Search

With the continuous advancement of deep space exploration missions, the solar system boundary exploration mission is established as one of the China's most important deep space scientific exploration missions. The mission of the solar system boundary exploration has many challenges such as ultra-remote detection distance, ultra-long operation time, and ultra-long communication delay. Therefore, the problem of high-precision autonomous navigation needs to be solved urgently. This paper designs an autonomous intelligent navigation method based on X-ray pulsars in the cruise phase, which estimate the motion state of the probe in real time. The proposed navigation method employs the Q-learning Extended Kalman filter (QLEKF) to improve navigation accuracy during long periods of self-determining running. The QLEKF selects automatically the error covariance matrix parameter of the process noise and the measurement noise by the reward mechanism of reinforcement learning. Compared to the traditional EKF and AEKF, the QLEKF improves the estimation accuracy of position and velocity. Finally, the simulation result demonstrates the effectiveness and the superiority of the intelligent navigation algorithm based on QLEKF, which can satisfy the high-precision navigation requirements in the cruise phase of the solar system boundary exploration. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for real-time identification of four parameters of the permanent magnet synchronous machines (PMSM) namely stator resistance, d-axis inductance, q-axis inductance and the rotor flux linkage. The proposed method is based on the utilization of the deep neural network to solve the problems of the existing model-based parameter estimation methods, which are caused by the non-linearity of the inverter and the inaccuracy of the measured rotor position. Extensive numerical simulations and experimental studies have been conducted to evaluate the robustness and the accuracy of the proposed online parameters identification solution, compared with the conventional methods such as recursive least square, extended Kalman filter and Adaline neural network.

Abstract With the continuous advancement of deep space exploration missions, the solar system boundary exploration mission is established as one of the China's most important deep space scientific exploration missions. The mission of the solar system boundary exploration has many challenges such as ultra-remote detection distance, ultra-long operation time, and ultra-long communication delay. Therefore, the problem of high-precision autonomous navigation needs to be solved urgently. This paper designs an autonomous intelligent navigation method based on X-ray pulsars in the cruise phase, which estimate the motion state of the probe in real time. The proposed navigation method employs the Q-learning Extended Kalman filter (QLEKF) to improve navigation accuracy during long periods of self-determining running. The QLEKF selects automatically the error covariance matrix parameter of the process noise and the measurement noise by the reward mechanism of reinforcement learning. Compared to the traditional EKF and AEKF, the QLEKF improves the estimation accuracy of position and velocity. Finally, the simulation result demonstrates the effectiveness and the superiority of the intelligent navigation algorithm based on QLEKF, which can satisfy the high-precision navigation requirements in the cruise phase of the solar system boundary exploration.

Horse-rider interaction analysis by wearable sensors is a promising tool for monitoring equestrian training. In this paper, a body sensor network (BSN) based equestrian motion analysis system is developed, which combines bespoke inertial measurement units (IMU) and MindWave electroencephalography (EEG) acquisition equipment. To fuse the mechanical and EEG signals collected from the system, emotional and attitude information can be obtained to analyze the interaction between the rider and horse in equestrian training. For motion data fusion, a novel method, exercise intensity extend kalman filter (EID-EKF), is proposed, which can also reconstruct the riders’ posture in different gaits by establishing a biomechanical model. The accuracy of our method is verified with the optical system Vicon to support the motion capture for four riding styles (walking, sitting troth, rising trot, canter). Finally, the emotion changes of the riders with different levels are quantified, and kinematic analysis is carried out by combining with inertial and emotional information. It is concluded from the experiment results that the estimation errors are well controlled, and motion patterns acquired according to the kinematic analysis are consistent with the actual situation. [ABSTRACT FROM AUTHOR]

Copyright of Information & Control is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: Because it is necessary for electric vehicle (EV) driver to know the remaining power of the EV battery during driving, thus it is very valuable to research the precise state‐of‐charge estimation, which can update the residual capacity of battery and prevent the occurrence of overcharge and over discharge phenomenon for battery. As compared with most model‐based methods, neural network‐based methods can directly use battery surface temperature as the input parameters of the model. In here, the state‐of‐charge of LiNi0.6Co0.2Mn0.2O2/graphite batteries is estimated using the compound method with improved extended Kalman filter and long short‐term memory network. The improved extended Kalman filter algorithm is composed of recursive least square method with forgetting factor algorithm, generalized regression neural network and extended Kalman filter algorithm. For the compound state‐of‐charge estimation method, the state‐of‐charge values are first estimated using the improved extended Kalman filter, and then optimized using long short‐term memory network. The datasets of dynamic stress test at 25°C are applied to train the long short‐term memory network, while the datasets under different conditions are used as validation datasets. The results reveal that the mean absolute error, maximum absolute error, and root mean square error for their combination method are less than 1%, 3%, 1.1%, respectively. Among three kinds of state‐of‐charge estimation methods, the compound method has the highest estimation accuracy under different working conditions. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) is an attractive positioning technology due to its high precision and flexibility. However, the vulnerability of PPP brings a safety risk to its application in the field of life safety, which must be evaluated quantitatively to provide integrity for PPP users. Generally, PPP solutions are processed recursively based on the extended Kalman filter (EKF) estimator, utilizing both the previous and current measurements. Therefore, the integrity risk should be qualified considering the effects of all the potential observation faults in history. However, this will cause the calculation load to explode over time, which is impractical for long-time missions. This study used the innovations in a time window to detect the faults in the measurements, quantifying the integrity risk by traversing the fault modes in the window to maintain a stable computation cost. A non-zero bias was conservatively introduced to encapsulate the effect of the faults before the window. Coping with the multiple simultaneous faults, the worst-case integrity risk was calculated to overbound the real risk in the multiple fault modes. In order to verify the proposed method, simulation and experimental tests were carried out in this study. The results showed that the fixed and hold mode adopted for ambiguity resolution is critical to an integrity risk evaluation, which can improve the observation redundancy and remove the influence of the biased predicted ambiguities on the integrity risk. Increasing the length of the window can weaken the impact of the conservative assumption on the integrity risk due to the smoothing effect of the EKF estimator. In addition, improving the accuracy of observations can also reduce the integrity risk, which indicates that establishing a refined PPP random model can improve the integrity performance.

As the key technology for Internet of things, wireless sensor networks have received more attentions in recent years. Mobile localization is one of the significant topics in wireless sensor networks. In wireless sensor network, non-line-of-sight propagation is a common phenomenon leading to the growing non-line-of-sight error. It is a fatal impact for the localization accuracy of the mobile target. In this article, a novel method based on the nearest neighbor variable estimation is proposed to mitigate the non-line-of-sight error. First, the linear regression model of the extended Kalman filter is used to obtain the residual of the distance measurement value. After that, the residual analysis is used to complete the identification of the measurement value state. Then, by analyzing the statistical characteristics of the non-line-of-sight residual, the nearest neighbor variable estimation is proposed to estimate the probability density function of residual. Finally, the improved M-estimation is proposed to locate the mobile robot. Experiment results prove that the accuracy and robustness of the proposed algorithm are better than other methods in the mixed line-of-sight/non-line-of-sight environment. The proposed algorithm effectively inhibits the non-line-of-sight error. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)