On-line Estimation Algorithm Research Articles

State of health estimation in composite electrode lithium-ion cells

Electrochemical models of lithium-ion batteries have been increasingly considered for online state of health estimation. These models can more accurately predict cell performance than traditional circuit models and can better relate physical degradation mechanisms to changes in model parameters. However, examples of state of health estimation algorithms that are validated with experimental data are scarce in the literature, particularly for cells with a composite electrode. The individual electrode active materials in a composite electrode may degrade at different rates and according to different physical mechanisms, and online estimation of this degradation facilitates more robust knowledge of how battery performance changes over its life. In this paper we use a reduced-order electrochemical model for a composite LiMn2O4-LiNi1/3Mn1/3 Co1/3O2 (LMO-NMC) electrode cell for online estimation of active material loss. Experimental data collected from composite electrode half cells that were aged under constant current cycling are used in an extended Kalman filter to estimate model parameters associated with loss of each active material. The capacity loss predicted by the online estimates agrees well with the measured capacity loss. Additionally, a differential capacity analysis demonstrates that active materials lose capacity at a similar rate, the same conclusion obtained from the online estimation algorithm.

Star extraction based on clustering within a star tracker

The traditional star extraction methods of star tracker need much processing time and large image memory, resulting in slow attitude acquisition and complex electronic circuits. A new star extraction method, star extraction based on clustering, was developed. The analysis of on-line background estimation algorithm and clustering algorithm in the new method was introduced. The method was realized in one of the novel star trackers developed by Beijing Institute of Control Engineering (BICE), on-board circumlunar return and reentry spacecraft of 3rd phase of China lunar exploration program. According to the house-keeping data packages recorded during on-ground real sky test and the flight mission, the processing time of star extraction was reduced by 84% at least and the image memory was reduced by 99% at least, compared with traditional methods. The results indicated that the star extraction based on clustering had advantages in processing time, memory and migration,which made it possible to realize the method in almost all kinds of optical sensors.

An Item Attribute Specification Method Based On the Likelihood D 2 Statistic

The Q-matrix is a very important component of cognitive diagnostic assessments, and it maps attributes to items. Cognitive diagnostic assessments infer the attribute mastery pattern of respondents based on item responses. In a cognitive diagnostic assessment, item responses are observable, whereas respondents' attribute mastery pattern is potentially, but not immediately observable. The Q-matrix plays the role of a bridge in cognitive diagnostic assessments. Therefore, Q-matrix impacts the reliability and validity of cognitive diagnostic assessments greatly. Research on how the errors of Q-matrix affect parameter estimation and classification accuracy showed that the Q-matrix from experts' definition or experience was easily affected by experts' personal judgment, leading to a misspecified Q-matrix. Thus, it is important to find more objective Q-matrix inference methods.This paper was inspired by Liu, Xu and Ying's(2012) algorithm and the item-data fit statistic G2 in the item response theory framework. Further research on the Q-matrix inference, an online Q-matrix estimation method based on the statistic D2 was proposed in the present study. Those items which are the base of the online algorithm are called as base items, and it is assumed that the base items are correctly pre-specified. The online estimation algorithm can jointly estimate item parameters and item attribute vectors in an incrementally manner.In the simulation studies, we considered the DINA model with different Q-matrix(attribute-number is 3, 4 and5), different sample size(400, 500, 800 and 1000), and different number of correct items(8, 9, 10, 11 and 12) in the initial Q-matrix. The attribute mastery pattern of the sample followed a uniform distribution, and the item parameters followed a uniform distribution with interval [0.05, 0.25].The results indicated that: when the number of base items was not too small, the online estimation algorithm with the D2 statistic could estimate the attribute vectors of rest items one by one, and further improve the estimation by using the joint estimation. When item parameters were unknown, item number was 20, and item attributes was 3, 4 or 5, based on the initial Q-matrix, the online estimation algorithm could recover the true Q-matrix with a high probability even when the number of base items were as small as 8.

Two Modified Unscented Kalman Filter and Acceleration Information in Unmanned Surface Vehicle Estimation

The stability problem is one of the existing problems of unscented Kalman filter (UKF), which due to the nonlinearity and complexity of system. The precision will be decreased or even UKF will be halted when the algorithm can't ensure the state covariance to be positive semidefinite. Based on decomposition of state covariance, the paper first reviews two modified UKFs which can enhance the state covariance to be positive semidefinite, then describes a method which combines the modified UKFs with acceleration measurement together to unmanned surface vehicle (USV) state estimation which main advantage lies in binding UKF with acceleration to simplify the system estimation model, so that it becomes an online estimation algorithm with higher precision and lower calculation complexity.

Online Estimation and Compensation of Friction in Industrial Cable Robot Manipulation

This work deal with on-line estimation and compensation of friction in industrial cable robot manipulation. First, a parametric model is developed for a classical friction model. Then, a recursive least squares on-line friction estimator is presented. Next, a Lyapunov based adaptive controller based on Backstepping scheme is designed to compensate for friction in actuator unit. Finally, we implement the simulation for this design. The simulation results show on-line parameter estimation algorithm and adaptive control scheme have generally achieved desired performance.

Robust model-based fault diagnosis for air handling units

Fault detection and diagnosis (FDD) for heating, ventilation and air conditioning (HVAC) equipment significantly impact energy consumption in both residential and commercial buildings. In most modern building management systems (BMS), HVAC historical data logs in large quantity and high resolution are recorded and available for further online or offline analysis, including automated FDD. In this paper, a model-based fault diagnosis method is developed by applying support vector machine (SVM) techniques to model parameters recursively calculated by an online estimator. The estimator presumes an autoregressive time series model with exogenous variables (ARX). A real-world air handling unit (AHU) dataset containing process variables measured at regular intervals is pre-processed by the online estimation algorithm. Each data vector in the original dataset (measured), together with a small number of appropriately selected lags, is converted to a parameter vector representing the state at the same instant. The set of parameter vectors is sub-divided into classes by SVM, enabling fault classification. Validation via experimental data demonstrates that the proposed hybrid approach produce superior performance measured by F-measure scores compared to alternative methods. Robustness to model uncertainty is also established.

Kalman Filter-Based Robot Manipulator Five-Degrees of Freedom Uncalibrated Vision Positioning

Robot manipulator position and posture control is a popular topic in the field of uncalibrated visual servoing, this paper presents a kalman filter-based robot manipulator five-degrees of freedom uncalibrated vision positioning method. In the case of the fixed binocular cameras and manipulator parameters are unknown; firstly, the specific point and angle image features information in the camera image space were selected in order to describe the relative pose relationship between robot manipulator ends and goals. Then, the kalman filter online estimation algorithm was applied to calculate image Jacobian matrix which is mapping relationship between image space to cartesian mission space, and vision controller was designed in the image plane realized robot manipulator five-degrees of freedom uncalibrated vision positioning control. Finally, Six-degrees of freedom robot manipulator’s five-degrees of freedom uncalibrated visual positioning Simulink model established in the Matlab environment, and the simulation result show that kalman filter online estimation method made the robot manipulator rapid convergence to the desired position and posture with high accuracy.

Online Updating of Rotor Time Constant Based on Combined Voltage and Current Mode Flux Observer for Speed-Sensorless AC Drives

The simultaneous online estimation algorithm of rotor time constant and rotor speed for a speed-sensorless induction machine is proposed. With a small signal injected to the flux command, the method can accurately complete the online tuning of Tr over a wide range of speed estimation, including the rated speed and the very low speed of 1 Hz or even lower speeds under both no-load and loaded operations. Sensitivity of stator resistance error is studied, and the impact of stator resistance error on this method is severely reduced by the closed-loop structure of combined voltage and current (UI) mode flux observer. The identification process only needs the ac component amplitude without considering ac component phase drifts; thus, the division by zero at the zero-crossing point is avoided, which greatly increases the accuracy. Furthermore, any differential integral operations or even a filter is not needed; hence, the computation is rather simple and time saving. Moreover, unlike other signal injection methods, there are no special requirements on the injected signal frequency. The accuracy and validity of the method has been verified by Matrix Laboratory simulation model. Further experimental results on a 2.2-kW 50-Hz 4-pole induction motor also show effectiveness of this proposed method.

The methods and algorithms for online indexes evaluation and estimation of stability, goal, and informational-technological abilities for information system

Now the important problems of structure dynamic control (SDC) theory are the evaluation and estimation of stability, goal, and informational-technological abilities for modern information system (IS). Our investigation shows that this problem can be solved by attainability set construction of SDC models. Preliminary evaluation of attainability sets for the SDC models lets allows to reduce the time (needed) required for solution of IS SDC tasks. Therefore in practice the different approximation of attainability sets are used. Methods and algorithms of evaluation of attainability sets for the SDC models are proposed.

Generalized Correlations for the Estimation of Condensate Power in Flooded Cylinders

Condensate accumulated inside dryer cylinders will produce an increased power demand and eventually the inability of the dryer drive to accelerate the section. In a previous paper, these authors presented an online estimation algorithm capable of detecting the presence of condensate during a section starting. In this paper, a complete set of tests is run over two experimental cylinders covering up to 40% of cylinder volumes, and the complete set of power curves are reported and analyzed. Specifically, the presence of a combined cascading-rimming region in the medium to high condensate loads is confirmed. This combined mode modifies the pattern followed by the maximum power in the low condensate region. Using these data, high-order and simplified correlations are proposed for the estimation of condensate in flooded dryer cylinders. These new correlations increase the capacity of the online estimation algorithm to give good estimations of the condensate load present inside the dryer cylinders up to 55 mm of rim thickness.

Rotor flux oriented control of induction machine based drives with compensation for the variation of all machine parameters

Abstract The performance of rotor flux oriented induction motor drives, widely used these days, relies on the accurate knowledge of key machine parameters. In most industrial drives, the rotor resistance, subject to temperature variations, is estimated on-line due to its significant influence on the control behaviour. However, the rest of the model parameters are also subject to slow variations, determined mainly by the operating point of the machine, compromising the dynamic performance and the accuracy of the torque estimation. This paper presents an improved rotor-resistance on-line estimation algorithm that contemplates the iron losses of the electrical machine, the iron saturation curve and the mechanical losses. In addition, the control also compensates the rest of the key machine parameters such as the leakage and magnetizing inductances and the iron losses. These parameters are measured by an off-line estimation procedure and stored in look up-tables used by the control. The paper begins by presenting the machine model and the proposed rotor flux oriented control strategy. Subsequently, the off-line parameter measurement procedure is described. Finally, the algorithm is extensively evaluated and validated experimentally on a 15 kW test bench

Side-Information-Dependent Correlation Channel Estimation in Hash-Based Distributed Video Coding

In the context of low-cost video encoding, distributed video coding (DVC) has recently emerged as a potential candidate for uplink-oriented applications. This paper builds on a concept of correlation channel (CC) modeling, which expresses the correlation noise as being statistically dependent on the side information (SI). Compared with classical side-information-independent (SII) noise modeling adopted in current DVC solutions, it is theoretically proven that side-information-dependent (SID) modeling improves the Wyner-Ziv coding performance. Anchored in this finding, this paper proposes a novel algorithm for online estimation of the SID CC parameters based on already decoded information. The proposed algorithm enables bit-plane-by-bit-plane successive refinement of the channel estimation leading to progressively improved accuracy. Additionally, the proposed algorithm is included in a novel DVC architecture that employs a competitive hash-based motion estimation technique to generate high-quality SI at the decoder. Experimental results corroborate our theoretical gains and validate the accuracy of the channel estimation algorithm. The performance assessment of the proposed architecture shows remarkable and consistent coding gains over a germane group of state-of-the-art distributed and standard video codecs, even under strenuous conditions, i.e., large groups of pictures and highly irregular motion content.

Analytic and Iterative Algorithms for Online Estimation of Coupling Inductance in Direct Power Control of Three-Phase Active Rectifiers

One of the best known control methods for three-phase active rectifiers is the so-called direct power control (DPC). The control algorithm of the DPC is primarily based on the regulation of instantaneous active and reactive power. Because the DPC method can operate without any grid voltage sensors, instantaneous power and grid phase voltages must be estimated. The value of the essential coupling inductor between the rectifier and its three-phase supply play an important role in these estimations. This value is theoretically known; however, the grid inductance, which is sometimes difficult to determine precisely, or the coupling inductor tolerance, among other factors, may lead to errors in the inductance value. These errors have a negative and considerable effect on the instantaneous power and grid voltage estimation. Therefore, they provoke an improper operation of the DPC algorithm and the rectifier itself. To avoid these drawbacks, two simple, fast and robust inductance online estimation algorithms (analytic and iterative) are proposed in this paper. The proper operation of both methods is shown by simulation and experimental results.

Model-Based Prognostics Under Non-stationary Operating Conditions

The paper presents a novel approach for prognostics of faults in mechanical drives under non-stationary operating conditions. The feature time series is modeled as an output of a dynamical state-space model, where operating conditions are treated as known model inputs. An algorithm for on-line model estimation is adopted to find the optimal model at the current state of failure. This model is then used to determine the presence of the fault and predict the future behavior and remaining useful life of the system. The approach is validated using the experimental data on a single stage gearbox.

Bandpass Second-Degree Digital-Integrator-Based Power System Frequency Estimation Under Nonsinusoidal Conditions

A novel digital signal processing algorithm for online estimation of the fundamental frequency of the distorted power system signals is presented. The basic algorithm relies on the development of an efficient variance reduction algorithm; and design of a new stable bandpass infinite impulse response (IIR) second-degree digital integrator (SDDI) with reduced approximation error. Compared with the well-established technique such as the enhanced-phase-locked-loop (EPLL) system, the proposed algorithm provides the following: 1) higher degree of immunity and insensitivity to harmonics and noise and 2) faster response during step frequency change. Structural simplicity, wide range of application, controls over speed and accuracy, and parameter robustness are other salient features of the method. The only limitation as compared with the EPLL system is its slower transient response during step change in signal magnitude. Based on simulation studies, performances of the proposed algorithm at different operating conditions have been presented, and its accuracy and response time have been compared with the EPLL systems.

A Bregman nonlinear proximal point algorithm for adaptive control

AbstractA nonlinear proximal point algorithm is proposed in the setting of adaptive control. Based on the Bregman generalized distance induced by a convex function, forward non-orthogonal projections provide a more general approach to classic optimization algorithms. In this paper convergence theorems are provided for the application of the Bregman algorithm to the adaptive control of both linear systems and nonlinear control-affine systems. It is shown that the proposed algorithm is particularly suited for adaptive control applications because it outperforms the gradient algorithm for on-line parameter estimation. Simulations on an autonomous underwater vehicle and a robotic manipulator show the benefits of the proposed adaptive control scheme when compared to the classic ones.

Trajectory averaging for stochastic approximation MCMC algorithms

The subject of stochastic approximation was founded by Robbins and Monro [Ann. Math. Statist. 22 (1951) 400--407]. After five decades of continual development, it has developed into an important area in systems control and optimization, and it has also served as a prototype for the development of adaptive algorithms for on-line estimation and control of stochastic systems. Recently, it has been used in statistics with Markov chain Monte Carlo for solving maximum likelihood estimation problems and for general simulation and optimizations. In this paper, we first show that the trajectory averaging estimator is asymptotically efficient for the stochastic approximation MCMC (SAMCMC) algorithm under mild conditions, and then apply this result to the stochastic approximation Monte Carlo algorithm [Liang, Liu and Carroll J. Amer. Statist. Assoc. 102 (2007) 305--320]. The application of the trajectory averaging estimator to other stochastic approximation MCMC algorithms, for example, a stochastic approximation MLE algorithm for missing data problems, is also considered in the paper.

An Ensemble Kalman Filter and Smoother for Satellite Data Assimilation

This paper proposes a methodology for combining satellite images with advection-diffusion models for interpolation and prediction of environmental processes. We propose a dynamic state-space model and an ensemble Kalman filter and smoothing algorithm for on-line and retrospective state estimation. Our approach addresses the high dimensionality, measurement bias, and nonlinearities inherent in satellite data. We apply the method to a sequence of SeaWiFS satellite images in Lake Michigan from March 1998, when a large sediment plume was observed in the images following a major storm event. Using our approach, we combine the images with a sediment transport model to produce maps of sediment concentrations and uncertainties over space and time. We show that our approach improves out-of-sample RMSE by 20%–30% relative to standard approaches. This article has supplementary material online.

Second-degree digital differentiator-based power system frequency estimation under non-sinusoidal conditions

This study presents development and implementation of a novel digital signal processing algorithm for on-line estimation of the fundamental frequency of non-sinusoidal power system signals. The basic algorithm relies on the development of a new variance reduction algorithm and design of an optimised infinite impulse response (IIR) second-degree digital differentiator (SDDD). The design of SDDD consists of obtaining a second-degree integrator from Tick integrator, and then modifying and optimising its transfer function appropriately to attain a stable second-degree differentiator. Compared with the well-established technique such as the enhanced phase-locked-loop (EPLL) system, the proposed algorithm provides faster transient response, higher degree of immunity and insensitivity to harmonics and noise. Structural simplicity, wide range of application and robustness against sampling frequency variation are other salient features of the method. The only limitation as compared with the EPLL system is its slight reduced accuracy (around 3 mHz) under static sinusoidal conditions. Based on simulation studies, performances of the proposed algorithm at different operating conditions have been presented and its accuracy and response time have been compared with the EPLL systems.

Adaptive Residual Useful Life Estimation of a Structural Hotspot

In conventional approaches to life prognosis, damage tolerance and fatigue life predictions are obtained based on assumed structural flaws, regardless of whether they actually occur in service. Consequently, a large degree of conservatism is incorporated into structural designs due to these uncertainties. In a real time environment, keeping track of the damage growth in a complex structural component manually is quite difficult and requires automatic damage state estimation. The current research on structural health monitoring (or on-line damage state estimation) techniques offers condition-based damage state prediction and corresponding residual useful life assessment. The real-time damage state information from an on-line state estimation model can be regularly fed to a predictive model to update the residual useful life estimation in the event of a new prevailing situation. This article discusses the use of an adaptive prognosis procedure, which integrates an on-line state estimation algorithm with an off-line predictive algorithm to estimate the condition-based residual useful life of structural hotspots such as a lug joint.