Robust Filter Research Articles

A robust fusion bus frequency estimation method to improve frequency oscillation damping in power systems

AbstractThis paper proposes a new robust method for accurately and reliably estimating remote bus frequencies in a power system. Two different measurement sources for the remote bus frequency are considered, that is, the ideal Frequency Divider (FD) and the Synchronous Reference Frame Phase Locked Loops (SRF‐PLLs). Each measurement signal encounters different uncertainties and data quality issues. In this paper, both data sources are employed and fused together to better estimate the remote bus frequencies. To this end, the model structure of the power system is selected and an Unscented Kalman Filter (UKF) is utilized together with a fusion covariance intersection method to enhance the accuracy of the estimated bus frequency. Since the fusion estimation method fails in case of quality issues on both channels, a robust Generalized Maximum‐likelihood UKF (GM‐UKF) using a novel outlier detection criteria is developed. The impact of the resulting robust fusion filter on the estimation of the remote bus frequencies and on the performance of WAPSS, which makes use of the estimated frequencies as the feedback signal, is examined via simulations. The results demonstrate the excellent performance and reliability of the proposed method in dealing with noise filtering and outlier suppression while ensuring a high statistical efficiency.

Multi-Sensor Fusion Target Tracking Based on Maximum Mixture Correntropy in Non-Gaussian Noise Environments with Doppler Measurements

This paper addresses the multi-sensor fusion target tracking problem based on maximum mixture correntropy in non-Gaussian noise environments exclusively using Doppler measurements. As Doppler measurements are non-linear, a statistical linear regression model is constructed using the unscented transformation. Then, a centralized measurement model is developed, and the mixture correntropy is determined, which contains the high-order statistics of state prediction and the measurement error caused by noise. Then, a robust fusion filter is proposed by maximizing the mixture-correntropy-based cost. To improve numerical stability, the information filter and corresponding square root version are also derived. Furthermore, the performance of the proposed algorithm is analyzed, and the selection of the kernel width is discussed. Experiments are performed using simulated data and automatic driving software. The results show that the estimation performance of the proposed algorithm is better with respect to outliers and mixture Gaussian noise than that of traditional methods.

A novel variational robust filter with Gaussian mixture model for unknown non-Gaussian noises

In numerous practical applications, the presence of unpredictable abnormal behaviors often leads to random measurement outliers, causing the random measurement noises are no longer Gaussian but unknown non-Gaussian distributions, and it is intractable to obtain accurate a priori information about the noises. To address this challenge and enable nonlinear state estimation under such measurement noises, we propose a novel robust filter based on Gaussian mixture model (GMM). Firstly, a GMM is introduced to model the unknown non-Gaussian measurement likelihood probability density function (ML-PDF). Without relying on any prior noise statistics, the GMM can accurately model the ML-PDF utilizing a set of model parameters adaptively learned by the variational Bayesian (VB) method. Then, considering the high computational burden associated with the existing variational iterative process, we develop an improved VB method, which independently calculates the posterior distributions of state and unknown model parameters, thereby improving the computational efficiency. Finally, a new robust filter is derived by integrating the GMM with the improved VB approach, The effectiveness and superiority of the filter are demonstrated through both numerical simulations and real-world data analysis.

A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises.

The features of measurement and process noise are directly related to the optimal performance of the cubature Kalman filter. The maneuvering target model's high level of uncertainty and non-Gaussian mean noise are typical issues that the radar tracking system must deal with, making it impossible to obtain the appropriate estimation. How to strike a compromise between high robustness and estimation accuracy while designing filters has always been challenging. The H-infinity filter is a widely used robust algorithm. Based on the H-infinity cubature Kalman filter (HCKF), a novel adaptive robust cubature Kalman filter (ARCKF) is suggested in this paper. There are two adaptable components in the algorithm. First, an adaptive fading factor addresses the model uncertainty issue brought on by the target's maneuvering turn. Second, an improved Sage-Husa estimation based on the Mahalanobis distance (MD) is suggested to estimate the measurement noise covariance matrix adaptively. The new approach significantly increases the robustness and estimation precision of the HCKF. According to the simulation results, the suggested algorithm is more effective than the conventional HCKF at handling system model errors and abnormal observations.

Manipulation of quantum spin transport of TM (Mn, Fe, Co, and Ni) (II) phthalocyanines coupled to carbon nanotube leads applicable in spintronic devices

We conducted a theoretical scrutiny on the quantum spin transport characteristics of a class of open-shell 3d-transition metal (TM = Mn, Fe, Co, and Ni) (II) phthalocyanines (TMPcs) molecules sandwiched between two semi-infinite armchair single-walled carbon nanotube probes. Herein, the spin density functional formalism is utilized in conjunction with state-of-the-art non-equilibrium Green's function technique. Molecular junctions composed of MnPc and FePc have higher local magnetic spin moments than CoPc and NiPc because of the strong exchange spin-splitting between the 3d-states of the Mn or Fe cation atoms and the p-states of the N anion atom. This indicates that TMPc molecules (TM = Mn, and Fe) bridged between two single-walled carbon nanotube (SWCNT) electrodes with a π-type geometry demonstrate a pronounced transmission near the Fermi level. Furthermore, the sign and robustness of spin filter efficiency are tailored by manipulating the central atom of 3d-transition metal and the contact SWCNT electrodes. Subsequently, FePc and MnPc molecular junctions have the advantage of operating practically as ideal spin filter devices compared to CoPc and NiPc molecular structures. Moreover, the tunneling magnetoresistance (TMR) of these molecular devices is assessed. For MnPc and FePc molecular junctions, the estimated total conductance exhibited a robust trend at zero bias voltage, whereas the magnitudes of spin-up and spin-dn quantum conductances increased significantly in CoPc and NiPc molecular configurations with bias voltages range between ±0.45 and ±0.95 Volt. Spin-down carriers essentially govern the quantum conductance of TMPc (Mn, Fe, Co, and Ni) molecular junctions at bias voltage around ±0.75 Volt. By examining their I-V characteristics, it has been established that MnPc, and FePc molecular junctions exhibit metal-like behavior, whereas CoPc and NiPc molecular devices are subject to negative differential resistance. As a result of our theoretical findings, it is found that TMPcs (TM = Fe, Mn) coupled to SWCNT probes yield a robust spin filter efficiency that may be of interest for prospective technological realizations in organic-inorganic molecular spintronic devices.

Robust weighted fusion Kalman filters under linearly correlated noise and mixed uncertainties of noise variances, multiplicative noises, and multiple networked inducements

AbstractThe paper solves the robust weighted fusion Kalman filtering problem for systems with linearly correlated noise and mixed uncertainties of noise variances, multiplicative noises, and multiple networked inducements including missing measurements, packets dropouts, and two‐step random measurement delays. It is assumed that system noise variances are uncertain but bounded above, and the other four uncertainties are compensated to fictitious white noise by the proposed model‐transformation method. For the transformed local multimodel system with correlated fictitious noise, the robust local time‐varying recursive Kalman filters are presented by decorrelation technique and minimax robust filtering principle. Then the six weighted fusion robust Kalman filters are presented in a unified form. The robustness of local and fused robust Kalman filters is proved by the extended Lyapunov equation approach, matrix factorization, and elementary transformation. Further, the local and fused steady‐state robust Kalman filters are designed. Finally, a simulation study applied to F404 aircraft engine system is provided to examine effectiveness and applicability of the proposed algorithm.

An Improved Multiple-Outlier Robust Filter Based on Maximum Correntropy Criterion for Integrated Navigation

In the integrated navigation of global navigation satellite system (GNSS) and Inertial navigation system (INS), the Kalman filter is frequently employed for state estimation. GNSS signals are subject to interference from the outside environment, and it is difficult for the classical Kalman filter to handle observations with outliers. Numerous robust filters have been proposed based on the maximum correntropy criterion (MCC), which is insensitive to non-Gaussian noise, to address the issue of state estimation in the presence of outliers. However, a fixed prior covariance will restrict the use of a maximum correntropy criterion Kalman filter (MCKF) in integrated navigation systems because GNSS signals can be affected by both time-varying noise and outliers. Existing filters consider the prior measurement noise covariance matrix (MNCM) as a deterministic matrix and can only amplify signals unidirectionally during iteration cycles. The weight of effective observations is reduced despite these filters having strong robustness. To overcome this shortcoming, this paper proposes a τ -based maximum correntropy criterion Kalman filter (τ -MCKF). A strategy based on the Chisquare test is designed to optimize the covariance of GNSS observations. By introducing τ to bi-directionally adjust the initial MNCM, τ -MCKF makes full use of effective observations. Then, the τ -corrected MNCM is used to weigh different elements of the innovation vector, and a robust estimation is calculated based on the multiple-outlier robust Kalman filter (MORKF) framework. The GNSS/INS integrated navigation system is taken as an example to verify the effectiveness of the filter through simulation and vehicular experiments.

Autonomous Asteroid Characterization Through Nanosatellite Swarming

This paper first defines a class of estimation problem called simultaneous navigation and characterization (SNAC), which is a superset of simultaneous localization and mapping (SLAM). A SNAC framework is then developed for the Autonomous Nanosatellite Swarming (ANS) mission concept to autonomously navigate about and characterize an asteroid including the asteroid gravity field, rotational motion, and 3D shape. The ANS SNAC framework consists of three modules: 1) multi-agent optical landmark tracking and 3D point reconstruction using stereovision, 2) state estimation through a computationally efficient and robust unscented Kalman filter, and 3) reconstruction of an asteroid spherical harmonic shape model by leveraging a priori knowledge of the shape properties of celestial bodies. Despite significant interest in asteroids, there are several limitations to current asteroid rendezvous mission concepts. First, completed missions heavily rely on human oversight and Earth-based resources. Second, proposed solutions to increase autonomy make oversimplifying assumptions about state knowledge and information processing. Third, asteroid mission concepts often opt for high size, weight, power, and cost (SWaP-C) avionics for environmental measurements. Finally, such missions often utilize a single spacecraft, neglecting the benefits of distributed space systems. In contrast, ANS is composed of multiple autonomous nanosatellites equipped with low SWaP-C avionics. The ANS SNAC framework is validated through a numerical simulation of three spacecraft orbiting asteroid 433 Eros. The simulation results demonstrate that the proposed architecture provides autonomous and accurate SNAC in a safe manner without an a priori shape model and using only low SWaP-C avionics.

A Robust Parameter Identification Approach with Anti-Outlier Characteristics for Lithium-Ion Batteries

Accurate acquisition of model parameters of lithium-ion batteries (LIBs) is imperative for precious estimation state of charge. However, due to the interference of noise, the inevitable uncertainty may lead to outliers appearing in measurement data, which often leads to inaccurate parameter identification. To solve the problem, a robust Kalman filter (RKF) that uses the Mahalanobis Distance Criterion (MDC) is proposed for the parameter identification of LIBs. Firstly, the one-order equivalent circuit model of the LIBs is created. Subsequently, the gain of robust factor is implemented into the Kalman filter to decrease filter gain and swell the measurement noise covariance when facing the outlier measurements. Finally, the dynamic stress test condition of the electric vehicle is used to prove the effectiveness of the RKF-MDC approach. The simulation reveals that the RKF-MDC can reduce the influence of outlier values for the parameter identification of LIBs.

Failure Risk Assessment of Grid-Connected Inverter With Parametric Uncertainty in LCL Filter

Grid-connected inverter failure may be caused by outliers of a population of units in field operation, due to the LCL filter parameter uncertainty. This paper proposes a probabilistic framework to analyze the impact of multiple filter parameter variances due to component tolerances, operating points, and aging effects. Both robust stability and filter performance are selected as the risk of failure. The robust stability of the grid-connected inverter is quantified by the structured singular value (SSV). The total harmonic distortion (THD) of the grid current is estimated to evaluate the filter performance. A case study on a three-phase inverter is performed to verify the proposed method through Hardware-in-the-Loop (HIL) simulation and physical prototype testing.

Robust federated deep reinforcement learning for optimal control in multiple virtual power plants with electric vehicles

The deployment of virtual power plants (VPPs) with electric vehicles (EVs) is crucial for the successful integration of renewable energy sources and efficient management of EV charging and discharging while maintaining sustainability and cost-effectiveness. Deep reinforcement learning (DRL) is a highly promising method that uses historical data to learn optimal control strategies and adapts to a wide range of real-time scenarios. To address data privacy concerns in VPPs, federated DRL, which trains models across multiple VPPs, is necessary. However, existing federated DRL methods are prone to disturbance, which can severely impact system performance. This paper proposes a robust federated DRL method to ensure the robustness and reliability of VPP control strategies. Firstly, we formulate the control strategies of multiple VPPs as a Markov decision process that takes into account disturbances, aiming to achieve self-balance as much as possible. Secondly, we employ the stochastically controlled stochastic gradient method to increase training speed. Additionally, we introduce the robust gradient filter to develop a robust federated DRL method based on policy-based DRL. Finally, we validate the effectiveness and robustness of the proposed robust federated DRL method, which maintains balance in internal VPP power.

A Pearson-Type VII Distribution With Adaptive Parameters Selection-Based Interacting Multiple Model Kalman Filter

Existing robust filters under generalized nonstationary noises conditions are difficult to choose suitable prior parameters, this brief proposed a Pearson-type VII distribution with adaptive parameters selection based interacting multiple model (IMM) Kalman filter (IMMPVII KF). In the model conditional filtering process, both the one-step prediction and the measurement likelihood are modeled as Pearson-type VII distributions. They are decomposed into Gaussian-Gamma Hierarchies (GGH), which are then matched to the time-varying heavy-tailed properties of the noises by pre-selecting the sets of shape and rate parameters and the variational Bayesian (VB) technique. Finally, a new model probability update method for filter under non-Gaussian conditions is derived. Simulation results show that the filter proposed in this brief has better robustness and adaptability to generalized non-stationary noises than the existing filters.

A data association algorithm for the robust confidence ellipsoid filter

In this paper, we propose an algorithm to deal with the data association problem of the robust confidence ellipsoid filter. First, at every time instant, for each target, we need to get a validation gate based on the confidence ellipsoid of the previous time instant. Herein, the validation gate is the minimum volume ellipsoid which guarantees that the true measurement of the target at the current time instant must be contained within it. To obtain the validation gate, an optimization problem is derived from the uncertain dynamic system and it is further converted into a semidefinite programming problem. Then, we make use of the measurements in the validation gate (candidate measurements) and the confidence ellipsoid of the previous time instant to gain the confidence ellipsoid of the current time instant. Similar to the process of obtaining the validation gate, the confidence ellipsoid is obtained by solving a semidefinite programming problem. The ellipsoid has the minimum volume and simultaneously, ensures that the true state vector is contained in it. Next, we propose a strategy to deal with the case where there is no measurement in the validation gate (missing measurement). Last, the numerical examples show the verifications of the proposed algorithm.

A novel robust iterated CKF for GNSS/SINS integrated navigation applications

In challenging circumstances, the estimation performance of integrated navigation parameters for tightly coupled GNSS/SINS is impacted by outlier measurements. An effective solution that employs a novel iterative sigma-point structure with a modified robustness optimization approach for enhancing the error compensation effectiveness and robustness of filters utilized in GNSS challenge conditions is proposed in this paper. The proposed method modifies the CKF scheme by incorporating nonlinear regression and numerous iteration processes for ameliorating error compensation. Subsequently, a loss function and penalty mechanism are implemented to enhance the filter's robustness to outlier measurements. Furthermore, to fully incorporate valid information of the innovation and speed up the operation of the proposed method, the outlier measurement detection criteria are established to bypass the penalty mechanism against measurement weights in the absence of outliers in GNSS measurements. Field experiments demonstrate that the proposed method outperforms traditional methods in mitigating navigation errors, particularly when multipath errors and non-line-of-sight (NLOS) reception are increased.

Power Quality Improvement using a New DPC Switching Table for a Three-Phase SAPF

This research focuses on the analysis and design of robust direct power control (DPC) for a shunt active power filter (SAPF). The study proposes a novel switching table design based on an analysis of the impact of inverter switching vectors on the derivatives of instantaneous reactive and active powers. The goal is to reduce the number of commutations by eliminating null vectors while maintaining the desired DC-bus voltage using a PI regulator-based anti windup technique. Additionally, a robust PLL structure-based band pass multivariate filter (BPMVF) is utilized to enhance the network voltage. The research demonstrates the effectiveness of the suggested power control through extensive simulation results, showing high performance in both transient and steady-state conditions. The proposed approach offers the advantages of sinusoidal network current, and unitary power factor, and eliminates the need for current regulators and coordinate transformations or PWM generators. Further research directions could explore the practical implementation and real-world performance of this technique in power systems.

Distributed fusion robust filter for multisensor networked singular control system with uncertain variances and missing measurement

AbstractA distributed fusion time‐varying robust filtering algorithm for multisensor networked singular control system is studied in this paper, mainly aiming at the system with missing measurement and uncertain variances in practical application. First, the singular value decomposition (SVD) method and de‐randomization method are used to transform the singular control system into a standard system only with uncertain variances. Based on the new standard system, a minimax robust local Kalman filter is proposed by using the minimax robust estimation principle, which ensures that the actual filtering error variance has the corresponding minimum upper bound for all allowable uncertainties. Further, the robust local and distributed fusion filters for original singular control systems are proposed, and the actual and conservative filtering error variances are obtained. Their robustness is proved by Lyapunov equation method and nonnegative definite matrix decomposition method. A simulation example of two‐loop system verifies the effectiveness of the proposed results.

Oxygen excess ratio control of PEM fuel cell: fractional order modeling and fractional filter IMC-PID control

Abstract Concerning global warming, an energy-efficient power source must produce low or no pollutant emissions and provide an unlimited fuel supply. Proton Exchange Membrane fuel cell (PEMFC) is an electrochemical device that transforms chemical energy into electrical energy. The performance and durability of PEM fuel cells are affected by voltage reversals and fuel starvation. Oxygen Excess Ratio (OER) is a crucial factor in controlling the fuel starvation of the PEMFC system. First, this work identified the PEMFC as an integer order and fractional-order first order plus time delay models using the predictor error method and Grunwald–Letnikov simulation method based on a trust-region-reflect algorithm, respectively. Fractional order models more accurately represented the PEM fuel cell system dynamics. Then, robust fractional filters cascaded with PID controllers based on the Internal Model Control scheme (IMC) are designed for identified integer and fractional order models to regulate the OER by compressor voltage manipulation. The genetic Algorithm (GA) optimization technique is used to find the optimal fractional filter tuning parameters. The proposed controller’s performance regarding Integral Absolute Error (IAE) and Total Variance (TV) is analyzed. Furthermore, the robustness of a perturbed plant and fragility with perturbed controllers are elucidated. The results show that a fractional filter cascaded with fractional order PID controller improves the performance compared to a fractional filter cascaded with integer order PID controllers.

A positioning algorithm based on improved robust extended Kalman filter with NLOS identification and mitigation

With the development of the information age and the maturity of Internet of Things technology, wireless sensor network has been widely applied in indoor localization. However, the non-line-of-sight (NLOS) propagation in complicated environment and the inherent noise of the sensor will introduce errors in the measurements, which will seriously lead to inaccurate positioning. In this paper, a novel localization scheme based on the mean reconstruction method is proposed, which reconstructs the distance measurements from all beacon nodes by taking the average twice to weaken the adverse effects of NLOS. At the same time, the noise average is re-estimated when the distance difference is not too large. Next, the robust extended Kalman filter (REKF) is used to process the reconstructed distance measurements to obtain positioning results. To make the positioning results more accurate, hypothesis test is used as NLOS identification to classify the position estimates generated from all distance combinations by least-squares. Then, the residual weighting (RWGH) method is utilized to combine the position estimates that fall into the validation region. At last, we merge the results from RWGH and REKF. The simulation and experimental results show that the proposed algorithm has high positioning accuracy and strong positioning robustness.

Robust filtering for spacecraft attitude estimation systems with multiplicative noises, unknown measurement disturbances and correlated noises

Unknown measurement disturbances and correlated noise caused by jitter or vibration phenomena during spacecraft operation and the complex external environment are important topics for spacecraft attitude estimation. This paper studies the problem of spacecraft attitude estimation for nonlinear systems with multiplicative noises, unknown measurement disturbances and correlated noises. In addition, a two-step prediction framework is used to complete the noise decoupling. This paper aims to design a robust filter that minimizes the upper bound of the prediction error covariance and estimation error covariance in the presence of multiplicative noises, correlated noises and unknown measurement disturbances. The prediction gain and filtering gain of the robust filter are designed to minimize the upper bound. Finally, the simulation results verify the effectiveness of the proposed filter.

Low-Cost Onboard Multisensor State Estimator Based on Global Mixed Sampling Optimization Filter

Long-term convergent and accurate state estimation( attitude, velocity, and position) is critical to unmanned aerial vehicles (UAVs). However, the measurement accuracy and noise of multiple low-cost sensors onboard are poorer compared to high-precision sensors. To improve the solution accuracy and reliability of UAVs state estimation based on the low-cost sensors, a two-step multi-sensor fusion estimator (federated mixed-sampling Kalman filter, FMSKF) is proposed. Firstly, a multi-sensor integrated navigation model array is designed depending on the different sensors, including the gyroscope, accelerometer, magnetometer, GPS module, and barometer. Then, a three-stage mixed sampling Kalman filter is proposed to obtain the local convergent state vector, which is difficult to meet the flight accuracy requirements. Therefore, a multi-sensor global information optimization filter is proposed to obtain the global convergent navigation solution parameters. Finally, the simulation and flight experimental results and detailed analysis demonstrate that the proposed algorithm can improve the state estimation accuracy, filtering robustness, and obtain desirable navigation performance.