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For Real-time hybrid simulation (RTHS) to be stable and accurate, it is essential to address the time desynchronization issue between the numerical and physical substructures. Desynchronization is primarily caused by time delays, inherent dynamics of the control plant, system uncertainties, and noises. While existing adaptive compensators have shown effective tracking performance in single-input single-output (SISO) RTHS, their effectiveness in multi-input multi-output (MIMO) RTHS has not been fully demonstrated. MIMO-RTHS presents additional challenges due to its larger solution space, and significant dynamic coupling between actuators. To address these challenges, this study introduces an adaptive compensation framework for MIMO-RTHS. The proposed framework utilizes a control law based on the inverse dynamics of the control plant, incorporating real-time adaptive parameter updates through Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) methods. Both the transfer function (TF) and discrete-time state-space (SS) models of the plant are employed in distinct parameter estimation cases. The performance of the proposed compensation is validated through a multi-axial RTHS (maRTHS) benchmark problem. Extensive simulations on the maRTHS incorporating various earthquake inputs, sensor noise, and model uncertainties, demonstrated an excellent tracking performance and strong robustness across four parameter estimation cases (EKF-TF, UKF-TF, EKF-SS, and UKF-SS). The use of UKF with SS model (UKF-SS) achieved superior performance, effectively managing nonlinearities and noise without requiring low-pass filtering. [ABSTRACT FROM AUTHOR]

The operation of parallel inverters can enhance the reliability of power supply and meet the demand of the grid forming system; however, due to the difference in the Zero Sequence Voltage (ZSV) and the existence of the Zero Sequence Path (ZSP), the problem of Zero Sequence Circulating Current (ZSCC) inevitably occurs. This paper proposes a fixed-time control strategy based on the Extended Kalman Filter (EKF), used in order to suppress the ZSCC issue in a paralleled inverter system. Firstly, the detailed mathematical model of ZSCC is described, where the inductance perturbations are considered according to the generation mechanism of ZSCC, and a novel ZSCC controller is designed based on the principles of the fixed-time stability theory which can assure the action time of the zero vectors in one switching cycle. Secondly, to reduce the influence of the inductor parameters on the ZSCC control effect, the EKF is used to identify the online inductance parameters of the filter, and the robustness of the algorithm can be improved. Subsequently, based on the Lyapunov stability criterion, it has been proved that the proposed control strategy is fixed-time stable. Finally, the simulation and experiments are employed to demonstrate the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of limited timing accuracy in complex environments for both the BeiDou system (BD) and the long-range navigation system (LORAN). We delve into the correction algorithm for LORAN timing signals and an adaptive fusion timing algorithm based on the extended Kalman filter (EKF). First, we introduce the advantages and limitations of the BD and LORAN systems in timing applications, as well as the principles of the EKF algorithm and its application in multisource information fusion. Next, we propose a correction algorithm signal to address the significant fluctuations in LORAN timing signals. Building on this, we continue to study an adaptive BD and LORAN fusion timing algorithm based on the EKF. This involves optimising system noise covariance through adaptive adjustments and establishing a fusion timing algorithm model based on the EKF. Finally, we construct a test platform to verify the effectiveness of the proposed algorithms. The experimental results demonstrate that, compared to a single navigation system, the adaptive BD and LORAN fusion timing algorithm based on the EKF significantly improves the accuracy and stability of system timing. Additionally, correcting the LORAN timing results before fusion further enhances system fusion timing performance metrics. The algorithm still maintains high performance in complex environments, showing great application prospects. [ABSTRACT FROM AUTHOR]

Sensor selection is a vital part of Wireless Sensor Network (WSN) management. This becomes of increased importance when considering the use of low-cost, bearing-only sensor nodes for the tracking of Unmanned Aerial Vehicles (UAVs). However, traditional techniques commonly form excessively large sensor clusters, which result in the collection of redundant information, which can deteriorate performance while also increasing the associated network costs. Therefore, this work combines a predictive posterior distribution methodology with a novel simplified objective function for optimally identifying and forming smaller sensor clusters before activation and measurement collection. The goal of the proposed objective function is to reduce network communication and computation costs while still maintaining the tracking performance of using far more sensors. The developed optimisation algorithm results in reducing the size of selected sensor clusters by an average of 50% while still maintaining the tracking performance of general traditional techniques. [ABSTRACT FROM AUTHOR]

Accurately characterizing the dynamic load environment is vital for the structural optimization and fatigue life assessment of liquid rocket engines, but the difficulty in precisely measuring or identifying such distributed loads is substantial. This paper proposed a method to determine the equivalent concentrated loads of liquid rocket engines at main vibration sources using the direct inverse method in the frequency domain. Response verification is performed to confirm the effectiveness of the equivalent loads by assessing the consistency between responses due to them and actual distributed loads. A pumped liquid rocket engine is investigated for specific research. Responses from the hot-fire test are used to identify equivalent loads at three main vibration sources, which are the gas generator, turbine shell, and combustion chamber. The results indicate that the errors in responses induced by the equivalent loads and the actual distributed loads are generally within ±4 dB. To further validate the equivalent loads, an experiment is conducted, scaling and applying the equivalent loads to the rocket engine. The resulting responses, after amplification, align well with those obtained from the hot-fire test, confirming the validity of the proposed method. However, response verification errors escalate significantly when nonprimary sources are included in the equivalent locations. [ABSTRACT FROM AUTHOR]

In farmland environments where GNSS signals are obstructed, such as forested areas or in adverse weather conditions, traditional GNSS/INS integrated navigation systems suffer from positioning errors and instability. To address this, a model-assisted integrated navigation system is proposed, combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The CNN-BiLSTM model is trained under normal GNSS conditions and used to predict positioning when GNSS signals are interrupted, effectively replacing GNSS to ensure stable and accurate navigation. Experimental validation is conducted using field data from tractor simulations. The results show that, during a 100-s GNSS denial, the CNN-BiLSTM model reduces the average position error by 79.3% compared to pure inertial navigation and by 5.4% compared to traditional LSTM. In a 30-s GNSS denial, the average position error is reduced by 41% compared to inertial navigation and 6.2% compared to LSTM. The model maintains positioning accuracy within 3% of the GNSS/INS output under normal conditions, demonstrating its feasibility and effectiveness. This approach offers a promising solution for autonomous tractor navigation in GNSS-denied agricultural environments, contributing to precision agriculture. [ABSTRACT FROM AUTHOR]

Model‐based monitoring and control of chemical and biochemical processes rely on state estimators such as extended Kalman filters (EKFs) to ensure accurate online model predictions. Accurate predictions depend on appropriate model parameters and suitable state‐estimator tuning factors. Extensions to our previously developed simultaneous parameter estimation and tuning (SPET) method are proposed so that SPET can be used for systems with nonstationary disturbances, time‐varying parameters, multi‐rate data, and measurement delays. A continuous stirred tank reactor (CSTR) case study with simulated data is used to illustrate and test the proposed method. Superior online model predictions and state‐estimator performance are achieved using SPET compared to a traditional approach for parameter estimation and EKF tuning, with improvements in the average sum‐of‐squared prediction errors ranging from 3% to 52% for the scenarios tested. The SPET approach will also be useful for more‐advanced state estimators that require the same tuning information as EKFs. [ABSTRACT FROM AUTHOR]

To effectively eliminate the nonlinear distortion caused by the modulation depth (C value) drift and carrier phase delay (θ) in the phase-generated carrier (PGC) demodulation scheme, the PGC-PDC-EKF joint algorithm is presented, which combines phase delay compensation (PDC) with an extended Kalman filter (EKF). The θ is accurately extracted and compensated by the PDC module. Furthermore, with the EKF algorithm, the harmonic distortion of the demodulated signal due to the fluctuation of C value is suppressed. The experimental results indicate that θ is compensated accurately with a resolution of 0.01745 rad. The signal-to-noise and distortion ratio (SINAD) of the improved scheme reaches 54.01 dB, which is 18.03 dB higher than the PGC-Arctan algorithm on average. The total harmonic distortion (THD) is as low as −62.28 dB, which is 26.04 dB lower than the PGC-Arctan algorithm. The linearity of the demodulation system exceeds 99.99%. The proposed method provides a significant reference for demodulation schemes of interferometric fiber optic sensing systems in practical applications. [ABSTRACT FROM AUTHOR]

For Real-time hybrid simulation (RTHS) to be stable and accurate, it is essential to address the time desynchronization issue between the numerical and physical substructures. Desynchronization is primarily caused by time delays, inherent dynamics of the control plant, system uncertainties, and noises. While existing adaptive compensators have shown effective tracking performance in single-input single-output (SISO) RTHS, their effectiveness in multi-input multi-output (MIMO) RTHS has not been fully demonstrated. MIMO-RTHS presents additional challenges due to its larger solution space, and significant dynamic coupling between actuators. To address these challenges, this study introduces an adaptive compensation framework for MIMO-RTHS. The proposed framework utilizes a control law based on the inverse dynamics of the control plant, incorporating real-time adaptive parameter updates through Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) methods. Both the transfer function (TF) and discrete-time state-space (SS) models of the plant are employed in distinct parameter estimation cases. The performance of the proposed compensation is validated through a multi-axial RTHS (maRTHS) benchmark problem. Extensive simulations on the maRTHS incorporating various earthquake inputs, sensor noise, and model uncertainties, demonstrated an excellent tracking performance and strong robustness across four parameter estimation cases (EKF-TF, UKF-TF, EKF-SS, and UKF-SS). The use of UKF with SS model (UKF-SS) achieved superior performance, effectively managing nonlinearities and noise without requiring low-pass filtering. [ABSTRACT FROM AUTHOR]

This paper investigates the seismic damage and identification of lateral stiffness for ancient timber structures. Based on the shaking table test of a palace‐style timber structure, the lateral displacement responses, interstory equivalent lateral stiffness (IELS), and sensitivity of lateral displacement to degradation of IELS were analyzed to investigate the degradation and identification of IELS. The state and observation equations of a simplified mechanical model of the timber frame considering the friction slipping of the column base were predicted. Considering the noise disturbance in the test, the IELS of the model was identified by the partial least squares–singular value decomposition (PLS‐SVD) and extended Kalman filter (EKF) method. Results shown that the ratio of the IELS of the column frame layer to Ru‐Fu layer decreased as the seismic damage increased, and the peak lateral displacement was more sensitive to the damage of the column frame layer than the Ru‐Fu layer. Under nondamage conditions, the identification error of the IELS was about 10%, while the error ranged from 15% to 20% under damage conditions. Through the identification of the equivalent lateral stiffness of the Xi'an bell tower, it was validated that the hybrid method is effective in monitoring the IELS of ancient timber structures. [ABSTRACT FROM AUTHOR]

The nonlinear roll damping coefficients and natural frequency of a floating body are discussed in this article using an augmented Extended Kalman Filtering (EKF) method. This paper presents a non-linear model-based observer for the combined estimation of free roll decay motion states and damping coefficients. The identification framework uses an improved extended Kalman filter (EKF) to deal with model parameter variability and noisy measurement input. The estimated error was compared to the theoretical results obtained from the covariance matrix for filtering correctness. It is found that the nonlinear damping coefficients and natural frequency obtained from the EKF approach are a more realistic representation of the roll dynamics from the general estimation procedure given by the literature. The proposed method also has the built-in capability to remove random noise from the measured roll. The influence of noise levels in the measurements on the identification accuracy was investigated and discussed. [ABSTRACT FROM AUTHOR]

Active jammers in the form of digital radio frequency memory (DRFM) could produce false targets with a coherence of range and velocity, making it increasingly efficient in interrupting radar systems based on data correlation analysis. By integrating the temporal variability characteristic and mitigating the impact of measurement errors inherent in the radar system, we propose a novel false target recognition methodology for a two‐station netted radar utilizing extended Kalman filter (EKF) and the Neyman–Pearson criterion. From the aspects of filtering characteristics and active jammer parameters, the simulation shows that the technique could obtain observable different convergence curves for false targets and is sensitive to velocity or range deception under specific parameters. Moreover, the method exhibits a favorable tolerance for measurement error and an exceptional capability to identify false targets from various directions, considering both radar performance parameters and spatial scale. This outcome highlights the significance of setting specific parameters following countermeasure scenarios and requirements, showcasing its practical value in engineering practice. [ABSTRACT FROM AUTHOR]

This study describes a method for applying an extended Kalman filter to a snake robot, which moves through a pipe using a helicoidal rolling motion. The filter estimates the helix radius, amount of twisting, and helix pitch to adapt to changes in the diameter inside the pipe. Additionally, offset values are added to these estimated values to propel the snake robot along the pipe autonomously. In particular, we experimentally demonstrated the effect of helix pitch on friction force and proposed a parameter-setting strategy that accounts for the range of motion of the joints. Finally, we validated the results of the proposed method through experiments on an actual robot. [ABSTRACT FROM AUTHOR]

Since installing speed sensors on the grinding head for high-speed precision roll grinders is not feasible, it is necessary to use direct torque control (DTC) without speed sensors, and its control system requires high estimation accuracy. In this paper, speed estimation methods using extended Kalman filter (EKF) and model reference adaptive system (MRAS) have been used to realise speed estimation in the DTC system of the spindle motor of the grinding head for a high-speed precision roll grinder. The simulation results showed that the EKF method has relatively high accuracy and a fast convergence speed for the estimation speed of the spindle motor of the grinding head for a high-speed precision roll grinder by comparison with the MRAS method. Moreover, the EKF method was also able to overcome the nonlinear and parameter uncertainty of the operating system of the motor and strongly immune to the noise interference of the system. Finally, the speed estimation accuracy of the motor using the EKF method had better than the MRAS method. Therefore, this work provides a general approach to applying the DTC without speed sensors for the motor and further lays a solid foundation for developing high-speed precision roll grinders. [ABSTRACT FROM AUTHOR]

A detailed analysis has been conducted on two motor control algorithms: direct torque control (DTC) and field-oriented control (FOC). There are two ways that a voltage source inverter (VSI) can regulate a permanent magnet synchronous motor (PMSM). When using the PMSM and voltage source inverter (VSI), dead time is employed to turn off both the upper and lower switches to prevent short circuits. However, by supplying the PMSM with unexpected polarity voltages at the VSI output voltage, this switching technique reduces distortion. It is challenging to utilize the sensor to directly detect the fault voltage that results in an open circuit. This work examines the nonlinearity of the electric power controller during dead time during PMSM operation using the DTC algorithm to increase control stability. The stress distribution is estimated using an extended Kalman filter (EKF). Ultimately, the model presented in this study verified the increase in stator current and torque output through simulations and testing. [ABSTRACT FROM AUTHOR]

Space robots have become increasingly important in on-orbit missions, especially for capturing non-cooperative targets. However, a major challenge is that the inertial parameters of these targets are often unknown, but crucial for post-capture tasks. This paper proposes continuous-discrete extended Kalman filter based identification methods that rely solely on noisy measurements of the manipulator's rotation angle, the base's attitude angle, and its position. The methods exploit the conservation of momentum or the dynamics of space robots to formulate the identification equations and construct the filter. In addition, an analytical solution for computing the Jacobian matrix of the dynamic response of a space robot is derived, and sparse matrix multiplication is used to reduce the computation time. The effectiveness and efficiency of the proposed methods are evaluated through numerical simulations using 2D and 3D models, and Monte Carlo simulations are performed to analyze robustness, noise effects, and initial states. The simulation results confirm the effectiveness of the proposed methods and demonstrate their ability to handle noise and uncertainty. [ABSTRACT FROM AUTHOR]

The Omicron variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is an extremely contagious variant that has garnered global attention due to its potential for rapid spread and its impact on the effectiveness of vaccines and non-pharmacological measures. In this paper, we investigate mathematical models involving vaccinated individuals and control functions to analyze how the spread of coronavirus disease 2019 (COVID-19) infection evolves over time. In the process of constructing a mathematical model for COVID-19, there are many parameters whose values are not yet known with certainty. Therefore, the extended Kalman filter method is used as a tool to estimate these parameters in an effort to better understand the dynamics of the spread and evolution of this disease. This method helps align the mathematical model with existing empirical data, allowing us to make more accurate predictions about the course of the COVID-19 pandemic and plan more precise actions to address the situation. Furthermore, an optimal control design is applied to reduce the number of infected individuals by implementing seven strategies involving a combination of health education, vaccination, and isolation controls. The simulation results we conducted indicate that the use of optimal control strategies can lead to a significant decrease in the number of individuals infected with COVID-19. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) are set to play a crucial role in the energy transition. Although EVs offer significant environmental benefits, their technology still faces major challenges related to performance optimization, energy efficiency improvement, and cost reduction. A key point to address these challenges is the accurate identification of the speed/torque operating points of the drive systems. However, this identification is generally achieved using mechanical sensors, which are fragile, bulky, and expensive. This paper aims to develop, implement, and validate a speed/torque observer in real time based on the Extended Kalman Filter (EKF) approach for an EV equipped with an Open-End Winding Induction Motor with Dual Inverter (OEWIM-DI). The implementation of the EKF is based on the state modeling of the OEWIM-DI, enabling the observation of the torque and speed using voltage and current measurements. The validation of this approach is conducted experimentally on the FPGA and DS1104 boards. The results show that this approach offers excellent performance in terms of accuracy, stability, and real-time response speed. These results suggest that the proposed method could significantly contribute to the advancement of EV technology by providing a more robust and cost-effective alternative to traditional mechanical sensors while improving the overall efficiency and performance of EV drive systems. [ABSTRACT FROM AUTHOR]

This paper presents the development of an advanced battery management system (BMS) for electric vehicles (EVs), designed to enhance battery performance, safety, and longevity. Central to the BMS is its precise monitoring of critical parameters, including voltage, current, and temperature, enabled by dedicated sensors. These sensors facilitate accurate calculations of the state of charge (SOC) and state of health (SOH), with real-time data displayed through an IoT cloud interface. The proposed BMS employs data-driven approaches, like advanced Kalman filters (KF), for battery state estimation, allowing continuous updates to the battery state with improved accuracy and adaptability during each charging cycle. Simulation tests conducted in MATLAB's Simulink across multiple charging and discharging cycles demonstrate the superior accuracy of the advanced Kalman filter (KF), in handling non-linear battery behaviours. Results indicate that the proposed BMS achieves a significantly lower error margin in SOC tracking, ranging from 0.32% to 1%, compared to traditional methods with error margins up to 5%. These findings underscore the importance of integrating robust sensor systems in BMSs to optimise EV battery management, reduce maintenance costs, and improve battery sustainability. [ABSTRACT FROM AUTHOR]

This paper focuses on application of fuzzy logic to enhance induction motor drive performance in case where the measured stator currents are distorted by Gaussian noise. For different load torques, the fixed proportional-integral (PI) speed controller provides an undesired drive performance for both transient and steady-state responses. At first, load torque is computed thanks to extended-Kalman-filtered stator currents. Next, load magnitude is employed to adjust the proportional gain and integral constant time of the fuzzy logic (FL) proportional-integral (PI) speed controller. Simulations of drive using two controllers: FL-PI and fixed PI ones, are carried out in different cases of stator current noise and load variation. Performance evaluations indicate that the FL-PI speed controller reduces the assessed indices and increases robustness to noise and load variation. [ABSTRACT FROM AUTHOR]

This paper presents the development of an advanced battery management system (BMS) for electric vehicles (EVs), designed to enhance battery performance, safety, and longevity. Central to the BMS is its precise monitoring of critical parameters, including voltage, current, and temperature, enabled by dedicated sensors. These sensors facilitate accurate calculations of the state of charge (SOC) and state of health (SOH), with real-time data displayed through an IoT cloud interface. The proposed BMS employs data-driven approaches, like advanced Kalman filters (KF), for battery state estimation, allowing continuous updates to the battery state with improved accuracy and adaptability during each charging cycle. Simulation tests conducted in MATLAB’s Simulink across multiple charging and discharging cycles demonstrate the superior accuracy of the advanced Kalman filter (KF), in handling non-linear battery behaviours. Results indicate that the proposed BMS achieves a significantly lower error margin in SOC tracking, ranging from 0.32% to 1%, compared to traditional methods with error margins up to 5%. These findings underscore the importance of integrating robust sensor systems in BMSs to optimise EV battery management, reduce maintenance costs, and improve battery sustainability.