Метод подавления случайных шумов инерциальных датчиков на основе комплексирования AR-модели и адаптивного фильтра Калмана типа SRUKF при начальной выставке БИНС на неподвижном основании

Introduction. In the gyrocompassing mode, the initial heading angle of a platformless inertial navigation system (PINS) is determined based on the data obtained from accelerometers and gyroscopes that measure the projections of the gravitational acceleration vector and the Earth’s angular velocity vector on the axes of the body coordinates system in the PINS initial stationary mode. Due to unavoidable circumstances, such as bias instability and random noise in the accelerometer and gyroscope signals, much time is required to obtain the sufficient amount of sensor data for achieving the necessary accuracy of useful measurement values by the averaging method. In this context, in order to reduce the time of the gyrocompassing mode, data processing methods should be used to eliminate the bias instability and random noise in the signals received from PINS inertial sensors.Aim. To develop a method for suppressing random noise and reducing bias instability in the signals of inertial sensors, thereby reducing the time of the gyrocompassing mode of PINS and providing for the required accuracy of its initial heading angle determination.Materials and methods. An autoregressive (AR) model was used to simulate random noise in the measured sensor signals followed by its filtering using a Sage-window square-root unscented Kalman filter (SW-SRUKF).Results. A mathematical model describing random noise in the PINS inertial sensors in the stationary mode was derived. A methodology for suppressing random noise was proposed. The effectiveness of the proposed method was tested on actual data, with the results presented in the form of figures and tables.Conclusion. A method for eliminating the bias instability and random noise of PINS accelerometers and gyroscopes was proposed based on AR model and SW-SRUKF. The accuracy and effectiveness of the proposed method was confirmed by processing actual inertial sensor data. The results obtained are significant for reducing the initial alignment time of a PINS in the gyrocompassing mode.Введение. В режиме гирокомпасирования начальный угол курса бесплатформенной инерциальной навигационной системы (БИНС) определяется на основе данных акселерометров и гироскопов, измеряющих проекции вектора гравитационного ускорения и вектора угловой скорости вращения Земли на оси связанной системы координат в начальном неподвижном режиме работы БИНС. Из-за неизбежного наличия нестабильности смещения нуля и случайных шумов в сигналах акселерометров и гироскопов требуется длительное время для получения достаточного объема данных датчиков, чтобы достичь требуемой точности определения полезных измеряемых значений методом усреднения. Поэтому, чтобы сократить время режима гирокомпасирования, необходимо использовать методы обработки данных для снижения нестабильности смещения нуля и случайных шумов в полученных от инерциальных датчиков БИНС сигналах.Цель работы. Разработка метода подавления случайных шумов и уменьшения нестабильности смещения нуля в сигналах инерциальных датчиков, благодаря чему сокращается время режима гирокомпасирования БИНС при обеспечении требуемой точности определения ее начального угла курса.Материалы и методы. Используется модель авторегрессии (англ. autoregressive – AR) для построения математической модели случайных шумов в сигналах датчиков, затем эти шумы фильтруются путем использования фильтра Калмана типа SKURF (англ. Square-Root Unscented Kalman Filter) с применением Sage-окна (англ. Sage window Square-Root Unscented Kalman Filter – SW-SRUKF).Результаты. Математическая модель случайных шумов инерциальных датчиков в неподвижном режиме. Алгоритм подавления случайных шумов. Результаты обработки реальных данных в виде рисунков и таблиц для апробации эффективности предложенного метода.Заключение. Предлагается метод шумоподавления для снижения нестабильности смещения нуля и случайных шумов акселерометров и гироскопов БИНС путем комплексирования AR-модели и SW-SRUKF. Корректность и эффективность предложенного метода подтверждена результатами обработки реальных данных с инерциальных датчиков. Полученные результаты значимы для сокращения времени начальной выставки БИНС в режиме гирокомпасирования

A novel adaptive state of charge estimation method of full life cycling lithium-ion batteries based on the multiple parameter optimization.

The state of charge (SoC) estimation is the safety management basis of the packing lithium-ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to describe its working characteristics by using the state-space description, in which the optimization strategy of the circuit structure is studied by using the aspects of equivalent mode, analog calculation, and component distribution adjustment, revealing the mathematical expression mechanism of different structural characteristics. A novel particle adaptive unscented Kalman filtering algorithm is introduced for the iterative calculation to explore the working state characterization mechanism of the packing LIB, in which the incorporate multiple information is considered and applied. The adaptive regulation is obtained by exploring the feature extraction and optimal representation, according to which the accurate SoC estimation model is constructed. The state of balance evaluation theory is explored, and the multiparameter correction strategy is carried out along with the experimental working characteristic analysis under complex conditions, according to which the optimization method is obtained for the SoC estimation model structure. When the remaining energy varies from 10% to 100%, the tracking voltage error is less than 0.035 V and the SoC estimation accuracy is 98.56%. The adaptive working state estimation is realized accurately, which lays a key breakthrough foundation for the safety management of the LIB packs

FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved

FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved

On the Enhancement of the Localization of Autonomous Mobile Platforms

The focus of many industrial and research entities on achieving full robotic autonomy increased in the past few years. In order to achieve full robotic autonomy, a fundamental problem is the localization, which is the ability of a mobile platform to determine its position and orientation in the environment. In this thesis, several problems related to the localization of autonomous platforms are addressed, namely, visual odometry accuracy and robustness; uncertainty estimation in odometries; and accurate multi-sensor fusion-based localization. Beside localization, the control of mobile manipulators is also tackled in this thesis. First, a generic image processing pipeline is proposed which, when integrated with a feature-based Visual Odometry (VO), can enhance robustness, accuracy and reduce the accumulation of errors (drift) in the pose estimation. Afterwards, since odometries (e.g. wheel odometry, LiDAR odometry, or VO) suffer from drift errors due to integration, and because such errors need to be quantified in order to achieve accurate localization through multi-sensor fusion schemes (e.g. extended or unscented kalman filters). A covariance estimation algorithm is proposed, which estimates the uncertainty of odometry measurements using another sensor which does not rely on integration. Furthermore, optimization-based multi-sensor fusion techniques are known to achieve better localization results compared to filtering techniques, but with higher computational cost. Consequently, an efficient and generic multi-sensor fusion scheme, based on Moving Horizon Estimation (MHE), is developed. The proposed multi-sensor fusion scheme: is capable of operating with any number of sensors; and considers different sensors measurements rates, missing measurements, and outliers. Moreover, the proposed multi-sensor scheme is based on a multi-threading architecture, in order to reduce its computational cost, making it more feasible for practical applications. Finally, the main purpose of achieving accurate localization is navigation. Hence, the last part of this thesis focuses on developing a stabilization controller of a 10-DOF mobile manipulator based on Model Predictive Control (MPC). All of the aforementioned works are validated using numerical simulations; real data from: EU Long-term Dataset, KITTI Dataset, TUM Dataset; and/or experimental sequences using an omni-directional mobile robot. The results show the efficacy and importance of each part of the proposed work

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen