Step Signal Research Articles

Linear Programming-Based Sparse Kernel Regression with L1-Norm Minimization for Nonlinear System Modeling

This paper integrates L1-norm structural risk minimization with L1-norm approximation error to develop a new optimization framework for solving the parameters of sparse kernel regression models, addressing the challenges posed by complex model structures, over-fitting, and limited modeling accuracy in traditional nonlinear system modeling. The first L1-norm regulates the complexity of the model structure to maintain its sparsity, while another L1-norm is essential for ensuring modeling accuracy. In the optimization of support vector regression (SVR), the L2-norm structural risk is converted to an L1-norm framework through the condition of non-negative Lagrange multipliers. Furthermore, L1-norm optimization for modeling accuracy is attained by minimizing the maximum approximation error. The integrated L1-norm of structural risk and approximation errors creates a new, simplified optimization problem that is solved using linear programming (LP) instead of the more complex quadratic programming (QP). The proposed sparse kernel regression model has the following notable features: (1) it is solved through relatively simple LP; (2) it effectively balances the trade-off between model complexity and modeling accuracy; and (3) the solution is globally optimal rather than just locally optimal. In our three experiments, the sparsity metrics of SVs% were 2.67%, 1.40%, and 0.8%, with test RMSE values of 0.0667, 0.0701, 0.0614 (sinusoidal signal), and 0.0431 (step signal), respectively. This demonstrates the balance between sparsity and modeling accuracy.

Design of sliding mode controller for servo feed system based on generalized extended state observer with reinforcement learning

Nonlinear friction, system uncertainty, and external disturbances have a significant impact on the performance of high-precision servo feed systems. In order to achieve higher tracking accuracy, a sliding mode controller based on a generalized extended state observer with the double critic deep deterministic policy gradient algorithm is designed. Firstly, a flexible two mass drive model (FTMDM) is established for the two-axis differential micro-feed system (TDMS). Next, a generalized extended state observer (GESO) is designed to estimate matched interference and mismatched interference. And it is proved that the observation error of GESO is bounded. A sliding mode controller based on GESO is further proposed. The stability of the controller has been proven through Lyapunov theory, and the error is bounded and converges to zero in finite time. The tuning process of controller parameters is simplified by using quadratic optimal control principle. Furthermore, a double critic deep deterministic policy gradient algorithm (DCDDPG) is proposed to achieve dynamic optimization of parameters about GESO. The simulation results show that GESO with DCDDPG can reduce the observation error of step signal and sinusoidal signal, and improve the observation accuracy of nonlinear friction significantly. Finally, experimental results show that the proposed control method achieves more accurate position tracking performance on TDMS.

Impairment of neuronal tyrosine phosphatase STEP worsens post-ischemic inflammation and brain injury under hypertensive condition

Hypertension is associated with poor outcome and higher mortality in patients with ischemic stroke. The impairment of adaptive vascular mechanisms under hypertensive condition compromises collateral blood flow after arterial occlusion in patients with acute ischemic stroke resulting in hypoperfusion. The increased oxidative stress caused by hypoperfusion is thought to be a trigger for the rapid evolution of ischemic infarct volume under hypertensive condition. However, the cellular factors and pathways that contribute to the exacerbation of ischemic brain injury under hypertensive condition is not yet understood. The current study reveals that predisposition to hypertension leads to basal loss of function of the neuron-specific tyrosine phosphatase STEP, which plays a crucial role in neuroprotection against excitotoxic insult. The findings further show that a mild ischemic insult in hypertensive rats triggers an early onset and sustained activation of the neuronal extracellular signal regulated kinase (ERK MAPK), a member of the mitogen activated protein kinase family and a substrate of STEP. This leads to rapid increase in the activation of neuronal NF-κB, expression of neuronal cyclooxygenase-2 and subsequent biosynthesis of the pro-inflammatory mediator prostaglandin E2, resulting in rapid morphological transformation of microglia to the pro-inflammatory state and subsequent exacerbation of ischemic brain injury. Restoration of STEP signaling with intravenous administration of a STEP-derived peptide mimetic reduces the pro-inflammatory response in neurons, activation of microglia, and ischemic brain injury. The findings suggest that the basal loss of STEP function under hypertensive condition contributes to the exacerbation of ischemic brain injury by enhancing post-ischemic inflammatory response. The study not only presents a novel role of STEP in regulating neuroimmune communication but also highlights the therapeutic potential of a STEP-mimetic in mitigating ischemic brain damage under hypertensive condition.

Research on the Performance and Control Strategy of Electro-Hydraulic Servo System for Selective Hole Digging Tree Planter

Compared to agricultural environments, afforestation sites are more complex, often presenting issues such as undulating and uneven terrain. These conditions lead to instability in hole digging depth and plant spacing during continuous movement, and the hole shape may not meet expectations. Additionally, the hydraulic system exhibits slow response speed and long steady-state time, affecting the quality of sapling planting. To address these issues, this paper designs an intelligent planting control system for intermittent hole digging under continuous dynamic movement, based on a large tree planter. The focus is on studying the dynamic accuracy of the hole digging cylinder to resolve the instability of plant spacing and planting depth in actual planting processes. Firstly, a motion trajectory model of the intermittent hole digging mechanism is established to obtain the relationship between the displacement trajectory of the rotating cutter and the displacements of the floating cylinder and the hole digging cylinder. Secondly, a mathematical model of the electro-hydraulic servo system is established to control the dynamic accuracy of the hole digging operation. Finally, a Simulink simulation model of the system is established to analyze the performance indicators of the hydraulic system during operation using step and sinusoidal excitation signals. The test results show that the displacement of the hydraulic piston rod can ensure a linear extension trend within the range of 0 to 0.4 m, and the extension distance of the hole digging cylinder in the planting system is 0 to 0.35 m, ensuring linear change within this stroke. When the system’s extension command is 1 V, the actual output is 0.6 m, with a relative error of less than 10% compared to the simulation value, indicating that the control strategy can effectively improve the dynamic performance of the system. When the hydraulic system is in a steady-state extension state at 50 to 58.6 s, the relative error with the simulation value is 7.3%, meeting the “double ten indicators” requirement. The research results clearly verify the superior performance of the proposed intelligent control system, and the proposed control strategy has great potential in practical applications, promising to improve afforestation quality by stabilizing planting spacing and planting depth.

Оцінка точності комп’ютерного моделювання окуло-моторної системи на основі моделей Вольтерри

Integral nonlinear models are used to create mathematical models of the human oculo-motor system (OMS). These models take into account both inertial and nonlinear properties of the objects under study. To obtain empirical data for model construction, experimental studies are conducted with OMS using «input-output» data. Visual stimuli are used as test signals, displayed on a computer monitor at various distances from the starting position, which formally corresponds to the action of step signals with varying amplitudes on the object of study. In this process, the responses of the OMS are recorded using innovative eye-tracking technology. Mathematical models in the form of Volterra series and polynomials are employed for computer modeling of the OMS. The aim of this research is to analyze the accuracy of OMS identification as multidimensional transient functions based on eye-tracking data, examining the dependency of computation errors for models of different orders on the amplitudes and quantities of the test signals used. The subject of the study includes various methods for identifying OMS, algorithms, and Python-based software tools for computing the dynamic characteristics of OMS using eye-tracking technology. The research explores identification methods: compensation, approximation, and least squares methods. The accuracy of the linear, quadratic, and cubic OMS models is evaluated. The most accurate models, constructed from real experimental data, are found to be quadratic or cubic OMS models obtained using the least squares method with three test signals.

Designing fractional-order ramp controller with explicit formulas using non-integer plant parameters

This paper aims to achieve two goals. First, the tracking problem with a ramp-type setpoint and disturbance is addressed, where classical methods provide steady-state error. Second, the available fractional-order controller designs in the literature are based on complex calculations. This paper presents a design scheme that employs a target loop-based controller, allowing users to compute tunable parameters without approximation or assumption. The control structure is capable of handling both the step and ramp signals. The main advantage of the proposed design is that it can be analytically tuned using the maximum sensitivity and phase-margin specifications of choice. Explicit relationships are established for the new fractional-order ramp controller (FoRC) parameters to facilitate tuning. The numerical investigations revealed improved servo and regulatory responses compared to recently published strategies. The design controller ensures stability and zero steady-state error as the ramp input increases. Furthermore, hardware verification demonstrates the scheme’s superior performance on a two-tank level loop, even with the influence of disturbances and modeling variations.

Fuzzy inference system enabled neural network feedforward compensation for position leap control of DC servo motor

To improve dynamic performance and steady-state accuracy of position leap control of the direct current (DC) servo motor, a fuzzy inference system (FIS) enabled artificial neural network (ANN) feedforward compensation control method is proposed in this study. In the method, a proportional-integral-derivative (PID) controller is used to generate the baseline control law. Then, an ANN identifier is constructed to online learn the reverse model of the DC servo motor system. Meanwhile, the learned parameters are passed in real-time to an ANN compensator to provide feedforward compensation control law accurately. Next, according to system tracking error and network modeling error, an FIS decider consisting of an FI basic module and an FI finetuning module is developed to adjust the compensation quantity and prevent uncertain disturbance from undertrained ANN adaptively. Finally, the feasibility and efficiency of the proposed method are verified by the tracking experiments of step and square signals on the DC servo motor testbed. Experimental results show that the proposed FIS-enabled ANN feedforward compensation control method achieves lower overshoot, faster adjustment, and higher precision than other comparative control methods.

Computer-Assisted Processing of Current Step Signals in Single Blocking Impact Electrochemistry.

Current step signals related to single-entity collisions in blocking impact electrochemistry were analyzed by computer-assisted processing for estimating the size distributions of various particles. In this work, three different types of entities were studied by single blocking impact electrochemistry: polystyrene nanospheres (350 nm diameter) and microspheres (1 μm diameter), phospholipid liposomes (300 nm diameter) and two different strains of Gram-negative bacillus bacteria (Escherichia coli and Shewanella oneidensis). The size estimations of these different entities from the current step signal analysis were compared and discussed according to the shape and size of each entity. From the magnitude of the current step transient, the size distribution of each entity was calculated by a new computer program assisting in the detection and analysis of single impact events in chronoamperometry measurements. The data processing showed that the size distributions obtained from the electrochemical data agreed with the dynamic light scattering and atomic force microscopy data for nanospheres and liposomes. In contrast, the size estimation calculated from the electrochemical data was underestimated for microspheres and bacteria. We demonstrated that our computer program was efficient for detecting and analyzing the collision events in single blocking impact electrochemistry for various entities from spherical hard nanoparticles to micrometer-sized rod-shaped living bacteria.

Adaptive Control for a Two-Axis Semi-Strapdown Stabilized Platform Based on Disturbance Transformation and LWOA-PID.

A two-axis semi-strapdown stabilized platform is a device designed to eliminate aircraft disturbances and ensure the stability of the sensor's orientation. A traditional two-axis semi-strapdown stabilization platform for aircraft can effectively control disturbance in pitch and yaw channel, but it cannot achieve ideal disturbance control in the roll channel. In order to solve this problem, an adaptive control method based on disturbance transformation and LWOA-PID is proposed. Disturbance transformation is the process of integrating the angular position disturbance of the roll from the previous moment into the combined disturbance of the pitch and yaw at the current moment. This is followed by decoupling the combined disturbance of the pitch and yaw at the current moment, thereby eliminating the disturbance caused by the roll from the previous moment. This process is repeated to achieve the goal of eliminating roll channel disturbances. To ensure the line of sight (LOS) pointing accuracy stability in the two-axis semi-strapdown stabilized platform system for aircraft, a whale optimization adaptive proportional-integral-derivative (LWOA-PID) controller based on Latin hypercube sampling is designed. It is then compared with the classical PID controller in Matlab/Simulink. The simulation results indicate that the disturbance conversion module proposed in this paper can eliminate the impact of roll axis disturbances on the LOS pointing accuracy of the two-axis semi-strapdown stabilized platform for aircraft. Compared to the classical PID controller, the LWOA-PID controller reduces tracking errors for step and sinusoidal signals by 50% and 75%, respectively. It also shortens optimization time by 37.5% compared to the WOA-PID while maintaining the same level of accuracy. Furthermore, when combined with the conversion module, the tracking error is reduced by an additional order of magnitude.

Cerebral perfusion metrics calculated directly from a hypoxia-induced step change in deoxyhemoglobin

Resting cerebral perfusion metrics can be calculated from the MRI ΔR2* signal during the first passage of an intravascular bolus of a Gadolinium-based contrast agent (GBCA), or more recently, a transient hypoxia-induced change in the concentration of deoxyhemoglobin ([dOHb]). Conventional analysis follows a proxy process that includes deconvolution of an arterial input function (AIF) in a tracer kinetic model. We hypothesized that the step reduction in magnetic susceptibility accompanying a step decrease in [dOHb] that occurs when a single breath of oxygen terminates a brief episode of lung hypoxia permits direct calculation of relative perfusion metrics. The time course of the ΔR2* signal response enables both the discrimination of blood arrival times and the time course of voxel filling. We calculated the perfusion metrics implied by this step signal change in seven healthy volunteers and compared them to those from conventional analyses of GBCA and dOHb using their AIF and indicator dilution theory. Voxel-wise maps of relative cerebral blood flow and relative cerebral blood volume had a high spatial and magnitude congruence for all three analyses (r > 0.9) and were similar in appearance to published maps. The mean (SD) transit times (s) in grey and white matter respectively for the step response (7.4 (1.1), 8.05 (1.71)) were greater than those for GBCA (2.6 (0.45), 3.54 (0.83)) attributable to the nature of their respective calculation models. In conclusion we believe these calculations of perfusion metrics derived directly from ΔR2* have superior merit to calculations via AIF by virtue of being calculated from a direct signal rather than through a proxy model which encompasses errors inherent in designating an AIF and performing deconvolution calculations.

Vibration signal acquisition and extraction for cutter wear monitoring

Abstract The cutter wear state is key in manufacturing. During the machining, the vibration signal of the tool spindle will change with the increase of cutter wear, which provides an effective method for cutter wear monitoring. However, due to the popularity of machining centers, the machining trajectory of a cutter is extremely complex in practical production. The complexity includes a single process containing multiple work steps, a step containing random and long non-cutting time, etc., making it difficult to acquire and extract the specific step signal as the effective signal for cutter wear monitoring. Therefore, this paper proposes a system that can synchronously acquire machine parameters and tool spindle vibration signals. Then, two algorithms—the cutting signal recognition algorithm and the step signal similarity detection algorithm—are proposed. The former algorithm is based on sliding window energy, which can accurately identify the instant the cutter begins to cut the workpiece. The step signal similarity detection algorithm based on the Edit Distance on Real (EDR) sequence ensures the correctness of the step extraction. The experimental results show that the system has efficient acquisition of vibration signals and correct step signal extraction, providing a data basis for cutter wear monitoring.

Pneumatic servo position control optimization using adaptive-domain prescribed performance control with evolutionary mating algorithm

Pneumatic servo systems face challenges such as friction, compressibility, and nonlinear dynamics, necessitating advanced control techniques. Research suggests model-based, model-free, hybrid, and optimization-based methods have their strengths. Therefore, this study presents an optimal control strategy using Adaptive Domain Prescribed Performance Control (AD-PPC) cascaded with PID and optimized using the Evolutionary Mating Algorithm (EMA) for a pneumatic servo system (PSS). The goal is to achieve faster transient control and stable rod-piston positioning with minimal friction through the hysteresis phenomenon of the targeted proportional valve-controlled double-acting pneumatic cylinder (PPVDC) representing the PSS. The novel EMA optimizes the cascaded controller based on the tracking error as its objective function. Simulation studies verify the proposed AD-PPC-PID controller with the PPVDC model plant, iteratively optimized by the EMA. The analytical study compares this setup's control system and optimization model with the same control system model using alternative optimization methods. The testing employs step and multi-step signals for PPVDC's rod-piston position input. Results show that the EMA-tuned AD-PPC-PID outperforms AD-PPC-PID controller with other optimizers. For both input trajectory tests, EMA-tuned AD-PPC-PID shows faster response times, with average improvements of 30 % in settling times and 70 % in tracking performance metrics compared to other optimizers, making it robust for nonlinear system applications like PPVDC rod-piston positioning.

Reconstructing dynamics of the Baltic Ice Stream Complex during deglaciation of the Last Scandinavian Ice Sheet

Abstract. Landforms left behind by the last Scandinavian Ice Sheet (SIS) offer an opportunity to investigate controls governing ice sheet dynamics. Terrestrial sectors of the ice sheet have received considerable attention from landform and stratigraphic investigations. In contrast, despite its geographical importance, the Baltic Sea remains poorly constrained due to limitations in bathymetric data. Both ice-sheet-scale investigations and regional studies at the southern periphery of the SIS have considered the Baltic depression to be a preferential route for ice flux towards the southern ice margin throughout the last glaciation. During the deglaciation the Baltic depression hosted the extensive Baltic Ice Lake, which likely exerted a considerable control on ice dynamics. Here we investigate the Baltic depression using newly available bathymetric data and peripheral topographic data. These data reveal an extensive landform suite stretching from Denmark in the west to Estonia in the east and from the southern European coast to the Åland Sea, comprising an area of 0.3 million km2. We use these landforms to reconstruct aspects of the ice dynamic history of the Baltic sector of the ice sheet. Landform evidence indicates a complex retreat pattern that changes from lobate ice margins with splaying lineations to parallel mega-scale glacial lineations (MSGLs) in the deeper depressions of the Baltic Basin. Ice margin still-stands on underlying geological structures indicate the likely importance of pinning points during deglaciation, resulting in a stepped retreat signal. Over the span of the study area we identify broad changes in the ice flow direction, ranging from SE–NW to N–S and then to NW–SE. MSGLs reveal distinct corridors of fast ice flow (ice streams) with widths of 30 km and up to 95 km in places, rather than the often-interpreted Baltic-wide (300 km) accelerated ice flow zone. These smaller ice streams are interpreted as having operated close behind the ice margin during late stages of deglaciation. Where previous ice-sheet-scale investigations inferred a single ice source, our mapping identifies flow and ice margin geometries from both Swedish and northern Bothnian sources. We anticipate that our landform mapping and interpretations may be used as a framework for more detailed empirical studies by identifying targets to acquire high-resolution bathymetry and sediment cores and also for comparison with numerical ice sheet modelling.

Research on Multi-Mode Control of Electro-Hydraulic Variable Displacement Pump Driven by Servo Motor

The electro-hydraulic power source with an electro-hydraulic variable pump driven by a servo motor is suitable for electrified construction machinery. To achieve better energy efficiency in different working conditions, the multi-mode control scheme was proposed for the electro-hydraulic power source. The control scheme includes pressure control, flow control, and torque control modes. The switching rule among the three control modes was formulated based on the minimum pump pressure. The fuzzy PID controller was designed, and a composite flow regulation strategy was formulated, including the load-sensitive adaptive displacement regulation and servo motor variable speed regulation. The AMESim-Simulink co-simulation model of multi-mode control was established. The test platform was built, and the experimental study was carried out. The results indicate that the fuzzy PID control has a shorter response time and a more stable control effect compared with PID control. Additionally, the composite flow regulation strategy improves the flow regulation range by 36% and reduces the flow overshoot by 20% compared with the load-sensitive adaptive displacement regulation. As the main control valve received an opening step signal, the full flow regulation (7~81 L/min) of the power source took approximately 0.5 s to rise and 0.2 s to fall. The relative error of pressure difference for the main control valve was 0.63%. When receiving the pressure and torque step signal, the pump pressure and pump input torque both took approximately 0.45 s to rise and 0.2 s to fall. The relative errors of pump pressure and torque control were 0.2% and 0.16%, respectively. In the multi-mode control, the electro-hydraulic power source could switch smoothly between flow control mode, pressure control mode, and torque control mode. These results provide a reference for the multi-mode control of an electro-hydraulic power source with an electro-hydraulic variable pump driven by a servo motor.

An ultrafast and robust structural damage identification framework enabled by an optimized extreme learning machine

Artificial intelligence (AI) has recently been implemented in structural health monitoring (SHM) systems for damage detection and identification. However, existing AI methods often involve a high number of parameters, resulting in the laborious workload during model training and implementation. In this study, we develop a lightweight damage identification framework embedded with an optimized extreme learning machine (ELM) using a significantly reduced number of parameters from frequency features of the structural vibrational signals. Coupled with the ensemble empirical mode decomposition (EEMD), the frequency features were abstracted from the structural vibrational signals in the data pre-processing step. Then, a metaheuristic algorithm (chaos game optimization, CGO) was chosen to optimize the model weight of the ELM to guarantee damage identification accuracy. Once the model is trained, the structural damages can be precisely identified despite the noise-contaminated data. We validated the efficiency and accuracy of the proposed framework with a public database and compare its performance with several common metaheuristic algorithms. With the proven capability, the proposed framework shows a promising future as a handy and reliable AI-assisted digital tool for robust development in next-generation structural monitoring systems.

A Network-Group Target State and Network Topology Estimation Method Based on Signals Containing Delay, Doppler and Address

Network-group targets are a set of objectives that adhere to a shared communication protocol, perform common tasks, and exhibit relatively coordinated movements. Typically, network-group targets emit radar and communication signals. However, they often employ a silent mode to evade our perception. Despite this, they continue to exchange data through their communication networks. By intercepting the communication signals of these targets, this paper proposes a method for estimating the state and network topology of network-group targets based on random finite set (RFS) theory. This method is based on the modeling of network-group targets using a labeled multi-Bernoulli (LMB) RFS. In state estimation, the usual method involves extracting the localization parameters from the signals in the first step and use these parameters for target tracking in the second step. This study focused on estimating the kinematic states of network-group targets using communication signals containing delay and Doppler information received by multiple mobile sensor platforms. The proposed method considers the coherency between frequency-hopping pulses in the signals, resulting in an improved estimation performance. In addition, considering that network-group targets require network access for information exchange, graph theory concepts were utilized to estimate the network topology of network-group targets by address measurement. The simulation results validated the effectiveness of the proposed method and demonstrated its superior performance.

Fuzzy adaptive PID control for path tracking of field intelligent weeding machine

In order to reduce the influence of external interference on the intelligent weeder, improve the stability of its path tracking system, enhance its weeding effect, and reduce the rate of seedling injury, the motion steering control of the four-wheeled weeder is studied. The control strategy of fuzzy adaptive proportional integral differential (PID) algorithm is determined by establishing the path tracking mathematical model of the field intelligent weeder; the fuzzy adaptive PID controller is designed with the input average steering angle deviation of the front wheels and the rate of change of the deviation so as to realize the automatic adjustment and optimization of the parameters. We use Simulink to build the control system model and to compare and analyze with the PID controller. The results show that under the action of the step signal, the rise time of the fuzzy adaptive PID and PID control system responses is 2.596 and 4.209 s, respectively; under the action of the impulse, the fuzzy adaptive PID has a significant advantage over the traditional PID control system in terms of the rise time and response time. In addition, this control system has a fast response speed, high adaptability, high anti-interference ability, and superior path tracking ability, which are necessary for improving the accuracy of the field intelligent weeder and reducing the rate of seedling injury.

Optical System Design for Laser-Induced Fluorescence Study of Helium Atoms on the Linear Plasma Device

According to the characteristic spectral lines of helium atoms, an optical system for the laser-induced fluorescence study of helium atoms has been designed. The design includes a helium spectral scheme and a laser injection and fluorescence collection system. The diode laser generates a 667.8-nm laser, and the laser is injected into the linear plasma device through an optical fiber. The fluorescence collection system detects 501.6-nm fluorescence signals. Experiments were carried out on the linear plasma device during helium discharge, simulating the helium ash environment at the boundary of the fusion reactor. The fluorescence collection was realized, and the fluorescence signals showed an increasing trend with laser power. Atomic density calibration and study will be performed based on the collected signals in the next step.

Error analysis of calibration for horizontal tensor rotating accelerometer gravity gradiometer

The rotating accelerometer gravity gradiometer (RAGG) is regarded as a significant tool in geophysics applications such as resource exploration and gravity-assisted navigation research. Here we develop a prototype of the horizontal tensor RAGG and build a dedicated calibration platform for evaluating its performance. The error of calibration is theoretically computed and experimentally verified. The errors considered include higher-order terms of the gravity gradient error, positioning error, demodulation phase error, as well as the dimensions and density error of the gravitational mass. It has witnessed a high-accuracy gravity gradient calibration platform based on a motorized translation stage, with error values of 0.49 E (1 E = 10−9 s2) in channel Γsin and 0.24 E in channel Γcos . At an averaging time of 32 s, the RAGG demonstrates optimal stability, featuring a minimum Allan deviation value of 6.6 E in channel Γsin and 5.5 E in channel Γcos . And the RAGG has achieved a measurement noise floor of about 40 E/√Hz @ 0.001–0.1 Hz in both channels. For simulating engineering applications, the experimental results demonstrate a standard deviation error of only 3.2 E by employing a gravity gradient excitation step signal of 10 E. It shows that this RAGG has the potential for a wide range of applications.

Large bandwidth and dynamic range current sensor based on micro-PCB Rogowski coil

A micro-PCB Rogowski coil with a volume of only 0.032 cm3 was proposed in this paper, and a magnetic flux concentration structure was introduced while ensuring the high-frequency performance of the coil to improve the measurement ability of the Rogowski coil sensor for weak current characterization. We have systematically investigated the magnetic core materials suitable for high-frequency current sensing, and the performance of core at high frequency has also been verified theoretically and experimentally. In addition, a high-precision signal processing circuit, whose main functions are amplification and integration, has also been developed. Experiments have shown that the developed PCB Rogowski coil current sensor can measure the current in the μA level with a bandwidth of 10 MHz, a sensitivity of up to 180 V A−1 and a resolution of up to 50 μA. The response time of the sensor to a step signal is within 30 ns, and it also has good waveform following ability. Compared with commercial TMR sensors, the sensor proposed in this work also has certain advantages in terms of noise.