Real-time State Estimation Research Articles

Macroscopic pedestrian flow simulation using Smoothed Particle Hydrodynamics (SPH)

Macroscopic pedestrian models are theoretically simpler than microscopic models, and they can potentially be solved faster while producing reasonable predictions of crowd dynamics. Therefore, they can be very useful for applications such as large-scale simulation, real-time state estimation and crowd management. However, the numerical methods presently used to solve macroscopic pedestrian models, which are mostly grid-based, have some shortcomings that limit their applicability. More specifically, they usually include complex procedures for grid generation and remeshing, and they produce simulation results that may not be sufficiently accurate (for example, because of unclear boundaries between flow states). Smoothed Particle Hydrodynamics (SPH) constitutes an alternative numerical method that could potentially overcome these limitations. SPH is a meshfree method where a crowd is represented by a set of particles that possess material properties and move according to macroscopic laws. Relevant state variables at each particle are approximated using information about the material properties of the neighboring particles and a smoothing function. This paper puts forward for the first time a generic SPH framework for solving macroscopic pedestrian models; in addition, it demonstrates that an SPH-based simulation model can produce meaningful and accurate results by means of three case studies. The first case study shows that the proposed numerical method can approximate well the analytical solution of a simple macroscopic model applied to a queue-discharge scenario. The second case study demonstrates that the proposed numerical method can potentially reproduce density dispersion (a phenomenon observed in real crowds) more accurately than grid-based methods, due to its meshfree, Lagrangian, and particle-based nature. The third case study highlights the need to reformulate the acceleration equation of the basic macroscopic model in order to reproduce lane formation in bi-directional flows (also an observed phenomenon) using the proposed SPH framework, and this paper presents a solution to do so.

Demand-Oriented Approach to Estimate the Operational Performance of Urban Networks With and Without Traffic Information Provision

This paper addresses the problem of estimating the operational performance of extended urban transport networks under different conditions of traffic information availability. The suggested approach employs a simulation procedure for the real-time estimation of the network state, based on the dynamic assignment of an O-D matrix, which is adjusted by the prevailing traffic conditions. Next, a different procedure is used for the online simulation of the route diversion behavior of users under the influence of route guidance information. Moreover, the approach provides the prediction of the network state by hypothesizing about the adjustment of the travel behavior of users to the new traffic conditions generated by the routing information. The implementation of the approach on a part of a real urban network led to the determination of suitable information updating frequencies for providing the largest improvements in the various performance measures. The results signify the important role of taking into account dynamic changes in the O-D demand size for the plausible evaluation of the impact of information provision on the network performance for real-time traffic operations and transport planning purposes.

Remaining Useful Life Estimation of BLDC Motor Considering Voltage Degradation and Attention-Based Neural Network

Brushless DC motor, also referred to as BLDC motor, has been a widely used electric machine due to its excellent performance over conventional DC motors. Due to complex operating conditions and overloading, several irregularities can take place in a motor. Stator related faults are among the most commonly occurring faults in BLDC motor. With an initial raise in local heating, a fault in the stator can largely reduce motor efficiency and account for the entire system breakdown. In this study, we present a deep learning-based approach to estimate the remaining useful life (RUL) of BLDC motor affected by different stator related faults. To analyze the motor health degradation, we have investigated two types of stator faults namely inter-turn fault (ITF) and winding short-circuit fault (WSC). A generator was coupled with the motor and using an average value rectifier (AVR), generator's output voltage was monitored for the entire lifecycle. A proven neural network for effective sequence modeling, recurrent neural network (RNN) is selected to train the voltage degradation data. For a better estimation of nonlinear trends, long-short term memory (LSTM) with attention mechanism is chosen to make predictions of the motor RUL for both types of faults. The main concern that encourages authors of this paper is the proposed method can be used for the real-time condition monitoring and health state estimation of BLDC motors. Also, the proposed AVR-LSTM method is not affected by environmental influences, making it suitable for diverse operating conditions.

Efficient Hardware Implementation of Nonlinear Moving-horizon State Estimation with Artificial Neural Networks

In this contribution we investigate the application of radial basis functions artificial neural networks embedded in hardware for real-time moving-horizon state estimation. The solution of the optimal moving-horizon state estimation problem may be faced as the mapping from the inputs and outputs of the system to the state estimates according to the system model. This mapping may be solved offline with the optimal formulation and then approximated by any higher order function approximation algorithm, such as the ones from machine learning. An approximate version with radial basis functions neural networks is developed and implemented in a Field Programmable Gate Array (FPGA) showing good results in terms of accuracy and computational time. We show that the state estimate using the approximate version of the moving-horizon algorithm can be run using a laboratory scale kit of approximately 500 kHz for an inverted pendulum at a clock rate of about 110 MHz. The latency to provide an estimate can be further reduced when FPGAs with higher clocks are used as the artificial neural network architecture is inherently parallel.

A Framework for Robust Assimilation of Potentially Malign Third-Party Data, and Its Statistical Meaning

This paper presents a model-based method for fusing data from multiple sensors with a hypothesis-test-based component for rejecting potentially faulty or otherwise malign data. Our framework is based on an extension of the classic particle filter algorithm for real-time state estimation of uncertain systems with nonlinear dynamics with partial and noisy observations. This extension, based on classical statistical theories, utilizes statistical tests against the system's observation model. We discuss the application of the two major statistical testing frameworks, Fisherian significance testing and Neyman-Pearsonian hypothesis testing, to the Monte Carlo and sensor fusion settings. The Monte Carlo Neyman-Pearson test we develop is useful when one has a reliable model of faulty data, while the Fisher one is applicable when one may not have a model of faults, which may occur when dealing with third-party data, like GNSS data of transportation system users. These statistical tests can be combined with a particle filter to obtain a Monte Carlo state estimation scheme that is robust to faulty or outlier data. We present a synthetic freeway traffic state estimation problem where the filters are able to reject simulated faulty GNSS measurements. The fault-model-free Fisher filter, while underperforming the Neyman-Pearson one when the latter has an accurate fault model, outperforms it when the assumed fault model is incorrect.

Measurement and feedback for cooling heavy levitated particles in low-frequency traps

We consider a possible route to ground state cooling of a levitated nanoparticle, magnetically trapped by a strong permanent magnet, using a combination of measurement and feedback. The trap frequency of this system is much lower than those involving trapped ions or nano-mechanical resonators. Minimisation of environmental heating is therefore challenging as it requires control of the system on a timescale comparable to the inverse of the trap frequency. We show that these traps are an excellent platform for performing optimal feedback control via real-time state estimation, for the preparation of motional states with measurable quantum properties.

A fractional-order model-based state estimation approach for lithium-ion battery and ultra-capacitor hybrid power source system considering load trajectory

In recent years, hybrid energy storage systems have been widely used in electric vehicle and smart grid applications. Real-time and robust modeling and state estimation are essential to the reliable and safe operation of the hybrid energy storage system. Although there exists a considerable mass of research on the modeling and state estimation of the lithium-ion batteries, a survey focusing on the remaining discharge time prognostic for the hybrid power source system has not been conducted. To fill this gap, this paper handles the problem of fractional-order modeling and the remaining discharge time prognostic of the lithium-ion battery and ultra-capacitor hybrid energy storage system. First, the fractional-order models for the lithium-ion batteries and ultra-capacitors are presented, where the particle swarm optimization Algorithm with the Chaos theory is employed for parameter identification in the time domain. Second, a Markov load trajectory prediction is proposed for enhancing the reliability and robustness of the remaining discharge time prognostic. Third, the prognostic framework of the hybrid energy storage system is presented based on the Bayesian method. The results with urban dynamometer driving schedule are analyzed and discussed, which indicate that the proposed method has high accuracy and robustness.

Machine Learning-Based Classification of the Health State of Mice Colon in Cancer Study from Confocal Laser Endomicroscopy

In this article, we address the problem of the classification of the health state of the colon’s wall of mice, possibly injured by cancer with machine learning approaches. This problem is essential for translational research on cancer and is a priori challenging since the amount of data is usually limited in all preclinical studies for practical and ethical reasons. Three states considered including cancer, health, and inflammatory on tissues. Fully automated machine learning-based methods are proposed, including deep learning, transfer learning, and shallow learning with SVM. These methods addressed different training strategies corresponding to clinical questions such as the automatic clinical state prediction on unseen data using a pre-trained model, or in an alternative setting, real-time estimation of the clinical state of individual tissue samples during the examination. Experimental results show the best performance of 99.93% correct recognition rate obtained for the second strategy as well as the performance of 98.49% which were achieved for the more difficult first case.

Real-time equality-constrained hybrid state estimation in complex variables

The hybrid power system state estimation problem requires computing the state of the power network using data from both legacy and phasor measurements. Recent research has shown that the normal equations approach in complex variables is computationally advantageous, particularly in the presence of phasor measurement values, and that its software implementation is best suited to modern processors that employ single instruction multiple data (SIMD) processor extensions. The complex normal equations approach is however not ideal for handling zero injection measurements, as it requires their modeling as virtual measurements with high weights. This paper employs Wirtinger calculus for extending the complex normal equations approach to include equality constraints, and contrasts it with two previously published implementations: the normal equations approach in complex variables and the hybrid equality constrained state estimator in real variables. Numerical results are reported on transmission networks having up to 9241 nodes; they show that the complex variable equality constrained hybrid state estimator exhibits superior performance as compared to the above two techniques in terms of both computational time and accuracy. Moreover, the execution time on the largest network is less than 300 ms, which makes the proposed implementation commensurate with the requirements of real-time applications.

A New Adaptive System for Suppression of Transient Wind Disturbance in Large Antennas

Under transient wind disturbance, vibration deformation of the large antenna surface profile causes deterioration of pointing performance. This paper presents a new adaptive system to suppress unknown transient wind disturbance. First, to monitor the vibration, based on the acceleration measurement and a low-order flexible model considering equivalent identification of forces, the real-time estimation of the vibration state is obtained in an unbiased minimum-variance way. Next, a novel four-cable-actuator mechanism with a circular slide track is proposed for suppressing the vibration, in which the locations of the cable drivers on the slide track are determined according to the attitude of the antenna, and the expected tension distribution of the cables is found by the vibration state and the optimal gain of the linear quadratic regulator (LQR). In the end, the simulation implementation of a 7.3 m antenna under the transient wind disturbance is used to verify the effectiveness of the proposed method.

Robust state and fault estimation for non‐linear stochastic systems with unknown disturbances: a multi‐step delayed solution

A study on the simultaneous state and fault estimation for non-linear discrete-time stochastic systems subjected to unknown disturbances is presented. The augmented system approach, system reformation using the state-dependent coefficient (SDC) factorisation, and unknown input filtering method are integrated to simultaneously estimate the state of the system and actuator and/or sensor faults. To achieve this aim, the non-linear system with faults and unknown disturbances is first transformed into an equivalent augmented system by using delayed measurements and the SDC factorisation. Next, within the SDC factorised linear-like augmented system and inspired by the robust two-stage Kalman filter, a novel multi-step estimator named as the robust simultaneous state and fault estimator is proposed to yield a robust state and fault estimation with a multi-step delay. Moreover, a novel real-time state estimator is designed based on the proposed hybrid fault reconstruction model in order to address the inherent time-delay problem. A comparison of the performance of the proposed filters with those of existing methods from the literature is demonstrated using a non-linear two-link manipulator system with friction forces acting simultaneously at each joint.

Reliable State Estimation of an Unmanned Aerial Vehicle Over a Distributed Wireless IoT Network

Unmanned aerial vehicles (UAVs) have attracted a lot of attention due to their enormous potentiality in civil and military applications over the past years. In order to allow accurate control action of UAV, a robust and real-time state estimation technique is required. In this paper, we propose a Kalman filter based UAV state estimation technique when the communication takes place over wireless links in an Internet of Things (IoT) network. We consider that a set of sensors observes the state of the UAV and transmits the observation to a control center (central server) over a distributed wireless IoT network. To deal with the communication impairments due to wireless communication links between the UAV's sensors and the IoT system components, e.g., IoT gateways, a Bose–Chaudhuri–Hocquenghem coded communication system is presented. Based on the received signals at the IoT gateways, a global state estimation technique is proposed. Performance of the proposed communication and estimation scheme is demonstrated through numerical results for different conditions. From the comparison with a conventional estimation scheme, it is observed that the proposed scheme significantly outperforms the conventional scheme in terms of state estimation and error performance.

Real-Time Water Quality Modeling with Ensemble Kalman Filter for State and Parameter Estimation in Water Distribution Networks

This study presents a novel approach to real-time water quality state (chlorine concentration) and reaction parameter estimation in water distribution systems (WDSs) using ensemble Kalman filter (EnKF)-based data assimilation techniques. Two different types of EnKF-based methods are used in this study: noniterative restart-EnKF (NIR-EnKF) and iterative restart-EnKF (IR-EnKF). The use of these data assimilation frameworks for addressing key uncertainties in water quality models, such as uncertainty in the source or initial concentration of chlorine and uncertainty in the wall reaction parameter, is studied. The effect of ensemble size, number and location of measurement nodes, measurement error, and noise are also studied extensively in this work. The performance of the proposed methodology is tested on two different water networks: a brushy plains network and a large, citywide WDS, the Bangalore inflow network. The results of the simulation study show that both the NIR-EnKF and IR-EnKF methods are appropriate for dealing with uncertainty in source chlorine concentration, but the IR-EnKF method performs better than the NIR-EnKF method in the case of reaction parameter uncertainty.

SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm

In order to improve the convergence time and stabilization accuracy of the real-time state estimation of the power batteries for electric vehicles, a fuzzy unscented Kalman filtering algorithm (F-UKF) of a new type is proposed in this paper, with an improved second-order resistor-capacitor (RC) equivalent circuit model established and an online parameter identification used by Bayes. Ohmic resistance is treated as a battery state of health (SOH) characteristic parameter, F-UKF algorithms are used for the joint estimation of battery state of charge (SOC) and SOH. The experimental data obtained from the ITS5300-based battery test platform are adopted for the simulation verification under discharge conditions with constant-current pulses and urban dynamometer driving schedule (UDDS) conditions in the MATLAB environment. The experimental results show that the F-UKF algorithm is insensitive to the initial value of the SOC under discharge conditions with constant-current pulses, and the SOC and SOH estimation accuracy under UDDS conditions reaches 1.76% and 1.61%, respectively, with the corresponding convergence time of 120 and 140 s, which proves the superiority of the joint estimation algorithm.

Exploiting data in smart factories: real-time state estimation and model improvement in metal forming mass production

Modern production systems have numerous sensors that produce large amounts of data. This data can be exploited in many ways, from providing insight into the manufacturing process to facilitating automated decision making. These opportunities are still underexploited in the metal forming industry, due to the complexity of these processes. In this work, a probabilistic framework is proposed for simultaneous model improvement and state estimation in metal forming mass production. Recursive Bayesian estimation is used to simultaneously track the evolution of process state and to estimate the deviation between the physics-based model and the real process. A sheet bending mass production process is used to test the proposed framework. A metamodel of the process is built using proper orthogonal decomposition and radial basis function interpolation. The model is extended with a deviation model in order to account for the difference between model and real process. Particle filtering is used to track the state evolution and to estimate the deviation model parameters simultaneously. The approach is tested and analysed using a large number of simulations, based on pseudo-data obtained from a numerical sheet bending model.

Model-based real-time plasma electron density profile estimation and control on ASDEX Upgrade and TCV

Real-time plasma electron density profile estimation and control are essential in the operation of future tokamaks. In particular, the robustness against diagnostics failure and disturbances is important for long pulse operation.A model-based approach to profile estimation is implemented on the control systems of ASDEX Upgrade and TCV, which is able to merge information from various diagnostics for both core and edge density, as well as systematically handling diagnostic failure. The model used for profile estimation is employed to tune a feedback controller before an experiment, thereby reducing the experimental time required for tuning.Subsequently, this observer and controller have been employed in scientific experiments on ASDEX Upgrade and TCV. On ASDEX Upgrade, the density profile estimator was used in high-density pellet-fuelled discharges, providing a more reliable real-time estimate of the core density for feedback control than previously achieved. On TCV, in experiments on integrated pressure and safety factor profile control, the density profile estimator and feedback controller provide a constant density despite disturbances from time-varying ECCD power. Additionally, the real-time density profiles provide an essential input for other real-time plasma state estimation codes including Electron Cyclotron ray tracing codes, contributing to a complete real-time estimation of the entire plasma state.

Resilient and Fast State Estimation for Energy Internet: A Data-Based Approach

Like the Internet in the computer industry that shifted from a centralized mainframe to a client-based structure, “energy Internet” was proposed to involve the customers and incorporate renewable energy. To empower such bidirectional communication and power management, the power grid deploys massive smart meters and wireless communication techniques. In this context, false data attacks are more likely to occur in an energy Internet system, and considerable imprecision could be caused for the state estimator. In this paper, resilient and real-time state estimation is proposed. First, we investigate the mechanism of the stealthy false data attack and the countermeasures. Then, based on the accurate and reliable pseudovoltage measurements, a changed measurements-based sequential state estimation is introduced. Finally, both simulation results and practical implementation demonstrate the efficacy and efficiency of the proposed state estimation method for the energy Internet.

Event-Trigger Heterogeneous Nonlinear Filter for Wide-Area Measurement Systems in Power Grid

The development of distributed generation raises high requirement on accurate real-time state estimation with phasor measurement unit (PMU) for wide-area measurement systems (WAMSs). Although the particle filter is among the best in estimation performance, its computation burden is truly heavy toward smart sensors of current generation. Besides, the infrastructure for PMU data communication is confronted with the challenges of the amount of data boosted by the high sampling frequency of PMUs and booming grid size. This paper proposes an event-trigger heterogeneous nonlinear filter (ET-HNF) to solve these problems. A master–slave nonlinear filtering structure is designed for WAMS by taking full advantage of computation power both at generator node and estimation center. The local slave filter is established at generator node to perform the event-trigger strategy based on low computational unscented transformation. Moreover, a Monte Carlo-based filter served as the master, which improves estimation center’s filtering performance by cooperating with the center slave. Simulation results verify the feasibility and performance of ET-HNF using the standard IEEE 39-bus system with PMUs.

Real-time Estimation of Urban Rail Transit Passenger Flow Status Based on Multi-source Data

The AFC data uploaded in real time for urban rail transit is incomplete and delayed. In order to improve the dynamic management and control level of rail transit, this paper describes the real-time state estimation process of passenger flow and its key issues, and analyses the relationship between real-time uploaded AFC data and mobile phone signal data, establishing a multi-source data fusion model based on gradient descent method; On this basis, combined with the multi-source data after fusion as the basis of real-time estimation, a realtime passenger flow estimation model based on Kalman filter is established. Practice shows that the fusion of multi-source data improves the accuracy of estimating basic data, the error of the real-time passenger flow estimation model based on Kalman filter is less than 10%, and the accuracy of the estimation model is good. The needs of the urban rail transit operation management department to grasp the real-time passenger flow distribution status of the network are met.

Adaptive Extended Kalman Filter with Correntropy Loss for Robust Power System State Estimation.

Monitoring the current operation status of the power system plays an essential role in the enhancement of the power grid for future requirements. Therefore, the real-time state estimation (SE) of the power system has been of widely-held concern. The Kalman filter is an outstanding method for the SE, and the noise in the system is generally assumed to be Gaussian noise. In the actual power system however, these measurements are usually disturbed by non-Gaussian noises in practice. Furthermore, it is hard to get the statistics of the state noise and measurement noise. As a result, a novel adaptive extended Kalman filter with correntropy loss is proposed and applied for power system SE in this paper. Firstly, correntropy is used to improve the robustness of the EKF algorithm in the presence of non-Gaussian noises and outliers. In addition, an adaptive update mechanism of the covariance matrixes of the measurement and process noises is introduced into the EKF with correntropy loss to enhance the accuracy of the algorithm. Extensive simulations are carried out on IEEE 14-bus and IEEE 30-bus test systems to verify the feasibility and robustness of the proposed algorithm.