Adaptive Extended Kalman Filter Research Articles

An Adaptive Extended Kalman Filter With Passivity-Based Control for DC-DC Converter in DC Microgrids Supplying Constant Power Loads

This paper proposes an adaptive nonlinear control strategy to eliminate the instability problem of buck converters in DC microgrids (DCMG) considering constant power load (CPL). The strategy includes an Adaptive Passivity-Based Controller (PBC) and an Adaptive Extended Kalman Filter (AEKF). The proposed AEKF performs faster and more accurately than the conventional EKF and adjusts the covariance matrices adaptively. Furthermore, this AEKF directly estimates the unknown CPL power supplied by the converter and prevents the number of measurement sensors from increasing. Hence, PBC ensures system stability alongside AEKF. The controller guarantees Lyapunov stability and fast dynamic response in the presence of uncertainties and perturbations compared to other nonlinear methods. MATLAB simulations and laboratory implementation are provided to confirm the performance and robustness of the proposed control strategy and validate the analysis.

An Adaptive Robust EKF Based on Mahalanobis Distance and Non-Holonomic Constraints for Enhancing Vehicle Positioning Accuracy

An Adaptive Robust EKF Based on Mahalanobis Distance and Non-Holonomic Constraints for Enhancing Vehicle Positioning Accuracy

State of health estimation of lithium-ion battery using dual adaptive unscented Kalman filter and Coulomb counting approach

Several charging and discharging processes of lithium-ion batteries (LIBs) can lead to a battery fading and degradation effect. This may cause sudden faults, leakages, and explosions. As a result, it is highly important to estimate the state of health (SoH) of the battery to avoid any battery problems. This article proposes a novel hybrid dual adaptive unscented Kalman filter (DAUKF)-Coulomb counting approach (CCA) to efficiently state of charge (SoC) and SoH estimation of LIBs. The Gazelle optimization algorithm (GOA) is utilized in identifying LIB model parameters under various SoC conditions. It is used for minimizing the integral squared error between measured and estimated battery voltages. The battery model includes loading, fading, and various dynamic conditions. The GOA-based model has better results than other models by >8 %. The proposed hybrid DAUKF-CCA is compared with the dual adaptive extended Kalman filter (DAEKF)-CCA and other multiple algorithms. The fitness function consists of integral squared error between estimated and measured SoC of LIBs. The simulation results of the DAUKF-CCA are verified by a comparison with the measurement results performed using commercial Panasonic LiBs. The SoC results using the DAUKF-CCA are very close to the measurement results and the error is <1 %. The proposed DAUKF-CCA can ensure that the SoC and SoH estimation of LIBs is efficiently achieved.

Rotor speed estimation for half-broken bar detection in induction motors using Kalman filtering

This study addresses the early detection of half-broken bars in induction motors (IMs). Most research on fault detection focuses on current signature analysis because it can provide online and remote monitoring at a low cost. However, errors caused by spectral leakage around the fundamental frequency supply reduce its reliability for fault diagnosis at the incipient stages. The proposed detection technique is based on a spectral analysis of the motor speed. To detect a half-broken bar, measurements of stator voltages and currents are processed by an adaptive extended Kalman filter for system state estimation. Then, a spectral analysis is applied to the estimated motor speed to obtain its power spectral distribution. Finally, the fault harmonic amplitude at 2sfs is used as fault indicator for fault diagnosis. The proposed approach has three main advantages: (1) speed estimation is unaltered by spectral leakage around the main frequency supply, (2) the cost of speed sensors is avoided, and (3) the absence of connecting shaft couplings prevents corruption of speed data by mechanical interference. Numerical simulations and experimental results were carried out for the squirrel cage IM to provide encouraging validation of the proposed early fault detection technique.

A switched adaptive strategy for state estimation in MEMS-based inertial navigation systems with application for a flight test

Abstract This paper proposes a switched model to improve the estimation of Euler angles and decrease the inertial navigation system (INS) error, when the centrifugal acceleration occurs. Depending on the situation, one of the subsystems of the proposed switched model is activated for the estimation procedure. During global positioning system (GPS) outages, an extended Kalman filter (EKF) operates in the prediction mode and corrects the INS information, based on the system error model. Compared with previous works, the main advantages of the proposed switched-based adaptive EKF (SAEKF) method are (i) elimination of INS error, during the centrifugal acceleration, and (ii) high accuracy in estimating the attitude and positioning, particularly during GPS outages. To validate the efficiency of the proposed method in various trajectories, an experimental flight test is performed and discussed, involving a microelectromechanical (MEMS)-based INS. The comparative study shows that the proposed method considerably improves the accuracy in various scenarios.

Adaptive EKF enhanced fault diagnosis and fault tolerant control for space manipulators with position measurements only

Space manipulators play a critical role in the operation of space robots. However, the failure of these manipulators can affect the control performance and even lead to instability. To improve the safety and robustness of space manipulators, this paper presents an efficient fault diagnosis and fault tolerant control method by integrating adaptive extended Kalman filter (AEKF) and sliding mode control (SMC). The proposed method uses AEKF for system estimation, taking into account uncertainties in the process and measurement noise, as well as sensor limitations. By detecting the failure of angle sensors timely, the trajectory tracking performance can be guaranteed by the robustness of AEKF. In cases where a joint experiences free-swinging, the failure can be identified based on the estimated control effectiveness coefficients from AEKF. Then, once the failure is identified, the joint angle can be regulated by exploiting the motion coupling between the joints. Furthermore, SMC is employed to mitigate the effects of unknown disturbances in the joints. The effectiveness of the proposed method is demonstrated through numerical simulation, which illustrates its ability to diagnose failures and maintain control performance under various failure scenarios. In addition, the robustness of the method is verified through Monte Carlo simulation, demonstrating its reliability in dealing with various uncertainties.

A novel multi-factor fuzzy membership function - adaptive extended Kalman filter algorithm for the state of charge and energy joint estimation of electric-vehicle lithium-ion batteries

In view of the unmeasurable state parameters of electric-vehicle lithium-ion batteries, this paper investigates a novel multi-factor fuzzy membership function - adaptive extended Kalman filter (MFMF-AEKF) algorithm for the online joint estimation of the state of charge and energy. Strong nonlinear characteristics of model parameters are characterized by considering multiple processing factors of electrochemical and diffusion effects for lithium-ion batteries and constructing an optimized multifactor coupling model. In the proposed MFMF-AEKF method, multi-space-scale factors are introduced to realize the numerical analysis of the multi-factor coupled model parameters and state estimation under dynamic working conditions of electric-vehicle lithium-ion batteries. The proposed MFMF-AEKF algorithm estimates the state of charge (SOC) with the overall best mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and maximum error (ME) values of 1.822 %, 4.322 %, 1.947 %, and 2.954 %, respectively, under challenging working conditions. And The MAE, MAPE, RMSE, and ME values for the state of energy (SOE) are 0.617 %, 1.711 %, 0.695 %, and 1.011 %, respectively. Both state estimation results are better than the traditional method. The proposed MFMF-AEKF algorithm has higher estimation accuracy which provides a feasible estimation algorithm for the joint SOC and SOE of lithium-ion batteries.

Investigation of navigation information correction techniques for master-slave AUV formations in unstable communication environments

Cooperative navigation within master–slave Autonomous Underwater Vehicles (AUVs) necessitates the periodic transmission of high-precision positional data from the master AUV, endowed with advanced navigational instrumentation, to the slave AUV characterized by limited positional accuracy. This data exchange is facilitated through an acoustic sensor mechanism, enabling the quantification of relative spatial separation between the two vehicles. Employing an Extended Kalman Filter (EKF), the positioning error of the slave AUV can be estimated and corrected, enhancing its positional accuracy. However, this collaborative navigation methodology encounters dual challenges. Firstly, the intricate underwater acoustic environment introduces the potential for data loss or outlier measurements in submarine acoustic communication, thereby jeopardizing the efficacy of collaborative navigation. Secondly, the current approach solely rectifies the slave AUV's positional error upon the receipt of data from the master AUV, neglecting the fundamental divergence in positional error that accrues between successive data receptions, stemming from the inherent imprecision in the dead reckoning process. To contend with these complexities, this study presents a novel approach to rectifying navigation data within master–slave AUV formations, particularly within the context of unstable communication conditions. The proposed method harnesses an outlier adaptive discriminant EKF framework, proficiently addressing the slave AUV's positional error while simultaneously estimating and compensating for azimuthal angular errors during underwater acoustic communication between the master and slave AUVs. This method establishes a pivotal groundwork for heightening the accuracy of independent autonomous navigation within the slave AUV during subsequent operational phases. The accuracy and applicability of the proposed method are rigorously validated through simulation-based verification and lake-water field experiments. The empirical results unequivocally highlight the efficacy of the underwater acoustic communication approach put forth in this study, with an outlier recognition rate exceeding 80%. Furthermore, the method adeptly rectifies azimuthal angular errors and positional inaccuracies within the AUV, thus effecting a comprehensive enhancement of overall positioning precision. This research bears significant implications for advancing autonomous underwater navigation, providing a robust framework to bolster the precision and reliability of master–slave AUV formations, particularly under challenging communication circumstances.

PSO-guided optimal estimator enabled regularized adaptive extended Kalman filter with unknown inputs for dynamic nonlinear indoor thermal state estimation

This paper presents a particle swarm optimization-guided maximum likelihood estimation enabled (MLE) adaptive extended Kalman filter (EKF) with unknown inputs algorithm for estimating the dynamic nonlinear thermal states for an indoor heating ventilation and air conditioning system. The concept of MLE has been introduced to enhance the speed of convergence of the filtering parameters in adaptive EKF. The nonlinear indoor environment has been modelled employing equivalent RC network taking relative humidity into account. At the outset, an EKF-based method accommodating the unknown inputs and an adaptive estimator-based variant of it are developed for estimating the temperature of the walls of a laboratory-scale realistic environment. Subsequently the proposed scheme comes into play to deal with the scenarios associated with undesirable divergence and poor initialization utilizing the metaheuristically adapted optimal regularizer. The proposed technique outperforms the other contemporary state-of-the-art counterparts in terms of mean squared error.

Yaw Stability Control of Unmanned Emergency Supplies Transportation Vehicle Considering Two-Layer Model Predictive Control

The transportation of emergency supplies is characterized by real-time, urgent, and non-contact, which constitute the basic guarantee for emergency rescue and disposal. To improve the yaw stability of the four-wheel-drive unmanned emergency supplies transportation vehicle (ESTV) during operation, a two-layer model predictive controller (MPC) method based on a Kalman filter is proposed in this paper. Firstly, the dynamics model of the ESTV is established. Secondly, the improved Sage–Husa adaptive extended Kalman filter (SHAEKF) is used to decrease the impact of noise on the ESTV system. Thirdly, a two-layer MPC is designed for the yaw stability control of the ESTV. The upper-layer controller solves the yaw moment and the front wheel steering angle of the ESTV. The lower-layer controller optimizes the torque distribution of the four tires of the ESTV to ensure the self-stabilization of the ESTV operation. Finally, analysis and verification are carried out. The simulation results have verified that the improved SHAEKF can decrease the state estimation error by more than 78% and achieve the noise reduction of the ESTV state. Under extreme conditions of high velocity and low adhesion, the average relative error is within 6.77%. The proposed control method can effectively prevent the instability of the ESTV and maintain good yaw stability.

DFFRLS-FAUKF: accurate and reliable monorail longitudinal slope identification method

Monorail cranes have always played an important role in mine auxiliary transportation systems owing to their excellent transportation performance and are therefore a desirable area in which to apply driverless technologies. However, the low-accuracy recognition of monorail track slopes and the poor reliability of recognition results make it difficult and dangerous to implement fully driverless monorail cranes. Aiming to solve these problems, a method for the accurate identification of longitudinal monorail slopes based on the use of a dynamic forgetting factor for recursive least squares (DFFRLS) and a fuzzy adaptive unscented Kalman filter (FAUKF) is proposed. First, acquired acceleration and velocity data are pre-processed using a rolling window. Second, the real-time longitudinal track-curvature value is calculated using the DFFRLS algorithm with the processed data and an established track-curvature model. Finally, based on existing track-curvature values, dynamic recognition of the monorail track slope is realized using the FAUKF algorithm with a fuzzy control factor, improving the accuracy of track gradient recognition. Experiments show that the DFFRLS-FAUKF algorithm improves the accuracy of track-slope recognition by up to 21.26% and 33.93% on average compared with that of DFFRLS with an adaptive extended Kalman filter (DFFRLS-AEKF) or an adaptive unscented Kalman filter (DFFRLS-AUKF).

System Identification of a Structure Equipped with a Cable-Bracing Inerter System Using Adaptive Extended Kalman Filter

An innovative cable-bracing inerter system (CBIS) has been proposed and shown to be effective in mitigating the structural response under dynamic excitation. The CBIS comprises an inerter element, an eddy current damping element, and a pair of tension-only cables that can transfer the story drift to rotating flywheels. To further investigate the characteristics of the CBIS, a system identification approach based on an adaptive extended Kalman filter (AEKF) and a recursive least-squares (RLS) algorithm is proposed. Depending on the CBIS model’s availability, the proposed approach uses two strategies: the AEKF identifies the parameters of the structure and the CBIS when the model is specific; alternatively, when the model is unspecific, the KF combined with an RLS algorithm identifies the restoring force generated by the CBIS as an unknown fictitious input. In addition, the AEKF incorporates a time-variant fading factor to track the target adaptively. The proposed approach is validated through free vibration and shaking table tests, demonstrating the accuracy in identifying structural parameters and restoring force provided by the CBIS. The identification process involves two stages: initially, the AEKF identifies the parameters of the bare structure without the CBIS, followed by a dual strategy using either AEKF or KF-RLS for identifying the parameters of the CBIS or its restoring force, respectively. The findings also verify the feasibility and validity of the mechanical model and operating principle of the CBIS, thereby contributing to the advancement and application of the CBIS in future studies.

An accurate and robust visual-inertial positioning method

The human–machine integrated coordinate measurement is a promising coordinate measurement method with high flexibility and efficiency for the complex working environments. The cameras installed on the head-mounted measurement device achieves accurate global positioning by observing the uncoded LED landmarks, and then combines with the local measuring to obtain 3D coordinates. However, limited by the frame rate of the camera, the fast movements of the operator’s head may cause landmark misidentification and visual positioning failure. In order to improve the robustness, a visual-inertial positioning method is proposed in this paper. An inertial measurement unit (IMU) is added to compensate for the deficiency of the visual positioning and enhance the dynamic performance. An adaptive extended Kalman filter (EKF), which adjusts the measurement noise covariance matrix based on the visual positioning uncertainty, is established to obtain the optimal state estimation. And an efficient initialization procedure is presented to implement the initial registration of uncoded landmarks based on the normal distribution transform algorithm and to determine the initial state of the IMU. Furthermore, the residual chi-square test is employed to detect false pose estimate in real time and to avoid positioning failure. The experiments demonstrate that the proposed method has high static positioning accuracy (0.681 mm) and high dynamic positioning robustness. The adaptive EKF realizes reliable landmark identification under fast movements and provides a higher accuracy than the common EKF.

Time-varying parameters estimation with adaptive neural network EKF for missile-dual control system

Time-varying parameters estimation with adaptive neural network EKF for missile-dual control system

The adaptive extended kalman filter approach for the Lotka-Volterra model and application to economic variables

The adaptive extended kalman filter approach for the Lotka-Volterra model and application to economic variables

Research on battery state of charge estimation based on variable window adaptive extended Kalman filter

Battery state of charge (SOC) estimation is one of the indispensable functions in the battery management system. Among them, the Kalman filter series of algorithms are widely used in the battery SOC estimation process. The adaptive extended Kalman filter uses the difference between the measured voltage and the estimated voltage (error innovation) as the innovative covariance matrix update noise in battery SOC estimation. However, the general adaptive Kalman filter does not consider the error innovation in the estimation process. The change. This will lead to inaccurate battery SOC estimation. In order to solve this problem, we proposes a variable window Kalman filter algorithm that takes into account the changes in the error innovation sequence. The algorithm updates the innovation covariance matrix according to the change of error innovation to improve the accuracy of SOC estimation. The results show that compared with the extended Kalman filter and the fixed window Kalman filter, its calculation efficiency is also improved while its accuracy is improved. Finally, its accuracy is verified under different initial parameters, and the study shows that the algorithm proposed in this article is robust.

Adaptive hybrid Kalman filter for attitude motion parameters estimation of space non-cooperative tumbling target

Attitude motion parameters estimation of the space non-cooperative tumbling target is the premise of the on-orbit target capture task. However, existing methods cannot balance the estimation accuracy and efficiency. In this paper, an adaptive hybrid Kalman filter is presented to estimate the attitude, angular velocity and inertia motion parameters of the space non-cooperative tumbling target. First, the dynamic model of the non-cooperative tumbling target is derived, and the nonlinear discrete state-space equation is obtained. Then, a hybrid Kalman filter is designed by combining the extended Kalman filter and the unscented Kalman filter. The adaptive technique is introduced to track time-varying uncertain measurement noise caused by complex space disturbances. After that, based on the first-order linearization technique, the sufficient condition for the local convergence of the proposed method is proved. Finally, the numerical simulation experiment was conducted to compare the proposed method with the adaptive extended Kalman filter and the adaptive unscented Kalman filter. The experimental results show that the estimation accuracy of the proposed method is much higher than that of the adaptive extended Kalman filter and slightly lower than that of the adaptive unscented Kalman filter, and the iteration-average Central-Processing-Unit time is 64.2% smaller than that of the adaptive unscented Kalman filter and slightly longer than that of the adaptive extended Kalman filter. The proposed method achieves a much higher accuracy with a slight loss of iteration-average Central-Processing-Unit time.

Leveraging digital twins for fault diagnosis in autonomous ships

Autonomous ships are vulnerable to a spectrum of potential faults, including propeller damage, shaft-line failures, and electrical supply disruptions. Swift fault detection is critical to minimize the potentially catastrophic consequences of these issues. In this paper, we introduce an innovative digital twin-based method for diagnosing faults in autonomous ships. Our digital twin system collects data from the ship’s sensor array, conducts comprehensive analysis, computes fault parameters, and provides real-time visualizations of the findings. To achieve this, we utilize the ship’s dynamical model and employ a novel adaptive extended Kalman filter (AEKF) algorithm to estimate the severity of these faults. We evaluate the effectiveness of this approach through numerical simulations and practical implementation in an autonomous surface vehicle called the Otter, developed by Maritime Robotics. The experimental results, which consider both normal and faulty ship propulsion systems, underscore the significant potential of this novel approach to improve ship condition monitoring during operational activities.

Establishment of a Lithium-Ion Battery Model Considering Environmental Temperature for Battery State of Charge Estimation

Accurate estimation of the state-of-charge (SOC) is essential to prevent overcharging and over-discharging of lithium-ion batteries. However, traditional SOC estimation methods exhibit significant errors under large temperature variations due to the strong temperature dependence of battery characteristics. To enhance the accuracy of SOC estimation, this study proposes a second-order RC equivalent circuit model with temperature correction. By considering the influence of temperature on model parameters, the model’s accuracy is improved by adjusting the estimated parameters. A polynomial coefficient data table for model parameters is established to expedite the computation time of the SOC estimation process. Finally, the temperature-corrected model is combined with an Adaptive Extended Kalman Filter (AEKF) algorithm for SOC estimation. The results of the Dynamic Stress Test (DST) condition experiments show that the temperature correction model can improve the accuracy of SOC estimation under different temperature conditions. It has a more lower SOC estimation error than the model without temperature correction.

Dynamic ultrasonic response modeling and accurate state of charge estimation for lithium ion batteries under various load profiles and temperatures

Accurate estimation of the State of Charge (SoC) plays a vital role in ensuring the efficient and safe operation of lithium iron phosphate (LFP) batteries. However, the flat open circuit voltage (OCV) curve of the LFP battery implies a low sensitivity to SoC, which results in large SoC estimation errors in the presence of noisy terminal voltage measurements. To address this challenge, an SoC estimation methodology utilizing an ultrasonic reflection response model is proposed, which is the first methodology regarding highly accurate and robust ultrasonic model-based SoC estimation under dynamic load profiles. Since ultrasound waves enable non-destructive acquisition of battery internal physical property changes directly associated with SoC, the ultrasonic battery near-surface reflection feature is extracted and demonstrated to exhibit a highly linear correlation with and higher sensitivity to SoC. We pioneeringly construct an empirical differential ultrasonic model to describe how the ultrasonic feature depends on the SoC and dynamic current. The advantage of such an ultrasonic model is demonstrated by theoretical and experimental results of an Adaptive Extend Kalman Filter (AEKF) and an Adaptive H-infinity Filter (AHIF) under various dynamic load profiles and temperatures. The Root Mean Square Error (RMSE) for ultrasonic model-based SoC estimation remains at approximately 1% across all tests, reducing by 36.7% compared to the voltage model, which shows its great potential in accurate SoC estimation.