Search Results (569)

This paper investigates the viability of utilizing Fused Deposition Modeling (FDM) for the fabrication and follow-up testing of a hemispherical resonator (HR). This form of resonator has several significant applications, including the design of vibratory gyroscopes. While traditional high-precision resonators for this application rely on expensive fused-silica fabrication, this study proposes a macro-scale approach using Polylactic Acid (PLA) to enable accessible lab-scale experimentation. The specimens, featuring a unique central-hole mounting configuration, were designed in SolidWorks and analyzed via finite element methods to establish the modal hierarchy. Experimental Modal Analysis (EMA) was conducted using a Laser Doppler Vibrometer (LDV) to acquire vibration signals, which were then analyzed in NVGate, MATLAB, and MEscope to extract natural frequencies and quality factor. Results for a lab-scale HR specimen identified the n = 2 wine-glass mode with a deviation from theoretical natural frequency predictions largely attributed to inherent defects in the fabrication process. Furthermore, a frequency split of 2.15 Hz was observed due to the inherent asymmetries and mass imbalances of the fabrication method. The quality factor was evaluated via the ring-down method and validated using the half-power bandwidth (HPBW) technique. This work demonstrates that 3D-printed resonators serve as an effective, low-cost platform for isolating modal behaviors and optimizing geometric parameters before advancing to micro-scale fabrication. Full article

Health status monitoring is critical for rotating systems to maintain safety and reliability. However, it is hard to track the health status hidden in unobservable parameters. Therefore, to address the issue of hidden-parameter estimation caused by the absence of critical sensors, this paper proposes an extended reinforcement learning framework named the Lyapunov Actor–Critic Tutor (LAC-T). In this framework, the LAC-T redefines parameter estimation in hidden health status monitoring as an observation–trajectory alignment problem rather than an observation–tracking problem, thereby maintaining the stability of the hidden-parameter estimation. Meanwhile, to address the multi-resolution problem in parameter estimation, a tutor component is introduced into the framework to guide parameter estimation. To validate the effectiveness and superiority of the proposed LAC-T framework, a case study including a motor torque coefficient estimation for a rotating system in a control moment gyroscope is investigated in this paper. The lower estimation error of the proposed LAC-T framework compared with the candidate problem definition and models indicates that the observation–trajectory alignment problem definition can stabilise the hidden-parameter estimation, and the tutor component can improve estimation accuracy, thereby improving the performance of hidden health status monitoring. Full article

To improve operational safety in confined spaces, this study proposes an intelligent safety monitoring framework that utilizes multimodal data from wearable devices. The framework comprises two core components: a human activity recognition (HAR) module and a bio-inspired adaptive multimodal decision fusion (BA-MDF) module. The HAR module processes accelerometer and gyroscope data through an enhanced FFT–LSTM architecture that integrates time- and frequency-domain features for real-time activity classification. The BA-MDF module, inspired by biological multisensory integration mechanisms—particularly the inverse effectiveness principle observed in the superior colliculus—evaluates contextual risk by adaptively fusing HAR outputs, heart rate variability, and geospatial constraints without additional computational overhead. Experimental testing demonstrated 92.4% overall HAR accuracy and 94.3% identification accuracy for emergency scenarios under a simulated sensor degradation environment. These results validate the framework’s effectiveness in mitigating risks from anomalous events in visually constrained environments. Full article

Performance evaluation for fiber optic gyroscopes (FOG) has become an active research area in recent years. However, such evaluations are often challenged by imperfect data characterized by low quality and non-uniform distribution, which severely affects the accuracy of fiber optic gyroscope performance evaluations. To address this problem, an adaptive belief rule base (BRB) for data quality and distribution (ABRB-QD) method is proposed for modeling FOG performance evaluation under imperfect data. ABRB-QD effectively integrates data quality assessment and distribution adaptation into a unified belief rule structure. In this method, a data quality factor is introduced based on data stability to solve poor data quality issues. An adaptive membership function is established based on data distribution to transform input information, addressing non-uniform distribution problems in imperfect data. Furthermore, a parameter optimization model is developed to enhance the evaluation accuracy of ABRB-QD. Finally, to illustrate the effectiveness of the developed method, a case study of FOG performance evaluation is conducted. Full article

Accurately classifying gait abnormalities is crucial for the effective monitoring and rehabilitation of stroke patients. This study proposed a lightweight machine learning framework for distinguishing healthy from abnormal gait patterns using statistical features extracted from wearable gyroscope data. Statistical z-axis angular velocity values from both limbs were derived and used to evaluate the performance of multiple classifiers, including logistic regression, support vector machines, and ensemble methods. A leave-one-out cross-validation strategy was employed to enhance generalizability across subjects. The results indicated that several classifiers achieve accuracy and area under the curve (AUC) values exceeding 0.95, with random forest and support vector machine-based models demonstrating near-perfect class separability, with an AUC of 0.98. These findings highlighted the effectiveness of using minimal set of biomechanically relevant gyroscope features for gait classification in real-world healthcare applications. The proposed pipeline is computationally efficient, making it well suited for implementing in wearable and remote monitoring systems. Full article

Fighting against gravity is a common challenge for all terrestrial animals, including most mammals. It means, in particular, avoiding falls to the ground while performing daily tasks, such as standing up, locomotion, or foraging for food. This means that balance control in humans involves a wide variety of contexts and balance paradigms, such as upright standing, hand standing, tightrope walking, ice-skater spinning, bicycling, whole-body gesturing, and fingertip stick balancing, among others. From the cybernetic point of view, the underlying control problem is to keep the CoP (Center of Pressure) and the CoM (Center of Mass) aligned dynamically on the common vertical axis, and this means that the variety of balance strategies can be reduced to two basic paradigms: the CoP strategy (the CoP is the control variable and the CoM is the controlled variable) and the CoM strategy (the CoM is simultaneously the control and the controlled variable). It is suggested that the two balance strategies are implemented by combining four basic control paradigms, as a function of the task and environmental conditions: • Opportunistic control: exploitation of a physical phenomenon as the gyroscopic effect. • Stiffness control: exploitation of the elastic properties of skeletal muscles. • Feedback control: measuring an incipient fall index and closing the loop in real time. In particular, it is shown that a phase-space-based formulation of intermittent feedback control can compensate for the destabilization effect of conventional continuous control due to the large feedback delay. • Feedforward control: exploitation of an internal body model to generate stable whole-body synergies in an anticipatory manner. Such control paradigms are illustrated by summarizing the results of experimental and simulated data. Full article

The reliable identification of impairment relevant to safety-critical activities remains a significant challenge for public safety, motivating the exploration of unobtrusive and widely accessible sensing technologies. This study examines the viability of utilising inertial data acquired from consumer-grade smartphones to characterise gait disturbances associated with simulated visual impairment. The study simulates intoxication-related effects using alcohol-impairment goggles and does not involve the measurement of real alcohol intoxication. Two supervised experimental protocols were conducted in which participants traversed predefined walking routes under normal conditions and while wearing alcohol-impairment simulation goggles representing five manufacturer-declared blood alcohol concentration (BAC)-related goggle conditions plus a no-goggles control condition. An initial indoor trial, conducted in a structured corridor environment, yielded limited discrimination of gait dynamics due to strong spatial and visual stabilisation cues. To address this limitation, a subsequent outdoor experiment was conducted along a 100 m path lacking prominent visual reference points, resulting in motion patterns that more closely reflect unconstrained, real-world locomotion. Tri-axial accelerometer and gyroscope signals were recorded using smartphones, followed by artefact removal, segmentation, and standardisation to ensure inter-trial comparability. The resulting curated dataset comprises 290,919 multi-channel samples derived from 96 walking trials involving 16 participants and is released as an openly accessible resource to support further research in gait analysis and classification of gait alterations associated with simulated impairment. Model evaluation was performed using an 80/20 train–test split conducted within each traversal, with training and test windows originating from the same participant and walking session. Consequently, the reported results reflect within-subject performance instead of subject-independent generalisation. Multiple deep learning architectures combining convolutional feature extraction, bidirectional long short-term memory layers, and self-attention mechanisms were systematically evaluated. Using a subject-dependent evaluation protocol, the best-performing architecture achieved an accuracy of 71.4% and a weighted F1-score of 71.5% in distinguishing gait patterns associated with different levels of simulated visual impairment. The best-performing architectures yielded classification performance consistent with exploratory, low-stakes assessment of gait alterations associated with simulated visual impairment, using accelerometer data alone. These findings illustrate the feasibility of using smartphones as auxiliary tools for exploratory, low-stakes screening or educational applications and contribute a publicly released dataset and benchmark results to facilitate methodological advancement in inertial sensor-based gait impairment analysis. Full article

This paper presents a unified dynamic-modeling and error-analysis framework for an FTR (force-to-rebalanced)-operated MEMS ring gyroscope. Starting from an equivalent mass-point representation of the ring resonator, a dynamic model including stiffness and damping errors is first established. Principle-related inertial-acceleration errors and structural errors are then analyzed within the same framework. The results show that, under practical rate-measurement conditions, inertial-acceleration errors have negligible effects on both the drive and sense modes. In contrast, structural errors, including modal-frequency perturbation, damping-decay-time mismatch, mass-distribution mismatch, and electrode angular misalignment, impair drive-mode amplitude control and frequency tracking, introduce in-phase bias components into the sense-mode output, and produce quadrature signals through frequency coupling. The analysis further indicates that electrostatic mode matching should be implemented in two steps: quadrature-stiffness correction followed by modal-frequency tuning. The proposed model provides a concise and physically transparent basis for resonator design, parameter identification, and control compensation in high-performance MEMS ring gyroscopes. Full article

This paper presents the design and field evaluation of a low-cost automatic steering system for agricultural tractors. The system employs a PID controller whose gains are tuned using a metaheuristic optimization method. Core hardware includes an ESP32 microcontroller, an MPU9250 inertial measurement unit, a GPS module, and a servo motor for closed-loop yaw angle control, with a complementary filter fusing gyroscope and magnetometer data for robust heading estimation. Nine optimization algorithms were systematically compared: Grid Search, Random Search, Bayesian Optimization, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Salp Swarm Algorithm (SSA). A cost function combining overshoot and settling time was used. Step response analysis showed that WOA achieved the best performance, with an integral absolute error of 6.31°·s, a settling time of 2.15 s, and a minimal overshoot of 0.08°. In field tests on asphalt and farmland, the WOA-tuned system reduced lateral deviation by 69% (from 12.4 cm to 3.8 cm) and 67% (from 18.7 cm to 6.2 cm), respectively, compared to manual steering. Repeated-measures ANOVA and paired t-tests confirmed statistically significant improvements (p < 0.001) with large effect sizes (Cohen’s d > 2.7). The core components cost under $150 USD. The study offers a reproducible pipeline for comparative metaheuristic evaluation in agricultural vehicle guidance. Full article

We analyze noise in a quantum-enhanced fiber optic gyroscope (FOG), focusing on one of the leading sources of phase uncertainty—uncorrelated photon saturation. Taking a squeezed state input as a source for N00N states, we compute the uncorrelated false coincidence counts at the optimal phase bias and determine an upper limit to the squeezed amplitude $\xi$ which allows for sub-shot noise precision. As examples, we apply parameters of present-day quantum FOG experiments and determine the maximum possible precision enhancement based on their respective $\xi$ and optimal phase bias points. With the aim of supporting future FOG setups with higher N00N state fluxes, our result highlights the need to transition to multimode states to bypass the $\xi$ limitation, such as photon pairs generated by the dynamical Casimir effect. Full article

In complex marine environments, achieving low-cost, highly reliable, and continuous navigation is crucial for the intelligent and autonomous operation of unmanned surface vehicles (USVs). Currently, the integrated Global Navigation Satellite System and Strapdown Inertial Navigation System (GNSS/SINS) serves as the primary navigation architecture for USVs. While the cost of high-performance GNSS receivers has steadily decreased, high-precision SINS remains prohibitively expensive. Consequently, micro-electromechanical system (MEMS)-based SINS has emerged as a preferred alternative due to its favorable balance of cost, power consumption, and size. However, significant inertial sensor errors make it difficult to maintain high-precision positioning during GNSS outages. To address this limitation, the single-axis rotational inertial navigation system (SRINS) has been introduced. Nevertheless, constrained by the single-axis mechanical structure and complex sea state disturbances, the system still struggles to effectively modulate random errors and azimuth gyroscope drift, rendering it insufficient for highly demanding navigation tasks. To overcome these bottlenecks, this article systematically reviews four core technologies: (1) Comprehensive denoising and temperature drift compensation techniques for MEMS gyroscopes; (2) rapid moving-base initial alignment models under high sea state disturbances; (3) fast online calibration methods for azimuth gyroscope drift; and (4) adaptive and robust GNSS/SINS integration architectures capable of accommodating high-dynamic conditions and non-Gaussian interference. Finally, this article discusses the engineering conflict between deploying high-precision algorithms and the limited onboard computational capacity of USVs. It concludes by highlighting a highly promising navigation paradigm for future research: the integration of factor graph optimization with physics-informed deep learning. Full article

This paper provides a comprehensive overview of the progress in fiber-optic gyroscope technology, covering 260 key studies of the last ten years. A critical comparative analysis of fiber-optic gyroscope with alternative inertial sensors (Micro-Electro-Mechanical Systems, Hemispherical Resonator Gyroscope, Ring Laser Gyroscope) has been carried out. Confirming the unique advantages of fiber-optic gyroscope for autonomous navigation. Fundamental limitations of accuracy are considered in detail: temperature drifts, polarization noise, and Rayleigh backscattering. Modern hardware methods for suppressing these errors, including the use of photonic crystal and hollow fibers (Air-Core/Hollow-Core), are also considered in this work. The central place in the review is occupied by the analysis of the technological paradigm shift from bulky discrete circuits to hybrid integrated photonics (Indium Phosphide, Silicon Nitride, Lithium Niobate) and hybrid architectures to reduce weight and size characteristics. The role of artificial intelligence (Deep Learning, Long Short-Term Memory) methods in nonlinear drift compensation and calibration is discussed. The usage of the Brillouin effect and optomechanics promising areas are outlined, necessary to create a new generation of navigation systems operating in the absence of Global Navigation Satellite Systems signals. Full article

Seismic-induced torsional response remains a significant barrier to achieving resilient and sustainable building foundations, as traditional passive isolation systems often fail to regulate rotational motion effectively. This study examines an adaptive gyroscopic feedback-based foundation control system designed to provide automated torsional seismic mitigation. The proposed system integrates real-time angular velocity sensing using MEMS gyroscopes, Kalman filter state estimation, and an adaptive Linear Quadratic Regulator to modulate damping in response to changing ground motion. A single-degree-of-freedom torsional foundation model was developed and evaluated in GNU Octave 8.4.0/MATLAB R2024a Simulink using the recorded El Centro 1940 NS earthquake input. The adaptive controller achieved notable improvements, reducing total vibration energy by 69%, peak angular displacement by 47.6%, and RMS angular velocity by 39.5% relative to the uncontrolled case, while keeping control energy below 19% of the seismic input. These results demonstrate that gyroscopic feedback enhances damping, limits torsional resonance, and stabilises foundation behaviour under actual earthquake excitation. The system’s low energy requirement, compatibility with embedded hardware, and automated response characteristics underscore its potential for integration into sustainable and intelligent foundation designs. While results are demonstrated using the El Centro 1940 record as a benchmark, broader generalisation will be established through multi-record suites and uncertainty quantification in future work. The study highlights a feasible pathway for advancing automated seismic protection in buildings through active, sensor-driven torsional control. Full article

In order to mitigate the effects of micro-vibrations due to control moment gyroscopes (CMGs) on spacecraft attitude control system, they are often mounted on isolation platforms. However, the flexible deformation of isolators may cause certain disturbances in CMG gimbal servo systems. In addition, gimbal servo systems also suffer from intrinsic disturbances due to rotor imbalance and gimbal components. Since these disturbances are distributed over a wide frequency range, they are difficult to suppress and may seriously deteriorate gimbal control performance. To suppress multiple disturbances and improve gimbal speed accuracy, a composite anti-disturbance control method is proposed. The proposed method consists of two components. The first component adopts an adaptive proportional-integral-resonant controller with phase compensation to suppress disturbance due to isolator and rotor imbalance disturbance with improved transient performance. The second component adopts an adaptive extended state observer to estimate and then compensate slowly varying disturbances with improved dynamic performance and steady-state accuracy. By integrating these two components, the proposed method can effectively suppress multiple disturbances in CMG gimbal servo systems. Simulation and experimental results demonstrate the superior performance of the proposed method. Full article

The gimbal servo system in a control moment gyroscope (CMG) is critical for high-precision spacecraft attitude control, where comprehensive performance testing and evaluation are essential for ensuring spacecraft reliability and service life. Traditional motor testbenches exhibit limitations, whereas the electric motor emulator (EME) based on power electronic converters is a promising alternative for testing extreme operating conditions, such as ultra-low speed operation and fault scenarios. However, existing EME control methods suffer from limited system bandwidth and insufficient emulation accuracy, which limits their applicability. To address these issues, this paper proposes an improved current control strategy for the ultra-low speed permanent magnet synchronous motor (PMSM) emulator. First, a mathematical model of the EME based on the topology of the voltage source converter is established. Then, based on the deadbeat control concept, a deadbeat predictive current control (DPCC) strategy is developed to enhance the dynamic performance. Furthermore, to suppress the parameter mismatch disturbance, an optimization scheme based on a cascaded extended state observer (CESO) is introduced. The first-stage ESO is applied to estimate and compensate for total disturbances, while the second-stage ESO is a supplement to suppress the remaining disturbances in the EME system, which improves the robustness of the DPCC controller. Finally, the effectiveness of the improved emulation accuracy of the proposed method is verified through experiments. Full article