Stability Of Algorithm Research Articles

Redesign of a Towing Mobile Robot Control Architecture and Implementation of Outdoor Experiments Using the Transverse Function Approach

This article discusses the redesign of a towing mobile robot to obtain a modern system for implementing mobile robot control research. Controller architecture issues are presented, and a selected control algorithm is considered in detail. The reconstruction of the robot is also intended to ensure that the current standards for the electronic architecture controlling the robot are met and that this architecture can be easily developed to include components related to the safety of the robot’s operation. The discussion of the control architecture is divided into a description of the high-level controller responsible for the position stabilization algorithm and a description of the low-level controller responsible for the drive motor control and robot safety. The high-level control algorithm is responsible for a trajectory tracking task realized with use of a transverse function approach algorithm. A time elastic band algorithm was also used to generate a reference trajectory, allowing the robot to be guided through waypoints. The low-level controller is comprehensively described with details on the industrial controller architecture used, the communication between the controller modules, and the interaction of these modules with the on-board computer. The redesign of the towing mobile robot was summarized by the implementation of outdoor experiments where the task of driving through reference points was completed.

An unsupervised video stabilization algorithm based on gyroscope image fusion

An unsupervised video stabilization algorithm based on gyroscope image fusion

Regenerative Braking Energy Flow Control Algorithm for Power Grid Voltage Stabilization in Mobile Energy Storage Systems

Regenerative Braking Energy Flow Control Algorithm for Power Grid Voltage Stabilization in Mobile Energy Storage Systems

Improved Campus Vehicle Detection Method Based on YOLOv11 and Grayscale Projection-Based Electronic Image Stabilization Algorithm

Improved Campus Vehicle Detection Method Based on YOLOv11 and Grayscale Projection-Based Electronic Image Stabilization Algorithm

The problem of satellite attitude stabilization in the geomagnetic field

A satellite moving along a circular Keplerian orbit in near-Earth space was explored, focusing on its attitude stabilization in the orbital coordinate system using the intrinsic magnetic and Lorentz force moments. Fluctuations in geomagnetic induction that occur as the satellite orbits cause the coefficients in the dynamical equations governing the satellite’s attitude motion to vary over time. The results show that, although the linearized system of differential equations of the satellite’s motion is non-stationary, it can be reduced to a stationary system of higher order, which holds even for high-precision multipole models of the geomagnetic field. Thus, a control law design was proposed to stabilize the satellite. The controllability of the system was analyzed, and an optimal stabilization algorithm based on the LQR method was developed. The effectiveness of the proposed approach was validated by computer modeling.

Method of testing the software of spacecraft electronic equipment based on fault injection in the algorithms of orientation and stabilization

The purpose of the work is to develop and describe a new method of testing spacecraft electronic equipment. The article substantiates the relevance of developing new methods and means of testing on-board electronic equipment of spacecraft based on the intentional introduction of faults for the purpose of testing survivability algorithms. It was shown that carrying out such tests makes it possible to increase the completeness of control and reliability of on-board electronic equipment of spacecraft while simultaneously reducing testing costs. A new method for testing spacecraft software based on fault injection is proposed, which is used to solve the specific task of checking orientation and stabilization modes. The proposed method makes it possible to develop semi-natural models of on-board electronic equipment of increased adequacy and reconfigurability due to the use of programmable logic integrated circuits as the basis of hardware and software complexes. The proposed method is implemented on the hardware and software of a ground-based debugging complex for on-board electronic equipment and is distinguished by the ability to simulate a wide range of on-board electronic equipment, low cost and mobility. All technical solutions described in the article were introduced into the production process when creating modern spacecraft for communications, radio navigation and geodesy.

Stability and Fault Detection Algorithm of Grid-Connected Inverter in Complex Power Grid Environment

In order to improve the stability, this paper designs a control strategy based on adaptive grid impedance. By monitoring the grid impedance in real time and dynamically adjusting the inverter control parameters, the influence of grid impedance change on inverter stability is effectively reduced. At the same time, LCL filter is introduced, which significantly filters out the higher harmonics in the inverter output current and improves the output waveform quality. In addition, a voltage compensation device is applied to quickly generate a compensation voltage signal when the grid voltage fluctuates to ensure the stability of the inverter output voltage. In the aspect of fault detection, a data-driven fault detection algorithm is proposed, which combines machine learning and real-time monitoring technology to realize early detection and accurate diagnosis of inverter faults by collecting and analyzing inverter operation data. The experimental results show that the algorithm performs well in the detection of power switch device faults, sensor faults and other types, and has high classification accuracy. The research shows that the control strategy based on adaptive grid impedance, the addition of LCL filter and the application of voltage compensation device have significantly improved the stability of grid-connected inverter in complex grid environment. The proposed data-driven fault detection algorithm provides strong support for rapid fault location and accurate treatment of inverter.

Correction: An Efficient Slope Stability Algorithm with Physically Consistent Parametrisation of Slip Surfaces

Correction: An Efficient Slope Stability Algorithm with Physically Consistent Parametrisation of Slip Surfaces

Development Study of Direct Damage Stability Algorithm Based on 3D Geometry Data Processing for Onboard Loading Instrument

Abstract The maritime world experienced a very significant development such as the process of cargo planning is the existence of a system called loading instrument which is regulated by several regulations from both Classification and IACS. Damage stability calculation is also very important for loading instruments. Currently in Indonesia, there is no known algorithm to calculate direct damage stability from 3D models. And also there is still no loading instrument in Indonesia that applies the algorithm. Therefore, this research aims to develop a direct damage stability calculation algorithm for the development of loading instruments in Indonesia. Direct damage stability uses the lost buoyancy method which is calculated from 3D geometry model directly. The calculation process starts by dividing the ship geometry and compartment with the desired station. In general, the numerical integration used in the calculation process is the Simpson method. Then Newton’s iteration is used to find an equilibrium condition by predetermined conditions. Finally, the calculation results will be compared with the cases of LR Rules and IACS Rules UR L5 as a form of validation. The calculation algorithm can later be applied to the onboard loading software computer needed by the ship for the loading process with damage applications.

Multi-Sensor Fusion and Deep Learning: A New Frontier in Stabilization Algorithm Optimization

The rapid development of automation technology has highlighted the importance of stabilization algorithms in ensuring the stability and functionality of smart devices. These algorithms are critical for maintaining balance and precision, especially in applications such as unmanned aerial vehicles (UAVs), handheld devices, and autonomous systems. This research focuses on the application of stabilization algorithms across various domains, analyzing their principles, advantages, limitations, and potential optimization strategies. The study employs a literature review to investigate the integration of multi-sensor fusion and deep learning techniques to improve system stability. It shows that multi-sensor fusion significantly enhances system robustness by mitigating external disturbances, while deep learning improves autonomous decision-making in dynamic environments, particularly in UAV path planning and task execution. The findings confirm the crucial role of stabilization algorithms in modern systems and provide insights into future optimization directions. By combining deep learning with sensor fusion, future systems are expected to achieve greater stability and autonomy, contributing to the advancement of UAVs and other intelligent systems.

An approach for accurately extracting vehicle trajectory from aerial videos based on computer vision

Vehicle trajectory data holds valuable information for advanced driving development and traffic analysis. While unmanned aerial vehicle (UAV) offer a broader perspective, the detection of small-scale vehicles in video frames still suffers from low accuracy or is even missed. This study proposes a comprehensive technical framework for accurate vehicle trajectory extraction, encompassing six main components: video stabilization, vehicle detection, vehicle tracking, lane marking detection, coordinate transformation, and data denoising. To mitigate video jitter, the SURF and FLANN stabilization algorithms are utilized. An enhanced detector based on You Only Look Once X (YOLOX) is employed for multi-target vehicle detection, incorporating a shallow feature extraction module within the detection head to improve the performance for low-level and small-scale features. Efficient Channel Attention (ECA) modules are integrated before the neck to further boost the expressiveness. Additionally, a sliding window inference method is applied at the input stage to prevent compression of high-resolution video frames. The Savitzky-Golay filter is used for trajectory noise reduction. Verification results demonstrate that the improved YOLOX achieves a mean average precision (mAP) of 88.7 %, an enhancement of 5.6 % over the original model. When compared to advanced YOLOv7 and YOLOv8 models, the proposed method increases mAP@50 by 7.63 % and 1.07 %, respectively. The Mostly Tracked (MT) trajectories metric reaches 98.9 %, and the root-mean-square error of one-sided localization is approximately 0.05 m. These results confirm that the proposed framework is an effective tool for high-accuracy vehicle trajectory data collection in traffic studies. Additionally, a vehicle trajectory dataset has been developed and is publicly accessible at www.cqskyeyex.com.

Review of Self-Stabilization Algorithms in UAV Electro-Optical Pods

Unmanned Aerial Vehicles (UAVs) have significantly impacted various industries, with electro-optical (EO) pods playing a crucial role in enhancing their functionality. These pods require advanced stabilization algorithms to maintain a stable line of sight (LOS) in dynamic environments. This paper reviews the evolution of self-stabilization algorithms used in UAV EO pods, from traditional Proportional-Integral-Derivative (PID) controllers to advanced Kalman filters and machine learning techniques. Through a comprehensive analysis, the paper explores the application of these algorithms in different scenarios, highlighting their importance in both military and civilian domains. The findings provide insights into optimizing EO pod performance, ensuring high-quality imaging and precise targeting in various UAV operations.

High-precision IFM receiver based on random forest algorithm

Abstract The six-port network-based Instantaneous Frequency Measurement (IFM) receiver is widely utilized in industrial applications for signal frequency demodulation. However, current IFM receivers face the issue of low algorithmic demodulation accuracy (ACC). According to the four-channel output characteristics of the IFM receiver, we use the Random Forest algorithm to extract features, train and test the output voltage data set, and recognize frequency labels. The experimental results show that the ACC of the proposed algorithm reaches 0.9583, which is 13.99% higher than that of the traditional polynomial fitting algorithm. To evaluate the noise reduction performance of the algorithm, we add Gaussian white noise to the original data and demodulate the signal with noise. Comparative tests prove the advantages of the algorithm in high stability and flexibility. Finally, multiple model evaluation indicators demonstrate that the algorithm features strong applicability and robustness to the experimental data with IFM receiver.

Deep network based approaches to mitigate seasonal effects in SAR images for deforestation monitoring

Abstract. Most deforestation monitoring programs are based on optical images, which are severely affected by clouds, especially in tropical regions. As an alternative, Synthetic Aperture Radar (SAR) data are minimally affected by atmospheric conditions. However, one of the challenges inherent in such approaches is the effect of seasonal rainforest variations over the SAR images. Recently proposed stabilization algorithms mitigate this effect with significant accuracy gains, however, at the cost of higher computational resources, needed to accommodate long temporal SAR image sequences. This work addresses this issue and presents two alternative solutions that attain similar or better accuracy at a much lower computational requirements. One solution is based on a ResUnet, while the second combines an LSTM and a UNet. Both approaches were tested using raw and stabilized pairs as well as raw sequences. Experiments have indicated that both approaches can mitigate the seasonality effect using a much shorter SAR image sequences than modern stabilization methods require. The results with the multitemporal input outperformed those with the preprocessed bitemporal set by 4.3% in Recall, 1.7% in Precision, and 2.7% in F1-Score, also delivering the best deforestation probability maps.

Research on Phase Stabilization Algorithm of Femtosecond Timing System

This paper presents the design, implementation, and validation of a femtosecond timing system aimed at achieving precise time control and phase synchronization for large particle accelerators. A prototype system utilizing a continuous wave laser was developed, focusing on minimizing timing jitter and long-term phase drift. Key components include an optical delay line for coarse adjustments and a fiber stretcher for fine-tuning, achieving an adjustment precision of 1 femtosecond. The system incorporates a phase detection module with a non-In-phase/Quadrature downconversion approach, enabling high-accuracy phase measurements. A collaborative algorithm was designed to optimize the interplay between the optical delay line and the fiber stretcher, utilizing a proportional-integral-derivative (PID) control algorithm to enhance adjustment precision. A Field Programmable Gate Array (FPGA) served as the core interface converter, facilitating data communication and real-time phase information acquisition. Experimental results demonstrated significant improvements in phase stability, with average phase deviation reduced from 1374.104 fs to 15.782 fs, showcasing the effectiveness of the proposed system in achieving high precision and stability in phase control. This research provides a solid foundation for future advancements in timing systems for high-frequency reference signals.

МЕТОДИКА ЕКСПЕРИМЕНТАЛЬНИХ ДОСЛІДЖЕНЬ СПРОЩЕНОЇ МОДЕЛІ БПЛА ТИПУ БІКОПТЕР

Modern aircraft are usually statically unstable objects. Due to this, increased maneuverability and controllability is achieved. One of the promising directions is the development and implementation of UAVs of the bicopter or convertible type. Their movement is provided by two screw electric drives. The non-identity of the engine parameters and the peculiarities of the movement of UAVs of this type cause special requirements for the control systems. To ensure sustainability, modeling and conducting research with the help of various tools is necessary. The most available models of unstable movements are pendulum devices of various types, which allow studying stabilization processes. The purpose of the work is to use a computer stabilization system of a simplified bicopter UAV model by changing the thrust of the engines. The tasks of the work are: development of a computer stabilization system for the angle of pitch or roll, creation and verification of stabilization algorithms on a laboratory bench using an ARDUINO controller, research of the functional capabilities of angular stabilization circuits. The object of the study: a computer control system of an aircraft that performs the task of stabilizing a UAV. Subject of research: processes of stabilization of the angular position of a simplified bicopter model. When performing the work, a control law using angular and angular velocity feedback was chosen. A variant of aircraft stabilization when using engine thrust control is proposed. The control law of the computer stabilization system, which is implemented on the ARDUINO controller, was selected and tested. The applied value of the work is the further use of the obtained results in modern samples of bicopters.

Stabilization of Two Axis Gimbal System with Self Tuning PID Control

Gimbal is a system that provides locking and tracking of the seeker on the target in missiles and increases the angle of view with its mobility in two axes. In this study, stabilization of a two-axis gimbal system used in the missile was carried out. In gimbal stabilization, adaptive controllers are preferred instead of classical controllers due to unbalance, cross-coupling, and unmeasurable disturbances. A Self Tuning PID controller based on Fuzzy Logic was developed for axis controls in the stabilization algorithm. Thanks to this controller, which works with the principle of choosing the most appropriate coefficient at every step, it was possible to control with less than 3% errors in the tests performed with the flight simulator. In addition, a PID controller whose coefficients are optimized with Particle Swarm Optimization is designed for comparison purposes. In experimental studies, it was seen that PID with adjustable coefficients gave better results than the fixed PID.

Stabilizing Dynamic Backscatter for Swift and Accurate Object Tracking

Accurate and high-speed object movement tracking systems often face significant challenges due to signal instability caused by the object’s movement and rotation. To address these issues, we present STABack, a novel object tracking system using backscatter tags and accelerometer sensors, designed for high-accuracy, high-speed movement tracking. We developed an amplitude stabilization algorithm which uses envelope detection to reduce the impact of high-frequency movement on the signal, and uses dynamic threshold output to lower the BER in backscatter demodulation. Our method reduces the BER by 0.3757 compared to the regular demodulation method, resulting in a final BER of 0.07. Our evaluation of the STABack prototype shows that it achieves a median distance measurement accuracy of 6.45 cm with a standard deviation of 6.95 cm, under the condition of a speed of 120 cm. The attitude angle estimation’s mean error is under 7 degrees. The system’s accuracy in detecting the target object’s trajectory is as high as 99%, and it can still decode with a bit error rate of no more than 0.034 at a speed of 166 cm/s. The power consumption of our system prototype is only 38.54 μW based on our experimental results. Overall, our results demonstrate that STABack can accurately estimate the movement and rotation of target objects in unstable backscatter channels.

A meshless model for three-dimensional direct numerical simulation of local scour around cylinder bridge foundation

A meshless local scour model for cylinder bridge foundation based on the smoothed particle hydrodynamics (SPH) method is proposed in this paper. Differing from the traditional Eulerian mesh model used for monopile local scour, it overcomes the high requirements of mesh quality and density for capturing large deformations at the fluid interface. Unlike the transient sediment erosion SPH model used for dam-break, sediment flushing, and water jet, the meshless approach is innovatively applied to the continuous simulation of bridge local scour under steady flow conditions. The present SPH model is established based on the SPH multi-phase fluid model, integrating a classical fluid model combined with numerical stabilization algorithms to govern water particle motion and a sediment model from Zhang et al. (2024) to govern sediment evolution, and tests a new scheme for boundary treatment in the model based on DualSPHysics. Furthermore, a novel scour visualization method that aims at extracting both visible and actual bed surfaces from discrete particles is proposed. The model is validated through comparison with rigid bed and live bed experiments, demonstrating favorable agreement in terms of flow and scour simulation. Importantly, the model results reveal a discrepancy between the elevation of the post-scour visible bed surface derived from the conventional Eulerian mesh model and experimental data, compared to the actual bed surface unaffected by water flow erosion. This indicates the necessity for incorporating a safety margin in foundation design when utilizing experimental results to determine the maximum scour depth.

Research on SMA motor modelling and control algorithm for optical image stabilization

Research on SMA motor modelling and control algorithm for optical image stabilization