Aiming at the low-cost, wide-range, and accurate measurement requirement for Microelectromechanical System (MEMS) Inertial Measurement Unit (IMU) on a multi-rotor Unmanned Aerial Vehicle (UAV), the paper designs a heterogeneous parallel redundancy configuration scheme. In redundant MEMS IMUs, a high-cost and small-range MEMS gyroscope is combined with low-cost and large-range MEMS gyroscopes. Then, an adaptive data fusion method of redundant MEMS gyroscopes is proposed. By the designed experiments based on the simulation data and the sensor measurement data, the proposed method has been proved that it can effectively improve the angular rate measurement performance of the multi-rotor UAV and broaden the angular rate measurement range on the basis of saving the configuration cost and volume of the micro IMU.

In this paper, we propose an improved torque sensorless speed control method for electric assisted bicycle, this method considers the coordinate conversion. A low-pass filter is designed in disturbance observer to estimate and compensate the variable disturbance during cycling. A DC motor provides assisted power driving, the assistance method is based on the real-time wheel angular velocity and coordinate system transformation. The effect of observer is proved, and the proposed method guarantees stability under disturbances. It is also compared to the existing methods and their performances are illustrated through simulations. The proposed method improves the performance both in rapidity and stability.

In this paper, we describe a rotation angle measurement system (RAMS) based on an inertial measurement unit (IMU) developed to measure the rotation angle of a movable surface. The existing IMU-based attitude (tilt) sensor can only accurately measure the rotation angle when the rotation axis of the movable surface is perfectly aligned with the X axis or Y axis of the sensor, which is always not possible in real-world engineering. To overcome the difficulty of sensor installation and ensure measurement accuracy, first, we build a model to describe the relationship between the rotation axis and the IMU. Then, based on the built model, we propose a simple online method to estimate the direction of the rotation axis without using a complicated apparatus and a method to estimate the rotation angle using the known rotation axis based on the extended Kalman filter (EKF). Using the estimated rotation axis direction, we can effectively eliminate the influence of the mounting position on the measurement results. In addition, the zero-velocity detection (ZVD) technique is used to ensure the reliability of the rotation axis direction estimation and is used in combination with the EKF as the switching signal to adaptively adjust the noise covariance matrix. Finally, the experimental results show that the developed RAMS has a static measurement error of less than 0.05° and a dynamic measurement error of less than 1° in the range of ±180°.

Accurately estimating single-axis rotational angle changes is crucial in many high-tech domains. However, traditional angle measurement techniques are often constrained by sensor limitations and environmental interferences, resulting in significant deficiencies in precision and stability. Moreover, current methodologies typically rely on fixed-axis rotation models, leading to substantial discrepancies between measured and actual angles due to axis misalignment. To address these issues, this paper proposes an innovative method for single-axis rotational angle estimation. It introduces a calibration technique for installation errors between inertial measurement units and the overall measurement system, effectively translating dynamic rotational inertial outputs to system enclosure outputs. Subsequently, the method employs triaxial accelerometers combined with zero-velocity detection technology to estimate the rotation axis position. Finally, it delves into analyzing the relationship between quaternion and axis–angle, aimed at reducing noise interference for precise rotational angle estimation. Based on this proposed methodology, a Low-Cost, a High Accuracy Measurement System (HAMS) integrating sensor fusion was designed and implemented. Experimental results demonstrate static measurement errors below ±0.15° and dynamic measurement errors below ±0.5° within a ±180° range.

In this work, we aimed to determine the nonlinear disturbance caused by cascaded coupling rigid–flexible deformation and friction in a direct-driven servo electric cylinder terminal positioning system (DDSEC-TPS) during feed motion of an intermittent, reciprocating, and time-varying load. For this purpose, a cascaded coupling dynamic error model of DDSEC-TPS was established based on the position–pose error model of the parallel motion platform and the rotor field-oriented vector transform. Then, a model to observe the dynamic error of the DDSEC-TPS was established using the improved beetle antennae search algorithm backpropagation neural network (IBAS-BPNN) prediction model according to the rigid–flexible deformation error theory of feed motion, and the observed dynamic error was compensated for in the vector control strategy of the DDSEC-TPS. The length and error prediction models were trained and validated using opposite and mixed datasets tested on the experimental platform, to observe dynamic errors and evaluate and optimize the prediction models. The experimental results show that dynamic error compensation can improve the position tracking accuracy of the DDSEC-TPS and the position–pose performance of the parallel motion platform. This study is of great significance for improving the consistency of following multiple DDSEC-TPSs and the position–pose accuracy of parallel motion platforms.

Accurate indoor localization is critical for emerging Internet of Things and smart environment applications. Channel State Information (CSI), with its fine spatial resolution and high sensitivity to environmental changes, offers strong potential in Wi-Fi-based indoor localization. However, practical deployment is hindered by multipath interference and device heterogeneity, which degrade positioning accuracy and system robustness. To address these challenges, this paper proposes a deep learning-enhanced CSI acquisition and processing framework, termed MSRANet. The framework incorporates parallel multi-scale temporal convolutions to capture hierarchical and fine-grained features, deep residual blocks for enhanced feature representation, and a dual attention mechanism to refine frequency and spatial features. A lightweight Weighted Moving Average module is also introduced to smooth prediction outputs and improve stability. The proposed system is implemented on low-cost embedded hardware and evaluated in multiple real-world indoor environments. Experimental results show a mean absolute error of around 0.7 meters, demonstrating improved positioning accuracy and robustness compared to existing methods, while maintaining computational efficiency suitable for real-time deployment.