Sensor Measurement Errors Research Articles

High-Order Disturbance Observer-Based Fuzzy Fixed-Time Safe Tracking Control for Uncertain Unmanned Helicopter with Partial State Constraints and Multisource Disturbances

In the real-world operation of unmanned helicopters, various state constraints, system uncertainties and multisource disturbances pose considerable risks to their safe fight. This paper focuses on anti-disturbance adaptive safety fixed-time control design for the uncertain unmanned helicopter subject to partial state constraints and multiple disturbances. Firstly, a developed safety protection algorithm is integrated with the fixed-time stability theory, which assures the tracking performance and guarantees that the partial states are always constrained within the time-varying safe range. Then, the compensation mechanism is developed to weaken the adverse impact induced by the filter errors. Simultaneously, the influence of the multisource disturbances on the system stability are weakened through the Ito^ differential equation and high-order disturbance observer. Further, the fuzzy logic system is constructed to approximate the system uncertainties caused by the sensor measurement errors and complex aerodynamic characteristics. Stability analysis proves that the controlled unmanned helicopter is semi-globally fixed-time stable in probability, and the state errors converge to a desired region of the origin. Finally, simulations are provided to illustrate the performance of the proposed scheme.

Anti-disturbance adaptive cruise control method based on H∞ robust controller with optimized safe velocity and following time headway

Abstract To alleviate driver workload, many vehicles are now equipped with adaptive cruise control (ACC) systems. However, ACC performance can significantly deteriorate in real-world conditions due to disturbances such as sensor measurement errors, system uncertainties, and modeling errors. Therefore, an innovative anti-disturbance ACC method is proposed based on H ∞ robust controller with optimized safe velocity and following time headway to overcome the above problems. Initially, we analyze the longitudinal following characteristics that are undergoing disturbances and establish the ACC system model. Then, the optimization models for the safe velocity and the following time headway during ACC are designed. Subsequently, based on H ∞ theory, the design criterion of the H ∞ robust controller for ACC is established by utilizing the linear matrix inequality technique. Finally, two typical experiment scenarios are given for hardware-in-the-loop simulation to demonstrate the effectiveness of the proposed method.

Robust adaptive control law design for enhanced stability of agriculture UAV used for pesticide spraying

In precision agriculture, such as crop spraying, controlling UAVs presents various challenges such as variable payload, inertial coefficient variation, influence of external disturbances such as wind gusts, and uncertainties associated with the dynamics. To address these challenges, this paper proposes a hybrid control technique that combines higher-order integral sliding mode control, fast-terminal sliding mode control, and adaptive law. The objective is to mitigate the effects of variable payload, external disturbances, and uncertainties while maintaining the stability and performance of the UAV during spraying. Initially, a mathematical model is constructed for a coaxial octocopter UAV that is fitted with a spraying tank. This model takes into account the variation in mass and moment of inertia. Then, a two-loop control structure is employed to attain control of both the translational and rotational axis of the UAV. The numerical simulations are performed on a nonlinear model of the agricultural UAV system and compared with neural network based sliding mode control and robust adaptive backstepping control schemes. The robustness of the proposed scheme is tested in wind gusts and sensor measurement error conditions. Finally, hardware-in-loop simulations are performed using the Pixhawk Orange Cube flight controller to validate the real-time capability of the proposed scheme.

Distributed driving electric vehicle lateral stability control method considering time delay

This paper introduces a novel control method to address lateral stability challenge in distributed driving electric vehicles. By considering inherent time delay and unknown sensor measurement error generated from design, manufacturing, and external disturbances, the proposed approach achieves better robustness. Firstly, we establish a two-degree-of-freedom (2-DOF) dynamic model for lateral motion of an electric vehicle driven by in-wheel motors (EV-DIM). Based on this model, a control system considering time delay and unknown sensor measurement error is constructed. The control system incorporates a controller based on a dual domination gains observer, and its global asymptotic stability is rigorously proved by the Lyapunov method. Numerical simulations and co-simulations via CarSim and Simulink verify the method’s effectiveness. Compared to the sliding mode control, the proposed method shows better performance which indicates this approach promising for enhancing lateral stability of an electric vehicle.

High-precision geometric positioning for optical remote sensing satellite in dynamic imaging

ABSTRACT Dynamic imaging of optical remote sensing satellites refers to the active acquisition of images while the satellite is maneuvering at a high angular velocity, which significantly enhances the efficiency and application value of remote sensing imagery. However, due to the influence of rapid maneuvering, the random error of star sensors significantly increases, resulting in a decrease in the geometric positioning accuracy of dynamic imaging remote sensing images. This paper proposes a dynamic fusion method for multisource attitude measurement data based on the noise adaptive estimation and bidirectional filter to achieve high-precision attitude determination and geometric positioning in dynamic imaging. Based on the measurement error model of star sensors, the weights of the star sensor and gyroscope are adaptively adjusted in multisource data fusion to reduce the impact of star sensor measurement errors on the gyroscope. Moreover, a bidirectional fusion filter that includes a low-velocity maneuvering stage is proposed to realize the optimal estimation of the satellite attitude parameter. The simulation data and onboard data of the Luojia3-01 (LJ3–01) satellite were tested to verify the effectiveness of the proposed method. The geometric positioning accuracy of the staring images of LJ3–01 improved from 10.048 m to 7.538 m. The registration accuracy of the sequential images improved from 3.568 pixels to 1.179 pixels. The proposed method can significantly improve the attitude determination accuracy and the geometric positioning accuracy of LJ3–01 satellite staring images. Moreover, for simulation data with various angular velocities, the attitude determination accuracies of the proposed method are better than 0.93”. The experimental results show that the proposed method can achieve high-precision attitude determination in dynamic imaging, reaching the accuracy in the traditional passive imaging.

Sowing Depth Monitoring System for High-Speed Precision Planters Based on Multi-Sensor Data Fusion

High-speed precision planters are subject to high-speed (12~16 km/h) operation due to terrain undulation caused by mechanical vibration and sensor measurement errors caused by the sowing depth monitoring system’s accuracy reduction problems. Thus, this study investigates multi-sensor data fusion technology based on the sowing depth monitoring systems of high-speed precision planters. Firstly, a sowing depth monitoring model comprising laser, ultrasonic, and angle sensors as the multi-sensor monitoring unit is established. Secondly, these three single sensors are filtered using the Kalman filter. Finally, a multi-sensor data fusion algorithm for optimising four key parameters in the extended Kalman filter (EKF) using an improved sparrow search algorithm (ISSA) is proposed. Subsequently, the filtered data from the three single sensors are integrated to address the issues of mechanical vibration interference and sensor measurement errors. In order to ascertain the superiority of the ISSA-EKF, the ISSA-EKF and SSA-EKF are simulated, and their values are compared with the original monitoring value of the sensor and the filtered sowing depth value. The simulation test demonstrates that the ISSA-EKF-based sowing depth monitoring algorithm for high-speed precision planters, with a mean absolute error (MAE) of 0.083 cm, root mean square error (RMSE) of 0.103 cm, and correlation coefficient (R) of 0.979 achieves high-precision monitoring. This is evidenced by a significant improvement in accuracy when compared with the original monitoring value of the sensor, the filtered value, and the SSA-EKF. The results of a field test demonstrate that the ISSA-EKF-based sowing depth monitoring system for high-speed precision planters enhances the precision and reliability of the monitoring system when compared with the three single-sensor monitoring values. The average MAE and RMSE are reduced by 0.071 cm and 0.075 cm, respectively, while the average R is improved by 0.036. This study offers a theoretical foundation for the advancement of sowing depth monitoring systems for high-speed precision planters.

Research on Corn Leaf and Stalk Recognition and Ranging Technology Based on LiDAR and Camera Fusion.

Corn, as one of the three major grain crops in China, plays a crucial role in ensuring national food security through its yield and quality. With the advancement of agricultural intelligence, agricultural robot technology has gained significant attention. High-precision navigation is the basis for realizing various operations of agricultural robots in corn fields and is closely related to the quality of operations. Corn leaf and stalk recognition and ranging are the prerequisites for achieving high-precision navigation and have attracted much attention. This paper proposes a corn leaf and stalk recognition and ranging algorithm based on multi-sensor fusion. First, YOLOv8 is used to identify corn leaves and stalks. Considering the large differences in leaf morphology and the large changes in field illumination that lead to discontinuous identification, an equidistant expansion polygon algorithm is proposed to post-process the leaves, thereby increasing the average recognition completeness of the leaves to 86.4%. Secondly, after eliminating redundant point clouds, the IMU data are used to calculate the confidence of the LiDAR and depth camera ranging point clouds, and point cloud fusion is performed based on this to achieve high-precision ranging of corn leaves. The average ranging error is 2.9 cm, which is lower than the measurement error of a single sensor. Finally, the stalk point cloud is processed and clustered using the FILL-DBSCAN algorithm to identify and measure the distance of the same corn stalk. The algorithm combines recognition accuracy and ranging accuracy to meet the needs of robot navigation or phenotypic measurement in corn fields, ensuring the stable and efficient operation of the robot in the corn field.

Establishment and application of real-time monitoring system for mine ventilation resistance

The monitoring of ventilation resistance in domestic coal mines is generally faced with two problems: huge workload and lagging monitoring data behind production, which is due to the backward monitoring technology. In recent years, with the progress of technology and equipment in the coal industry, ventilation resistance monitoring products have appeared in the market one after another, but they still face the following main problems: (1) the sensor measurement error needs to be further improved; (2) The monitoring scheme is relatively simple, and the use of absolute pressure multi-parameter monitoring faces two problems: measurement error and construction cost; (3) There is a lack of successful application cases of ventilation resistance monitoring in the industry, and the analysis and application of ventilation resistance monitoring data by the upper computer is insufficient. In view of the above problems, this paper studies the principle, monitoring scheme, system framework and practical application of ventilation resistance monitoring, aiming to solve the problem of real-time online monitoring and analysis of ventilation resistance.

Small-size temperature/high-pressure integrated sensor via flip-chip method

Hydraulic technology with smaller sizes and higher reliability trends, including fault prediction and intelligent control, requires high-performance temperature and pressure-integrated sensors. Current designs rely on planar wafer- or chip-level integration, which is limited by pressure range, chip size, and low reliability. We propose a small-size temperature/high-pressure integrated sensor via the flip-chip technique. The pressure and temperature units are arranged vertically, and the sensing signals of the two units are integrated into one plane through silicon vias and gold–gold bonding, reducing the lateral size and improving the efficiency of signal transmission. The flip-chip technique ensures a reliable electrical connection. A square diaphragm with rounded corners is designed and optimised with simulation to sense high pressure based on the piezoresistive effect. The temperature sensing unit with a thin-film platinum resistor measures temperature and provides back-end high-precision compensation, which will improve the precision of the pressure unit. The integrated chip is fabricated by MEMS technology and packaged to fabricate the extremely small integrated sensor. The integrated sensor is characterised, and the pressure sensor exhibits a sensitivity and sensitivity drift of 7.97 mV/MPa and −0.19% FS in the range of 0–20 MPa and −40 to 120 °C. The linearity, hysteresis, repeatability, accuracy, basic error, and zero-time drift are 0.16% FS, 0.04% FS, 0.06% FS, 0.18% FS, ±0.23% FS and 0.04% FS, respectively. The measurement error of the temperature sensor and temperature coefficient of resistance is less than ±1 °C and 3142.997 ppm/°C, respectively. The integrated sensor has broad applicability in fault diagnosis and safety monitoring of high-end equipment such as automobile detection, industrial equipment, and oil drilling platforms.

Enhanced hydrofoil pressure field reconstruction through phase-informed sensing and sensor optimization

This study proposes a cost-effective mixed-learning method for the cavitation behavior on hydrofoils, aiming to predict the global pressure field on the hydrofoil surface with sparse placed pressure sensors and the variation of the cavity outline captured by the high-speed cameras. The method integrates CS (compressed sensing) and deep learning techniques. Firstly, within the framework of CS, the Particle Swarm Optimization (PSO) algorithm is utilized to identify the best measuring points and achieve the initial reconstruction accordingly. Subsequently, in combination with the phase information, the final reconstructions are obtained within the deep learning framework. Through validation, the proposed mixed-learning model demonstrates significantly improved reconstruction performance compared to a single source of pressure information and an unoptimized measurement point position and significantly reduces the number of sensors. This provides a novel and effective approach for accurately predicting the pressure field on hydrofoil surfaces. Furthermore, the study evaluates the robustness concerning hydraulic sensor measurement errors, sensor placement deviations, missing measuring points, reconstruction performance under various cases and hydrofoil surface regions, and the contribution of optimized measuring points to the global field. Results show that the mixed-learning model exhibits robustness against typical measurement errors, position deviations of hydraulic sensors and missing measuring points on the suction side. Additionally, a positive correlation exists between the average pressure gradient under different conditions and regions and the reconstruction error, with the pressure side region significantly contributing to global field reconstruction. These findings underscore the mixed model's superiority in predicting pressure fields on hydrofoils and offer guidance for rational sensor installation.

IMPLEMENTATION OF BIOGAS EQUIPMENT USING ARDUINO MEGA 2560 WITH SENSOR MQ-4

The biogas in the research that the authors do is a mixture of cow dung with 1: 2 water, where the biogas in this study is carried out by anaerobic fermentation or without air, a mixture of cow dung and water is put into the reactor and the fermentation process is carried out for 12 days to prove whether biogas In this study, gas will be stored in storage (biogas bag 1 and biogas bag 2. The research method used, namely conducting literature studies, exploration, determining equipment specifications, designing and implementing and testing. In designing this tool, it is able to produce gas from the biogas fermentation process, namely methane gas which can be detected by a functioning methane gas sensor. Where there is an increase in PPM testing between the first MQ-4 sensor from 5246.36 and the second MQ-4 sensor detected at 5667.23 PPM with the adjusted difference of 420.87 PPM, so the methane gas is tested with a stove and a lighter. The MQ-4 sensor measurement error results reached 5.63%. Keywords: Reaktor fixed dome, MQ-4, Gas Metana, Monitoring

Seven‐degree‐of‐freedom‐based electric wheel sampled‐data active shimmy control method considering unknown sensor measurement error

AbstractThe active shimmy control methods for electric vehicle driven by in‐wheel motors (EV‐DIM) have been proposed in the recent years. However, these methods assume that data obtained from sensors are accurate, despite the fact that sensor measurements are prone to error. This unknown measurement error can make shimmy control difficult. Additionally, current shimmy models are low degree‐of‐freedom, which simplifies control but decreases accuracy. In this paper, we address these issues using a sampled‐data output control method based on a higher seven‐degree‐of‐freedom (7DOF) shimmy model which includes the steering system, suspension, and electric wheel. We first construct a 7DOF electric wheel shimmy model and use Lagrange's theorem to derive the electric wheel shimmy dynamic equations. We then obtain system state equations that account for unknown sensor measurement error based on the 7DOF shimmy model. A sampled‐data observer and controller are designed to attenuate or eliminate the shimmy phenomenon via a domination gain. Finally, we conduct numerical simulations and experiments to verify the effectiveness of our proposed method.

A novel joint depth sensor calibration method without fixture for mobile robots’ navigation

AbstractCommercial mobile robots are usually equipped with multiple depth sensors that can measure the point cloud information around the robot's environment. The installation process of these sensors contains assembly error and sensor measurement error, so it is necessary to calibrate each sensor to align the point cloud. In order to obtain the sensor calibration results of commercial robots under normal working conditions, this study proposes a fixture free multi depth sensor joint calibration method that can be deployed on low‐cost embedded computing units, which efficiently aligns the point clouds of each sensor. During the calibration process, the robot is placed in the center of three upright thin plates perpendicular to the ground. 2D LIDAR depicts high‐precision contours of the upright thin plates. In the calibration process of each depth sensor, the roll angle and pitch angle of the sensor point cloud are first calibrated to make it perpendicular to the ground, and then the yaw angle and position of the point cloud are calibrated to fit the high‐precision contour of the upright thin plate. The results show that this method can be deployed on low‐cost embedded computing units, with real‐time and accurate calibration results. The convergence of calibration results can be achieved through up to 5 iterations, and the average running time is less than 120 ms. This research achievement provides a reference for multi‐sensor calibration of commercial robots.

Exploring Practical Acoustic Transduction Attacks on Inertial Sensors in MDOF Systems

In cyber-physical systems, inertial sensors are the basis for identifying motion states and making actuation decisions. However, extensive studies have proved the vulnerability of those sensors under acoustic transduction attacks, which leverage malicious acoustics to trigger sensor measurement errors. Unfortunately, the threat from such attacks is not assessed properly because of the incomplete investigation on the attack's potential, especially towards multiple-degree-of-freedom systems, e.g., drones. To thoroughly explore the threat of acoustic transduction attacks, we revisit the attack model and design a new yet practical acoustic modulation-based attack, named KITE. Such an attack enables stable and controllable injections, even under frequency offset based distortions that limit the effect of prior attacking approaches. KITE exploits the potential threat of transduction attacks without the need of strengthening attackers' abilities. Furthermore, we extend the attack surface to multiple-degree-of-freedom (MDOF) systems, which are more widely deployed but ignored by prior work. Our study also covers the scenario of attacking moving targets. By revealing the practical threat from acoustic transduction attacks, we appeal for both the attention to their harm and necessary countermeasures.

Distributed optical fiber magnetic field sensor based on polarization-sensitive OFDR.

A distributed optical fiber magnetic field sensor based on a polarization-sensitive optical frequency domain reflectometer (POFDR) is proposed. It extracts the accumulated Faraday rotation by combining the Stokes vectors and the backward Mueller matrices from the measured states of polarization (SOPs) and obtains the magnetic field component. This method avoids adjusting the input polarization during the magnetic field sensing process. It overcomes the drawback of the conventional POFDR scheme, which requires at least two sets of different input SOPs for each sensing. Finally, the aforementioned effectiveness has been experimentally verified by using a single-mode sensing fiber. The results show that the sensor has good repeatability and linearity. The measurement error of the magnetic field sensor is 19.4 mT. The measured magnetic field variations agree with the applied ones with similarities higher than 0.98.

Control parameter optimization of underwater gliders with uncertainty using hierarchical optimization method and interval analysis theory

This article attempts to solve the engineering optimization problem for control parameter configuration of underwater gliders with parameter uncertainty. The optimization objectives are maximizing the glider energy utilization rate and average voyage velocity; design variables include the net buoyancy adjustment amount and movable mass block translation amount; and uncertain parameters include the glider control parameter errors, manufacturing errors and sensor measurement errors. First, a dynamic simulation-based performance evaluation model of the glider is established, and interval number order theory is used to quantify the uncertainty. Then, surrogate models of the performance evaluation model are established, and the Sobol’ method-based sensitivity analysis is executed to identify the key uncertain parameters. Thus, a hierarchical optimization framework is introduced, and its system level and sub-level are used for determining optimal control parameter values and performance evaluation parameter intervals, respectively. Finally, optimization calculation is executed under different parameter configurations, and some rules are summarized.

Adaptive pneumatic soft gripper with embedded flexible bending sensor

Purpose The purpose of this paper is to introduce a variable distance pneumatic gripper with embedded flexible sensors, which can effectively grasp fragile and flexible objects. Design/methodology/approach Based on the motion principle of the three-jaw chuck and the pneumatic “fast pneumatic network” (FPN), a variable distance pneumatic holder embedded with a flexible sensor is designed. A structural design plan and preparation process of a soft driver is proposed, using carbon nanotubes as filler in a polyurethane (PU) sponge. A flexible bending sensor based on carbon nanotube materials was produced. A static model of the soft driver cavity was established, and a bending simulation was performed. Based on the designed variable distance soft pneumatic gripper, a real-time monitoring and control system was developed. Combined with the developed pneumatic control system, gripping experiments on objects of different shapes and easily deformable and fragile objects were conducted. Findings In this paper, a variable-distance pneumatic gripper embedded with a flexible sensor was designed, and a control system for real-time monitoring and multi-terminal input was developed. Combined with the developed pneumatic control system, a measure was carried out to measure the relationship between the bending angle, output force and air pressure of the soft driver. Flexible bending sensor performance test. The gripper diameter and gripping weight were tested, and the maximum gripping diameter was determined to be 182 mm, the maximum gripping weight was approximately 900 g and the average measurement error of the bending sensor was 5.91%. Objects of different shapes and easily deformable and fragile objects were tested. Originality/value Based on the motion principle of the three-jaw chuck and the pneumatic FPN, a variable distance pneumatic gripper with embedded flexible sensors is proposed by using the method of layered and step-by-step preparation. The authors studied the gripper structure design, simulation analysis, prototype preparation, control system construction and experimental testing. The results show that the designed flexible pneumatic gripper with variable distance can grasp common objects.

Error analysis and modeling of the racetrack magnetohydrodynamic linear motion sensor

Micro-vibration measurement methods for spacecraft structures mainly include the use of an accelerometer, laser, and magnetohydrodynamic (MHD) measurement methods. The micro-vibration measurement sensor developed based on the MHD measurement method has no mechanical wear between internal components, a fast high-frequency vibration response, and strong anti-interference properties. To reduce the measurement error of the racetrack MHD linear motion sensor developed in the laboratory, this paper investigated the sensor error, analysed the error source, combined the BP neural network optimized by particle swarm optimization (PSO) with variational mode decomposition (VMD), used the VMD-PSO-BP neural network to establish the error compensation model of the racetrack MHD linear motion sensor, and combined the PSO-BP neural network with wavelet threshold de-noising (WTD). The WTD-PSO-BP and RBF neural networks were used to develop the error compensation model of the racetrack MHD linear motion sensor. Comparing the three models, the experimental results show that the VMD-PSO-BP model has the best compensation effect. The mean absolute error of the output signal of the racetrack MHD linear motion sensor compensated by the VMD-PSO-BP neural network model was 1–2 times lower than that before compensation, the signal-to-noise ratio was 10 times higher on average, and the correlation coefficient was more than 0.95.

Using synthetic data to benchmark correction methods for low-cost air quality sensor networks

A benchmark was performed, comparing the results of three different methodologies proposed by three institutions to calibrate a network of low-cost PM2.5 sensors, on an hourly basis, using synthetically generated real concentrations and sensor measurements. The objective of the network calibrations was to correct the 2000+ sensor measurements in the Netherlands for the sensitivity to (local) environmental conditions. The option to use real measurements was dropped because the number of low-cost sensors sufficiently close to the 40 reference measurement locations was assessed to be spatially insufficient to benchmark the proposed approaches. Instead, synthetic real concentrations were generated to enable validation at all sensor locations. Hourly actual sensor and actual fixed concentrations, as well as interpolated concentration maps, were used as underlying data to generate the synthetic data sets for the period of 1 month. The synthetic sensor measurement errors were constructed by sampling from a collection of differences between actual sensor values and actual measurements. Of the three tested calibration methods, two follow a similar approach, although having differences in, e.g., outlier analyses and method of grouping sensors, leading also to comparable corrections to the raw sensor measurements. A third method uses significantly stricter rules in outlier selection, discarding considerably more sensors because of insufficient quality. Differences between the methods become most apparent when analyzing data at a smaller time scale. It is shown that two network calibration methods are better at correcting the hourly/daily bias.

Accounting for Vibration Noise in Stochastic Measurement Errors of Inertial Sensors

Accounting for Vibration Noise in Stochastic Measurement Errors of Inertial Sensors