On-line Estimation Algorithm Research Articles

Wind-gust Compensation Algorithm based on High-gain Residual Observer to Control a Quadrotor Aircraft: Real-time Verification Task at Fixed Point

Wind is considered a strong disturbance for quadrotor aircrafts (UAV) when an outdoor task at a fixed point is carried out. The effect of wind produces a distortion on the attitude of the vehicle which is reflected on undesired longitudinal movements. This paper addresses a real-time implementation and design of a robust embedded control-observer based on a type high-gain observer algorithm for on-line estimation and compensation of external disturbances produced by wind gusts on an autonomous quadrotor aircraft. A real-time experimental implementation of embedded Residual High Gain algorithm control is proposed in order to eliminate the effects of real perturbations in the hover position of the UAV. A Lyapunov function was used to practical stability analysis the system. Also numerical simulations were carried out to estimate wind behavior by the use of Drydel mathematical wind model. The main contribution of this work is the implementation of a Residual High Gain Observer in an outdoor real-time experiment in presence of real wind gusts perturbations. The proposed embedded algorithm control improves the stabilization of an UAV in the presence of real wind gusts with average of 8 m/s. The proposed algorithm improved the UAV behavior as shown by the GPS position experimental results, decreasing the wind effect on the translational movement of the aircraft.

Simultaneous Self-Sensing of Displacement and Force for Soft Dielectric Elastomer Actuators

This paper presents a novel self-sensing method for soft actuators based on dielectric elastomer (DE) membranes. The proposed self-sensing scheme permits the reconstruction of both membrane force and displacement during actuation, based on voltage and current measurements only. The simultaneous self-sensing of displacement and force allows one to implement interaction control strategies without the need for additional electro-mechanical transducers. To achieve this goal, an online estimation algorithm based on recursive least squares is implemented to reconstruct the membrane capacitance from voltage and current measurements. Subsequently, mathematical models are developed to relate the capacitance to membrane displacement and force. Several modeling approaches are compared, ranging from physics-based to black box ones (i.e., Hammerstein–Wiener models and neural networks), in order to evaluate which strategy maximizes the estimation accuracy. After discussing the complete self-sensing algorithm, experimental validation is performed on a prototype consisting of a cone DE membrane.

Estimation of Gas Turbine Unmeasured Variables for an Online Monitoring System

Abstract One of the main functions of gas turbine monitoring is to estimate important unmeasured variables, for instance, thrust and power. Existing methods are too complex for an online monitoring system. Moreover, they do not extract diagnostic features from the estimated variables, making them unusable for diagnostics. Two of our previous studies began to address the problem of “light” algorithms for online estimation of unmeasured variables. The first study deals with models for unmeasured thermal boundary conditions of a turbine blade. These models allow an enhanced prediction of blade lifetime and are sufficiently simple to be used online. The second study introduces unmeasured variable deviations and proves their applicability. However, the algorithms developed were dependent on a specific engine and a specific variable. The present paper proposes a universal algorithm to estimate and monitor any unmeasured gas turbine variables. This algorithm is based on simple data-driven models and can be used in online monitoring systems. It is evaluated on real data of two different engines affected by compressor fouling. The results prove that the estimates of unmeasured variables are sufficiently accurate, and the deviations of these variables are good diagnostic features. Thus, the algorithm is ready for practical implementation.

A Family of Gradient Descent Grid Frequency Estimators for the SOGI Filter

This paper applies the adaptive gradient descent method to the second-order generalized integrator (SOGI) filter in order to find an online estimation algorithm for the grid frequency, which leads to the proposal of three possible estimators. One of them is identical to the frequency-locked loop algorithm reported in the literature, which proves that it should be understood as a gradient descent estimation algorithm and not as a “frequency locked loop.” The proposed gradient descent estimators are simple and suitable to be implemented into a digital processor with small computational burden. However, due to the SOGI characteristics, the estimators show to be especially sensitive to subharmonic and dc-offset voltage distortion. These problems are removed adopting a cascaded double SOGI approach, which strongly increases the rejection capability to harmonics and enhances the response to voltage sags. Simulation and experimental results are provided to validate the proposed contribution.

Moving-Horizon Modulating Functions-Based Algorithm for Online Source Estimation in a First-Order Hyperbolic Partial Differential Equation

In this paper, an online estimation algorithm of the source term in a first-order hyperbolic partial differential equation (PDE) is proposed. This equation describes heat transport dynamics in concentrated solar collectors where the source term represents the received energy. This energy depends on the solar irradiance intensity and the collector characteristics affected by the environmental changes. Control strategies are usually used to enhance the efficiency of heat production; however, these strategies often depend on the source term which is highly affected by the external working conditions. Hence, efficient source estimation methods are required. The proposed algorithm is based on modulating functions method (MFM) where a moving-horizon strategy is introduced. Numerical results are provided to illustrate the performance of the proposed estimator in open-and closed-loops.

A user-friendly, low-cost turbidostat with versatile growth rate estimation based on an extended Kalman filter.

For various experimental applications, microbial cultures at defined, constant densities are highly advantageous over simple batch cultures. Due to high costs, however, devices for continuous culture at freely defined densities still experience limited use. We have developed a small-scale turbidostat for research purposes, which is manufactured from inexpensive components and 3D printed parts. A high degree of spatial system integration and a graphical user interface provide user-friendly operability. The used optical density feedback control allows for constant continuous culture at a wide range of densities and offers to vary culture volume and dilution rates without additional parametrization. Further, a recursive algorithm for on-line growth rate estimation has been implemented. The employed Kalman filtering approach based on a very general state model retains the flexibility of the used control type and can be easily adapted to other bioreactor designs. Within several minutes it can converge to robust, accurate growth rate estimates. This is particularly useful for directed evolution experiments or studies on metabolic challenges, as it allows direct monitoring of the population fitness.

Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States.

Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle’s parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle’s roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle’s states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm.

Distributed oscillation damping estimation to large scale power systems: A multi-area online empirical approach

Online extraction and characterization algorithms to the distributed damping factor that can accurately monitoring the transient response in large interconnected power systems has attracted much attention, principally to maximize the multi-area functions in energy control centers. In this paper we present an online estimation algorithm to the distributed damping factor of inter-area electromechanical oscillations in power systems. The method is based on a statistical analysis of multi-area matching patterns (MPs) modes from spatially coupled empirical coefficients, and derived with a network partitioning method by coherent signal blocks into data clusters from the phase domain. This provides a distributed global picture on the modal behavior, which is derived using synchronized-time multi-area signals simultaneously recording from wide-area monitoring schemes (WAMS). For purposes as our multi-area online approach, a sliding data window frame for estimating the distributed damping factor of low-frequency electromechanical oscillations based on Prony and Fourier methods with multiple empirical orthogonal functions (EOFs) analysis is presented. In order to demonstrate the usefulness and adaptivity to obtain an online damping factor estimation and global picture of inter-area electromechanical modes from dynamic oscillations in distributed power systems, the proposed algorithm is applied in detail to the 16 machine-68 bus New England test system.

On the Performance of Online Parameter Estimation Algorithms in Systems with Various Identifiability Properties

In recent years, Bayesian inference has been extensively used for parameter estimation in nonlinear systems; in particular it has proved to be very useful for damage detection purposes. The problem of parameter estimation is inherently correlated with the issue of identifiability, i.e., is one able to learn uniquely the parameters of the system from available measurements? The identifiability properties of the system will govern the complexity of the posterior probability density functions (pdfs), and thus the performance of learning algorithms. Off-line methods such as Markov Chain Monte Carlo methods are known to be able to estimate the true posterior pdf, but can be very slow to converge. In this paper we study the performance of on-line estimation algorithms on systems which exhibit challenging identifiability properties, i.e., systems for which all parameters cannot be uniquely identified from the available measurements, leading to complex, possibly multi-modal posterior pdfs. We show that on-line methods are capable of correctly estimating the posterior pdfs of the parameters, even in challenging cases. We also show that a good trade-off can be obtained between computational time and accuracy by correctly selecting the right algorithm for the problem at hand, thus enabling fast estimation and subsequent decision making.

A Streaming Algorithm for Online Estimation of Temporal and Spatial Extent of Delays

Knowing traffic congestion and its impact on travel time in advance is vital for proactive travel planning as well as advanced traffic management. This paper proposes a streaming algorithm to estimate temporal and spatial extent of delays online which can be deployed with roadside sensors. First, the proposed algorithm uses streaming input from individual sensors to detect a deviation from normal traffic patterns, referred to as anomalies, which is used as an early indication of delay occurrence. Then, a group of consecutive sensors that detect anomalies are used to temporally and spatially estimate extent of delay associated with the detected anomalies. Performance evaluations are conducted using a real-world data set collected by roadside sensors in Bangkok, Thailand, and the NGSIM data set collected in California, USA. Using NGSIM data, it is shown qualitatively that the proposed algorithm can detect consecutive occurrences of shockwaves and estimate their associated delays. Then, using a data set from Thailand, it is shown quantitatively that the proposed algorithm can detect and estimate delays associated with both recurring congestion and incident-induced nonrecurring congestion. The proposed algorithm also outperforms the previously proposed streaming algorithm.

A simple algorithm for amplitude estimation of distorted power system signal

Abstract This paper presents development and implementation of a novel digital signal processing algorithm for on-line estimation of peak value of the fundamental component of a non-sinusoidal power system signal. The algorithm relies on stable band-pass first and second degree digital integrator based rigorous mathematical deduction and recombines itself with zero-crossing avoiding technique. Compared with the well-established technique such as the enhanced phase-locked-loop (EPLL) based system, the proposed algorithm provides higher degree of immunity and insensitivity to harmonics and noise, and faster response. 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. Moreover, a simple laboratory setup implemented by MATLAB and dedicated hardware is built to verify the performance of the proposed algorithm in real time applications. The framework of laboratory prototype has been given out and the necessary modification in proposed algorithm for on-line implementation has been discussed.

Payload Estimation in AC Electric Mining Shovels Using Drive Signals

Shovels play a key role in surface mining operations. Keeping the shovel payload inside the limits defined by manufacturers will reduce mechanical components wear and overheating of electric drive train components. This paper presents and evaluates two online estimation algorithms of shovel payload based exclusively on shovel drive signals. Proposed algorithms use a model reference adaptive system structure based on a 3-degree of freedom (DOF) integrated mechanical-electrical model of an ac drive rope shovel. The developed estimators based on the hoist and swing drives signals are evaluated first using an idealized truck loading cycle, and then using field signals of a production mining shovel, showing fast and accurate cross checked values. Having this information, operators will be capable of operating their equipments inside the safety boundaries and avoid overloading the mining trucks.

Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy.

Functional near-infrared spectroscopy (fNIRS) is a relatively low-cost, portable, noninvasive neuroimaging technique for measuring task-evoked hemodynamic changes in the brain. Because fNIRS can be applied to a wide range of populations, such as children or infants, and under a variety of study conditions, including those involving physical movement, gait, or balance, fNIRS data are often confounded by motion artifacts. Furthermore, the high sampling rate of fNIRS leads to high temporal autocorrelation due to systemic physiology. These two factors can reduce the sensitivity and specificity of detecting hemodynamic changes. In a previous work, we showed that these factors could be mitigated by autoregressive-based prewhitening followed by the application of an iterative reweighted least squares algorithm offline. This current work extends these same ideas to real-time analysis of brain signals by modifying the linear Kalman filter, resulting in an algorithm for online estimation that is robust to systemic physiology and motion artifacts. We evaluated the performance of the proposed method via simulations of evoked hemodynamics that were added to experimental resting-state data, which provided realistic fNIRS noise. Last, we applied the method post hoc to data from a standing balance task. Overall, the new method showed good agreement with the analogous offline algorithm, in which both methods outperformed ordinary least squares methods.

Determination of Paris' law constants and crack length evolution via Extended and Unscented Kalman filter: An application to aircraft fuselage panels

Prediction of fatigue crack length in aircraft fuselage panels is one of the key issues for aircraft structural safety since it helps prevent catastrophic failures. Accurate estimation of crack length propagation is also meaningful for helping develop aircraft maintenance strategies. Paris' law is often used to capture the dynamics of fatigue crack propagation in metallic material. However, uncertainties are often present in the crack growth model, measured crack size and pressure differential in each flight and need to be accounted for accurate prediction. The aim of this paper is to estimate the two unknown Paris' law constants m and C as well as the crack length evolution by taking into account these uncertainties. Due to the nonlinear nature of the Paris' law, we propose here an on-line estimation algorithm based on two widespread nonlinear filtering techniques, Extended Kalman filter (EKF) and Unscented Kalman filter (UKF). The numerical experiments indicate that both EKF and UKF estimated the crack length well and accurately identified the unknown parameters. Although UKF is theoretical superior to EKF, in this Paris' law application EKF is comparable in accuracy to UKF and requires less computational expense.

A Modified RLS Algorithm for Online Estimation of Low-Frequency Oscillations in Power Systems

A number of methods have been proposed and implemented to improve system damping against low-frequency electromechanical oscillations in the power system. Among these, flexible ac transmission systems (FACTS) can be used to provide power oscillation damping (POD) function to the power system. The design of the POD algorithms in these devices requires estimation of the power oscillation frequency components and this is mostly achieved through the use of various filter combinations. However, these filter-based solutions are characterized by low bandwidth to extract the required signal components accurately, and this limits the dynamic performance of the FACTS controllers. Moreover, the filters are designed for specific frequencies, and a change in the system would reduce the performance of the methods. Thus, there is a need for a better estimation algorithm with fast and selective estimation of the required signal that is robust against system parameter uncertainties. In this paper, this is achieved by the use of a recursive least square algorithm that uses variable forgetting factor and a frequency adaptation mechanism. The investigated method has fast estimation in transient conditions without compromising its selectivity in steady state. The effectiveness of the proposed method is proven through simulation as well as experimental verification.

Design of manned lunar rover wheels and improvement in soil mechanics formulas for elastic wheels in consideration of deformation

Most of the current lunar rover vehicle wheels are inconvenient for changing broken wheels and have poor shock absorbing in driving, so they cannot be used to carry people on the moon. To meet the demands for manned lunar transportation, a new wheel possessing a woven metal wire mesh tire and using hub-rim combination slide mechanism is designed in this article. The characteristics of the new wheel is analyzed by comparing with the same-size conventional rover wheels after demonstrating the validity of FEM simulation. The new wheel possesses lighter structure and superior shock absorbing. It also provides stronger traction because the deformation of the designed wheel increases the contact area between the tire and lunar terrain. In order to establish an on-line soil parameter estimation algorithm for low cohesion soil, the stress distribution along a driven deformable wheel on off-road terrain is simplified. The basic mechanics equations of the interaction between the wheel and the lunar soil can be used for analytical analysis. Simulation results show that the soil estimation algorithm can accurately and efficiently identify key soil parameters for loose sand.

Data Estimation with Predictive Switching Mechanism in Wireless Sensor Networks

In wireless sensor networks (WSN), sink node needs to store and track data efficiently in a timely manner. As data is stored in a regular time interval, massive data is being collected which results in poor data tracking at sink node. To overcome this problem, an online data tracking and estimation (ODTE) algorithm is developed. ODTE algorithm captures continuous data and does online data tracking at regular time interval. It also measures distortion factor (DF) which estimates an optimal data collected at sink node. Moreover, data transmission overhead is another major issue at sink node while transmitting continuous data from sensor nodes. To solve this, we develop data prediction systems (DPS) which allows sensor nodes to transmit data to a sink node within a limit, controlled by a switching operation. This make sink node for predicting data for sensor nodes thereby reducing transmission overhead in WSN.We validate ODTE algorithm and prediction models using simulation results.

Variable step‐size non‐negative normalised least‐mean‐square‐type algorithm

This paper proposes a fast and precise adaptive filtering algorithm for online estimation under a non-negativity constraint. A novel variable step-size (VSS) non-negative normalised least-mean-square (NLMS)-type algorithm based on the mean-square deviation (MSD) analysis with a non-negativity constraint is derived. The NLMS-type algorithm under the non-negativity constraint is derived by using the gradient descent of the given cost function and the fixed-point iteration method. Furthermore, the VSS derived by minimising the MSD yields improvement of the filter performance in the aspects of the convergence rate and the steady-state estimation error. Simulation results show that the proposed algorithm outperforms existing algorithms.

A generalized online estimation algorithm of multi-axis contouring errors for CNC machine tools with rotary axes

High-accuracy contouring error estimation is the premise of effective contouring error control. However, two key factors make it complex to estimate contouring error for multi-axis machines with rotary axes: the nonlinear kinematics and the synchronization requirement of the tool tip position and tool orientation. This paper proposes a generalized online estimation algorithm of multi-axis contouring errors for CNC machine tools with rotary axes. The nearest reference tool pose to the actual one is searched at first, and then the corresponding contouring error components on each axis are estimated by using linear ratio estimation, where the tool tip position and tool orientation are naturally synchronized to one same pose on the desired trajectory. The advantage of the proposed contouring error estimation algorithm is that only the interpolated reference poses and drive commands are needed in the calculation, which increases the generality of the algorithm to different trajectory types and machine topologies. On the other hand, the calculation load is reduced when compared with existing iterative multi-axis contouring error estimation approaches. Simulation results on both five- and four-axis machines show that the proposed contouring error estimation algorithm can estimate axis components of contouring errors with high accuracy. Experiment results on an in-house developed five-axis experiment platform verify the effectiveness of the proposed algorithm.

Online updating and uncertainty quantification using nonstationary output-only measurement

Extended Kalman filter (EKF) is widely adopted for state estimation and parametric identification of dynamical systems. In this algorithm, it is required to specify the covariance matrices of the process noise and measurement noise based on prior knowledge. However, improper assignment of these noise covariance matrices leads to unreliable estimation and misleading uncertainty estimation on the system state and model parameters. Furthermore, it may induce diverging estimation. To resolve these problems, we propose a Bayesian probabilistic algorithm for online estimation of the noise parameters which are used to characterize the noise covariance matrices. There are three major appealing features of the proposed approach. First, it resolves the divergence problem in the conventional usage of EKF due to improper choice of the noise covariance matrices. Second, the proposed approach ensures the reliability of the uncertainty quantification. Finally, since the noise parameters are allowed to be time-varying, nonstationary process noise and/or measurement noise are explicitly taken into account. Examples using stationary/nonstationary response of linear/nonlinear time-varying dynamical systems are presented to demonstrate the efficacy of the proposed approach. Furthermore, comparison with the conventional usage of EKF will be provided to reveal the necessity of the proposed approach for reliable model updating and uncertainty quantification.