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In this paper, a state space electromyography (EMG) model is proposed to predict multi-DOF upper limb joint angles continuously and simultaneously. The two main approaches to estimating multi-joint kinematics are numerical and model-based methods. The high computational cost of the numerical algorithms causes delays. On the other hand, the open-loop model-based systems would not be effective to estimate the joint angles due to the accumulated errors that are not compensated. To overcome this problem, a relation between EMG signal features and joint angles is trained and developed offline to form measurement equations. Then, the integration of the Hill-based musculoskeletal model and the trained measurement equations is used in a state-space model. By using Extended Kalman Filter (EKF), the multi-joint angles are estimated continuously and simultaneously. In this paper, the shoulder abduction–adduction and elbow flexion–extension angles of the human arm during dumbbell shoulder press are estimated using the EMG signals. The average root mean squared error (RMSE) of the estimation and real joint angles of the shoulder and elbow to be 0.26 and 0.27, respectively, indicates that the proposed model-based method can be used to estimate the joint angles continuously and simultaneously reducing the computational time of numerical estimations. [ABSTRACT FROM AUTHOR]

This study addresses the divergence issues in GPS navigation systems caused by inaccuracies in dynamic modeling and explores solutions using the extended Kalman filter (EKF). Since algorithms such as the Kalman filter (KF) and EKF rely on assumed process models that often deviate from real-world conditions, their performance in real-time applications can degrade. This paper introduces fictitious process noise as an effective remedy to mitigate divergence, demonstrating its benefits through covariance estimation and tuning factors to enhance observability and controllability, particularly for continuous differential GPS (DGPS) access. The study evaluates several motion scenarios, including stationary receivers, straight-line trajectories with constant and varying speeds, and turning trajectories. The inclusion of process noise allows the EKF to adapt to changes in direction and speed without explicitly modeling turning or acceleration dynamics. To ensure robustness, the simulations incorporate a variety of scenarios to assess the statistical reliability and real-world performance of the EKF, ensuring the findings are statistically robust and widely applicable. Simulated receivers were used to evaluate the position (P), position–velocity (PV), and position–velocity–acceleration (PVA) models. The results from both the Ordinary Least-Squares (OLS) and EKF simulations show improved vehicle trajectory tracking and demonstrate the EKF's potential for broader navigation system applications. This paper's novel contribution lies in its thorough analysis of the divergence issues in GPS navigation filter designs due to dynamic modeling inaccuracies, providing a systematic approach to addressing these challenges and offering new insights to improve estimation accuracy. [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]

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]