identification Index (II). The II of a branch is the ratio between the number of measurements adjacent to that branch, whose normalized residuals are higher than a specified threshold value, and the total number of measurements adjacent to that branch. Using several measurement snapshots, in Stage 2 the suspicious parameters are estimated, in a simultaneous multiple-state-and-parameter estimation, via an augmented state and parameter estimator which increases the V-θ state vector for the inclusion of suspicious parameters. Stage 3 enables the validation of the estimation obtained in Stage 2, and is performed via a conventional weighted least squares estimator. Several simulation results (with IEEE bus systems) have demonstrated the reliability of the proposed approach to deal with single and multiple parameter errors in adjacent and non-adjacent branches, as well as in parallel transmission lines with series compensation. Finally the proposed approach is confirmed on tests performed on the Hydro-Quebec TransEnergie network.

Adaptive Vision-based Missile Guidance in the Presence of Evasive Target Maneuvers

AbstractA nonlinear adaptive visual servo guidance law is presented for a bank-to-turn (BTT) missile airframe that achieves near zero miss distance interception of a target undergoing unknown evasive maneuvers. The controller is developed assuming unknown missile linear velocity and target depth measurements; hence can be regarded as a pure vision-based guidance law. By approximating the unknown, scaled relative velocity via power series expansion, a continuous adaptive parameter update law is developed to compensate for the unknown missile-target relative velocity and the depth measurements. In addition, robust elements are included in the guidance law to compensate for external disturbances and parameter identification errors. A rigorous Lyapunov-based stability analysis is utilized to prove uniformly ultimately bounded (UUB) stability of the system states, and high-fidelity numerical simulation results are provided to verify the performance of the proposed missile guidance law.

Adaptive generalized synchronization between Chen system and a multi-scroll chaotic system

Based on Lyapunov theory, the adaptive generalized synchronization between Chen system and a multi-scroll chaotic system is investigated. According to the form of target function a proper adaptive controller is designed, by which the controlled Chen system can be synchronized with a multi-scroll chaotic system including unknown parameters. The Lyapunov direct method is exploited to prove that the synchronization error and parameter identification error both converge to zero. Numerical simulation results verify the feasibility of the proposed method further.

Digital Calibration of Nonlinear Memory Errors in Sigma–Delta Modulators

A digital adaptive calibration technique to linearize sigma-delta (ΣΔ) modulators based on an output-referred distortion analysis of discrete-time integrators (DTIs) is presented. A sequential power series (a special form of Volterra series) is found sufficient to model the nonlinear memory errors in a DTI, which entails the application of adaptive polynomial transversal filtering for error correction of the modulator. Error-parameter identification is accomplished by correlating various moments of the digital output with a 1-bit pseudorandom noise, which is injected into the analog input of each DTI of the modulator. Simulations demonstrate the effectiveness of the technique in compensating the signal-dependent errors of first- and second-order ΣΔ modulators.

Improvements in Network Parameter Error Identification via Synchronized Phasors

This paper is concerned about the identification of network parameter errors, which are known to bias the state estimation solution. Previously proposed parameter error identification techniques implicitly assume the availability of a set of conventional measurements. In this paper, it is first shown that identification of certain parameter errors based on such conventional measurements, no matter how redundant they are, may not always be possible. The paper then describes the need for phasor measurements to overcome this limitation and presents an identification method that requires the use of such measurements. Examples are also included to describe the application of the proposed method to a number of typical topologies and measurement configurations.

An Entropy-Like Estimator for Robust Parameter Identification

This paper describes the basic ideas behind a novel prediction error parameter identification algorithm exhibiting high robustness with respect to outlying data. Given the low sensitivity to outliers, these can be more easily identified by analysing the residuals of the fit. The devised cost function is inspired by the definition of entropy, although the method in itself does not exploit the stochastic meaning of entropy in its usual sense. After describing the most common alternative approaches for robust identification, the novel method is presented together with numerical examples for validation.

Active Stabilization of a Hybrid Vibration Absorber Subjected to Velocity Feedback Control

A method of actively stabilizing a hybrid vibration absorber driven by a force proportional to the velocity detected at the attachment position of the absorber is investigated. This method can be applied to improve model-independence of robust performance while allowing for multimode controllability for the case where a hybrid vibration absorber is desired as an actuator. Robustness not only against variations in a primary structure to be controlled but also against parameter identification errors in a vibration absorber system is discussed. It is illustrated that although this method transforms the hybrid absorber equation with a spring term into the full active absorber equation, the important advantage of a hybrid absorber over a full active absorber, that is, the power-saving effect near the first resonant frequency of a primary structure can be retained. Application examples are presented also for the vibration isolation and active noise control problems.

An Approach to Network Parameter Estimation in Power System State Estimation

In energy management systems, state estimation is a key function for building a network real-time model. This article describes an approach to network parameter estimation. An appropriate method for network parameter error detection, identification, and correction is developed. The method shown is applicable to single as well as multiple parameter errors. An approximate formula for the perturbed pseudoinverse is developed. To speed up operator's decision-making procedure, a network reduction method and a graphical user interface feature is developed.

Detection of Component Failures for Smart Structure Control Systems

Uncertainties in the dynamics model of a smart structure are often of significance due to model errors caused by parameter identification errors and reduced-order modeling of the system. Design of a model-based Failure Detection and Isolation (FDI) system for smart structures, therefore, needs careful consideration regarding robustness with respect to such model uncertainties. In this paper, we proposes a new method of robust fault detection that is insensitive to the disturbances caused by unknown modeling errors while it is highly sensitive to the component failures. The capability of the robust detection algorithm is examined for the sensor failure of a flexible smart beam control system. It is shown by numerical simulations that the proposed method suppresses the disturbances due to model errors and markedly improves the detection performance.

Optimization with few violated constraints for linear bounded error parameter estimation

In the context of linear constrained optimization, we study the problem of finding an optimal solution satisfying all but k of the given n constraints. A solution is obtained by means of an algorithm of the complexity min{O(n/spl middot/k/sup d/), O(n/spl middot/d/sup k+1/)}, where d is the dimension of the problem. We then use these results to solve the problem of robust identification in the presence of outliers in the setting of bounded error parameter identification. Finally, we show that the estimate obtained converges to the true but unknown parameter in the presence of outliers.

DETECTION OF COMPONENT FAILURES FOR SMART STRUCTURE CONTROL SYSTEMS

Uncertainties in the dynamics model of a smart structure are often of significance due to model errors caused by parameter identification errors and reduced-order modeling of the system. Design of a model-based Failure Detection and Isolation (FDI) system for smart structures, therefore, needs careful consideration regarding robustness with respect to such model uncertainties. In this paper, we proposes a new method of robust fault detection that is insensitive to the disturbances caused by unknown modeling errors while it is highly sensitive to the component failures. The capability of the robust detection algorithm is examined for the sensor failure of a flexible smart beam control system. It is shown by numerical simulations that the proposed method suppresses the disturbances due to model errors and markedly improves the detection performance.

Coordinated Transportation of an Object by Multiple Mobile Robots without Using Force/Torque Sensors.

In this paper, we propose a decentralized control algorithm of multiple mobile robots transporting an object in coordination without using force/torque sensors. In this algorithm, each robot is controlled as if it has a specified impedance dynamics, using the identified parameters of each robot, without using a force/torque sensor. These robots transport a object in coordination using a leader follower type control algorithm. We also consider the effect of parameter identification errors and propose a method to reduce the effect of them. The proposed control algorithm is experimentally applied to two mobile robots, and the experimental results illustrate the validity of the proposed control algorithm.

非構造的な不確かさがある場合のパラメータ同定誤差の上界に関する一考察

非構造的な不確かさがある場合のパラメータ同定誤差の上界に関する一考察

最小二乗評価関数に基づく非構造的な不確かさを考慮したパラメータ推定誤差の上界を用いた入力信号の設計

最小二乗評価関数に基づく非構造的な不確かさを考慮したパラメータ推定誤差の上界を用いた入力信号の設計

Minimization with few violated constraints and its application in set-membership identification

Abstract We study the problem of finding an optimal solution satisfying all but k of the given n constraints. A solution is obtained along with an algorithm of the complexity min{ O(n. k d ), O(n. d k+1 ) }, where d is the dimension of the problem. We then use the results to solve successfully the problem of robust estimation in the presence of outliers in the setting of bounded error parameter identification. It is shown that the obtained estimate converges to the true but unknown parameter even in the presence of unknown outliers.

A Consideration of the Upper Bound of Parameter Identification Error with Unstructured Modelling Uncertainty and Disturbance

When a control system is designed, it is important to make a model which can explain the input-output relation precisely. To identify the model’s parameter as precisely as possible, it is important to make the experimental signal less affected by the disturbance or the unstructured model uncertainty and sufficiently informative. In this report, the upper bound of the estimated parameter error is proposed when the unstructured model uncertainty exists. This upper bound can be used to decide the optimal identification input.