Search Results (913)

In lithium-ion battery management systems (BMSs), accurate state of charge (SOC) estimation is essential for the stable operation of BMSs. Furthermore, the accuracy of SOC estimation is significantly influenced by the precision of battery model parameters. To improve the SOC estimation accuracy, this paper focuses on the second-order RC equivalent circuit model, firstly designs a simple and reliable improved adaptive forgetting factor (IAFF) regulation mechanism, and proposes the improved adaptive forgetting factor recursive least squares (IAFFRLS) algorithm, which not only improves the accuracy of parameter identification, but also exhibits excellent performance in anti-interference. Secondly, based on the identified model, a weighted multi-innovation improved Sage–Husa adaptive extended Kalman filter (WMISAEKF) algorithm is proposed to solve the problem of filter divergence caused by noise covariance updating. It fully utilizes historical innovations to reasonably allocate innovation weights to achieve accurate SOC estimation. Compared with the VFFRLS algorithm and AFFRLS algorithm, the IAFFRLS algorithm reduces the root mean square error (RMSE) by 29.30% and 19.29%, respectively, and the RMSE under noise interference is decreased by 82.37% and 78.59%, respectively. Based on the identified model for SOC estimation, the WMISAEKF algorithm reduces the RMSE by 77.78%, compared to the EKF algorithm. Furthermore, the WMISAEKF algorithm could still converge under different levels of noise interference and incorrect initial SOC values, which proves that the proposed algorithm has good stability and robustness. Simulation results verify that the parameter identification algorithm proposed in this paper demonstrates higher identification accuracy and anti-interference performance. The proposed SOC estimation algorithm has higher estimation accuracy and good robustness, which provides a new practical support for extending battery life. Full article

The State of Charge (SOC) is vital for battery system management. Enhancing SOC estimation boosts system performance. This paper presents a combined framework that improves SOC estimation’s accuracy and stability for electric vehicles. The framework combines a Long Short-Term Memory (LSTM) network with an Adaptive Unscented Kalman Filter (AUKF). An Improved Arithmetic Optimization Algorithm (IAOA) fine-tunes the LSTM’s hyperparameters. Its novelty lies in its adaptive iteration algorithm, which adjusts iterations based on a threshold, optimizing computational efficiency. It also integrates a genetic mutation strategy into the AOA to overcome local optima by mutating iterations. Additionally, the AUKF’s adaptive noise algorithm updates noise covariance in real-time, enhancing SOC estimation precision. The inputs of the proposed method include battery current, voltage, and temperature, then producing an accurate SOC output. The predictions of LSTM are refined through AUKF to obtain reliable SOC estimation. The proposed framework is firstly evaluated utilizing a public dataset and then applied to battery packs on actual engineering vehicles. Results indicate that the Root Mean Square Errors (RMSEs) of the SOC estimations in practical applications are below 0.6%, and the Maximum Errors (MAX) are under 3.3%, demonstrating the accuracy and robustness of the proposed combined framework. Full article

This paper proposes a novel reliable neural network control (NNC) method for active vibration control of uncertain structures. First, reliable model predictive control (MPC) was established by introducing nonprobabilistic reliability constraints into traditional MPC. An importance sampling strategy was established to improve the efficiency of the entire training process to achieve sufficient accuracy. An adaptive nonprobabilistic Kalman filter was further proposed for estimating the uncertain region of system states. Compared to existing reliability-based control methods, the proposed reliable NNC ensured structural safety across broader loads. Compared with reliable MPC, reliable NNC significantly reduced the online computational load, making it suitable for vibration control of high-frequency complex structural systems. The effectiveness and superiority of the proposed reliable NNC were validated through two numerical examples and experimental verification. Full article

The transfer alignment of strapdown inertial navigation systems (SINSs) is of great significance for improving the strike accuracy of airborne tactical vehicles. This study designed a new fuzzy logic-based adaptive filtering method by using the fuzzy logic theory to address the influence of system model error on the state estimation of the Kalman filter for SINS transfer alignment. It established the state error model and measurement error model, which were embedded with the state prediction residual and measurement residual, respectively, for SINS transfer alignment. The fuzzy rules were designed and introduced into the Kalman filtering framework to estimate the covariances of the system measurement and predicted state by minimizing their residuals to improve filtering accuracy for SINS transfer alignment. Simulation and experimentation together with associated comparative analyses were conducted, demonstrating that the proposed method can effectively handle the influence of system model error on SINS transfer alignment, and its accuracy is at least 18.83% higher than benchmark methods for transfer alignment. Full article

The popularity of keeping ornamental fish has grown increasingly, as their vibrant presence can provide a calming influence. Accurately assessing the health of ornamental fish is important but challenging. For this, researchers have focused on developing fish tracking methods that provide trajectories for health assessment. However, issues such as mirror images, occlusion, and motion prediction errors can significantly reduce the accuracy of existing algorithms. To address these problems, we propose a novel ornamental fish tracking method based on deep learning techniques. We first utilize the You Only Look Once (YOLO) v5 deep convolutional neural network algorithm with Distance Intersection over Union–Non Maximum Suppression (DIoU-NMS) to handle occlusion problems. We then design an object removal algorithm to eliminate fish mirror image coordinates. Finally, we adopt an improved DeepSORT algorithm, replacing the original Kalman Filter with an advanced Noise Scale Adaptive (NSA) Kalman Filter to enhance tracking accuracy. In our experiment, we evaluated our method in three simulated real-world fish tank environments, comparing it with the YOLOv5 and YOLOv7 methods. The results show that our method can increase Multiple Object Tracking Accuracy (MOTA) by up to 13.3%, Higher Order Tracking Accuracy (HOTA) by up to 10.0%, and Identification F1 Score by up to 14.5%. These findings confirm that our object removal algorithm effectively improves Multiple Object Tracking Accuracy, which facilitates early disease detection, reduces mortality, and mitigates economic losses—an important consideration given many owners’ limited ability to recognize common diseases. Full article

Global Navigation Satellite System (GNSS) receivers may produce measurement outliers in real-world applications owing to various circumstances, including poor signal quality, multipath effects, data loss, satellite signal loss, or electromagnetic interference. This can lead to a noise distribution that is non-Gaussian heavy-tailed, affecting the effectiveness of satellite navigation filters. This paper presents a robust Extended Kalman Filter (EKF) based on the Maximum Correntropy Criterion with a Student’s t kernel (STMCCEKF) for GPS navigation under non-Gaussian noise. Unlike traditional EKF and Gaussian-kernel MCCEKF, the proposed method enhances robustness by leveraging the heavy-tailed Student’s t kernel, which effectively suppresses outliers and dynamic observation noise. A fixed-point iterative algorithm is used for state update, and a new posterior error covariance expression is derived. The simulation results demonstrate that STMCCEKF outperforms conventional filters in positioning accuracy and robustness, particularly in environments with impulsive noise and multipath interference. The Student’s t-distribution kernel efficiently mitigates heavy-tailed non-Gaussian noise, while it adaptively adjusts process and measurement noise covariances, leading to improved estimation performance. A detailed explanation of several key concepts along with practical examples are discussed to aid in understanding and applying the Global Positioning System (GPS) navigation filter. By integrating cutting-edge reinforcement learning with robust statistical approaches, this work advances adaptive signal processing and estimation, offering a significant contribution to the field. Full article

Model-based sensorless control of interior permanent-magnet synchronous motors (IPMSMs) typically employs an estimation observer with embedded position information, followed by a position extraction process. Although a type-2 phase-locked loop (PLL) is widely adopted for position and speed extraction, it suffers from steady-state tracking errors under variable-speed operation, leading to torque bias in IPMSM torque control. To mitigate this issue, this paper first proposes an adaptive bandpass full-order observer in the stationary reference frame. Subsequently, a Kalman filter (KF)-based compensation strategy is introduced for the PLL to eliminate tracking errors while maintaining system stability. Experimental validation on a 300 kW platform confirms the effectiveness of the proposed sensorless torque control algorithm, demonstrating significant reductions in position error and torque fluctuations during acceleration and deceleration. Full article

The exponential growth of autonomous systems demands robust security mechanisms that can operate within the extreme constraints of real-time embedded environments. This paper introduces SMART DShot, a groundbreaking machine learning-enhanced framework that transforms the security landscape of unmanned aerial vehicle motor control systems through seamless integration of adaptive timing correction and real-time anomaly detection within Digital Shot (DShot) communication protocols. Our approach addresses critical vulnerabilities in Electronic Speed Controller (ESC) interfaces by deploying four synergistic algorithms—Kalman Filter Timing Correction (KFTC), Recursive Least Squares Timing Correction (RLSTC), Fuzzy Logic Timing Correction (FLTC), and Hybrid Adaptive Timing Correction (HATC)—each optimized for specific error characteristics and attack scenarios. Through comprehensive evaluation encompassing 32,000 Monte Carlo test iterations (500 per scenario × 16 scenarios × 4 algorithms) across 16 distinct operational scenarios and PolarFire SoC Field-Programmable Gate Array (FPGA) implementation, we demonstrate exceptional performance with 88.3% attack detection rate, only 2.3% false positive incidence, and substantial vulnerability mitigation reducing Common Vulnerability Scoring System (CVSS) severity from High (7.3) to Low (3.1). Hardware validation on PolarFire SoC confirms practical viability with minimal resource overhead (2.16% Look-Up Table utilization, 16.57 mW per channel) and deterministic sub-10 microsecond execution latency. The Hybrid Adaptive Timing Correction algorithm achieves 31.01% success rate (95% CI: [30.2%, 31.8%]), representing a 26.5% improvement over baseline approaches through intelligent meta-learning-based algorithm selection. Statistical validation using Analysis of Variance confirms significant performance differences (F(3,1996) = 30.30, p < 0.001) with large effect sizes (Cohen’s d up to 4.57), where 64.6% of algorithm comparisons showed large practical significance. SMART DShot establishes a paradigmatic shift from reactive to proactive embedded security, demonstrating that sophisticated artificial intelligence can operate effectively within microsecond-scale real-time constraints while providing comprehensive protection against timing manipulation, de-synchronization, burst interference, replay attacks, coordinated multi-channel attacks, and firmware-level compromises. This work provides essential foundations for trustworthy autonomous systems across critical domains including aerospace, automotive, industrial automation, and cyber–physical infrastructure. These results conclusively demonstrate that ML-enhanced motor control systems can achieve both superior security (88.3% attack detection rate with 2.3% false positives) and operational performance (31.01% timing correction success rate, 26.5% improvement over baseline) simultaneously, establishing SMART DShot as a practical, deployable solution for next-generation autonomous systems. Full article

Flexibility is advocated as an effective solution to address the growing need to alleviate grid congestion, necessitating efficient energy management strategies for industrial operations. This paper presents a mixed-integer linear programming (MILP)-based optimization framework for a flexible asset in an industrial setting, aiming to minimize operational costs and enhance energy efficiency. The method integrates dynamic pricing and real-time grid analysis, alongside a state estimation model using Extended Kalman Filtering (EKF) that improves the accuracy of system state predictions. Model Predictive Control (MPC) is employed for real-time adjustments. A real-world case studies from aquaculture industries and industrial power grids in Denmark demonstrates the approach. By leveraging dynamic pricing and grid signals, the system enables adaptive pump scheduling, achieving a 27% reduction in energy costs while maintaining voltage stability within 0.95–1.05 p.u. and ensuring operational safety. These results confirm the effectiveness of grid-aware, flexible control in reducing costs and enhancing stability, supporting the transition toward smarter, sustainable industrial energy systems. Full article

The low-cost Strapdown Inertial Navigation System (SINS)/Global Navigation Satellite System (GNSS) is widely used in autonomous vehicles for positioning and navigation. Initial alignment is a critical stage for SINS operations, and the alignment time and accuracy directly affect the SINS navigation performance. To address the issue that low-cost SINS/GNSS cannot effectively achieve rapid and high-accuracy alignment in complex environments that contain noise and external interference, an adaptive multiple backtracking robust alignment method is proposed. The sliding window that constructs observation and reference vectors is established, which effectively avoids the accumulation of sensor errors during the full integration process. A new observation vector based on the magnitude matching is then constructed to effectively reduce the effect of outliers on the alignment process. An adaptive multiple backtracking method is designed in which the window size can be dynamically adjusted based on the innovation gradient; thus, the alignment time can be significantly shortened. Furthermore, the modified variational Bayesian Kalman filter (VBKF) that accurately adjusts the measurement noise covariance matrix is proposed, and the Expectation–Maximization (EM) algorithm is employed to refine the prior parameter of the predicted error covariance matrix. Simulation and experimental results demonstrate that the proposed method significantly reduces alignment time and improves alignment accuracy. Taking heading error as the critical evaluation indicator, the proposed method achieves rapid alignment within 120 s and maintains a stable error below 1.2° after 80 s, yielding an improvement of over 63% compared to the backtracking-based Kalman filter (BKF) method and over 57% compared to the fuzzy adaptive KF (FAKF) method. Full article

Inertial navigation systems (INSs) exhibit distinctive characteristics, such as long-duration operation, full autonomy, and exceptional covertness compared to other navigation systems. However, errors are accumulated over time due to operational principles and the limitations of sensors. To address this problem, this study theoretically explores a numerically simulated integrated inertial navigation system consisting of a single-axis cold atom interferometer gyroscope (CAIG) and a conventional inertial measurement unit (IMU). The system leverages the low bias and drift of the CAIG and the high sampling rate of the conventional IMU to obtain more accurate navigation information. Furthermore, an adaptive gradient ascent (AGA) method is proposed to estimate the variance of the measurement noise online for the Kalman filter. It was found that errors of latitude, longitude, and positioning are reduced by 43.9%, 32.6%, and 32.3% compared with the conventional IMU over 24 h. On this basis, errors from inertial sensor drift could be further reduced by the online Kalman filter. Full article

In this paper, a joint Matching Field Processing (MFP) Algorithm based on horizontal uniform circular array (UCA) is proposed for three-dimensional position of underwater wrecked targets. Firstly, a Marine search and rescue position model based on Minimum Variance Distortionless Response (MVDR) and matching field quadratic joint Algorithm was proposed. Secondly, an MVDR beamforming method based on pre-Kalman filtering is designed to refine the real-time DOA estimation of the desired signal and the interference source, and the sound source azimuth is determined for prepositioning. The antenna array weights are dynamically adjusted according to the filtered DOA information. Finally, the Adaptive Matching Field Algorithm (AMFP) used the DOA information to calculate the range and depth of the lost target, and obtained the range and depth estimates. Thus, the 3D position of the lost underwater target is jointly estimated. This method alleviates the angle ambiguity problem and does not require a computationally intensive 2D spectral search. The simulation results show that the proposed method can better realise underwater three-dimensional positioning under certain signal-to-noise ratio conditions. When there is no error in the sensor coordinates, the positioning error is smaller than that of the baseline method as the SNR increases. When the SNR is 0 dB, with the increase in the sensor coordinate error, the target location error increases but is smaller than the error amplitude of the benchmark Algorithm. The experimental results verify the robustness of the proposed framework in the hierarchical ocean environment, which provides a practical basis for the deployment of rapid response underwater positioning systems in maritime search and rescue scenarios. Full article

To address the degradation in localization accuracy caused by insufficient robustness of filter parameters and inefficient multi-trajectory data fusion in dynamic environments, this paper proposes a Kalman filter-based localization calibration method optimized by reinforcement learning and information matrix fusion (RL-IMKF). An actor–critic reinforcement learning network is designed to adaptively adjust the state covariance matrix, enhancing the Kalman filter’s adaptability to environmental changes. Meanwhile, a multi-trajectory information matrix fusion strategy is introduced, which aggregates multiple trajectories in the information domain via weighted inverse covariance matrices to suppress error propagation and improve system consistency. Experiments using both simulated and real-world sensor data demonstrate that the proposed method outperforms traditional extended Kalman filter approaches in terms of localization accuracy and stability, providing a novel solution for cooperative localization calibration of unmanned aerial vehicle (UAV) swarms in dynamic environments. Full article

This paper presents a novel data-driven framework for real-time State of Charge (SOC) estimation in lithium-ion battery systems using a data-driven Prognostics and Health Management (PHM) approach. The method leverages an optimized bidirectional Long Short-Term Memory (Bi-LSTM) network, trained with enhanced datasets filtered via exponentially weighted moving averages (EWMAs) and refined through SHAP-based feature attribution. Compared against a Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) across ten diverse drive cycles, the proposed model consistently achieved superior performance, with mean absolute errors (MAEs) as low as 0.40%, outperforming EKF (0.66%) and UKF (1.36%). The Bi-LSTM model also demonstrated higher R² values (up to 0.9999) and narrower 95% confidence intervals, confirming its precision and robustness. Real-time implementation on embedded platforms yielded inference times of 1.3–2.2 s, validating its deployability for edge applications. The framework’s model-free nature makes it adaptable to other nonlinear, time-dependent systems beyond battery SOC estimation. Full article

To investigate the influence of different water quality conditions on the underwater transmission performance of laser communication signals, this paper systematically analyzes the absorption and scattering characteristics of the underwater laser communication channel, and constructs a transmission model of laser propagation in water, so as to explore the transmission influence mechanism under typical water quality environments. On this basis, a system of in situ measurements for underwater laser channel attenuation is designed and constructed, and several sets of experiments are carried out to verify the rationality and applicability of the model. The collected experimental data are denoised by the fusion of wavelet analysis and adaptive Kalman filtering (DWT-AKF in short) algorithm, and compared with the data measured by an underwater hyperspectral Absorption Coefficient Spectrophotometer (ACS in short), which shows that the channel attenuation coefficients of the model inversion and the measured values are in high agreement. The research results provide a reliable theoretical basis and experimental support for the performance optimization and engineering design of the underwater laser communication system. Full article