Simulation Experiments Research Articles

Intelligent Model-Free Control for Power Line Inspection Robots: Tackling Input Time Delays with Data-Driven Solutions

This article presents an innovative approach to model-free adaptive control designed for power line inspection robots facing challenges with input time delays. The strategy begins by employing a compact-form dynamic linearization technique to transform the original system into a data-driven model. Subsequently, utilizing real-time input and output information, the system’s pseudo-partial derivatives are assessed online. Leveraging these assessment parameters, a weighted one-step prediction control mechanism is designed, and a compact-form dynamic linearization model-free adaptive control framework is established. Moreover, the research incorporates compression mapping to thoroughly confirm the convergence of the algorithm, thereby ensuring its stability. Ultimately, the effectiveness and practicality of this control method are substantiated through a series of simulation experiments, demonstrating its robust performance.

The Impacts of Volcanic Activity on Microbial Growth—A Simulation Experiment in the Qiliao Section in Shizhu County, Chongqing, China

The impact of volcanic activity on microorganisms has always been a hot topic of discussion during geological history. Further studies are needed on the effects of volcanic activity on microbial growth in shale and the differences in nutrients provided by volcanic ash and other weathered rocks. This study’s results indicated that TOC contents at the bottom of the shale layer are 1.93–4.44% and 3.0% on average. The TOC contents at the top of the layer are 3.38–5.13% and 4.0% on average. It indicated that TOC contents at the bottom of the shale layer are smaller than the TOC contents at the top of the layer, suggesting that volcanic activity posed a long-term effect on biological growth. Seven different leachate concentrations were set in this experiment as follows: 1/10, 1/100, 1/500, 1/1000, 1/1500, 1/2000, and 1/2500. The results showed that the growth status of Pseudourostyla crassipes was affected by the addition of leachates with different concentrations compared to the control group. Additionally, the synthesis of chlorophyll a by Anabaena pseudoichthyoides was the most efficient with the 1/10 volcanic ash leachate. Through the analysis of major and trace elements in the solution before labeland after cultivation, the main elemental content of Ca2+, Mg2+, Na+, and K+ decreased by 3.8~87.24%, 75.96~92.70%, 86.56~95.67%, and 5.42~20.52% in the solution after microbial growth respectively. The trace elements B, Ba, Zn, and Fe decreased by 27.54~94.39%, 20~82.03%, 70.45~98.29%, and 99%. It was found that the B, Ba, Fe, and Zn elements decreased significantly. The nutrients from volcanic ash are the main factor promoting microbial growth. It can be indicated that the volcanic ash soaking solution has a higher content of nutrients when compared to the solubility of nutrients in volcanic ash compared to that of granite. A higher content of nutrients promotes microbial growth. The calculation results indicate that a volcanic eruption with a quantity of several 1010 m3 has a significant impact on microorganisms, lasting from tens of thousands to hundreds of thousands of years.

Research on coal rock parameter calibration based on discrete element method

To ensure that the discrete element model of the coal wall accurately reflects the actual cutting process of coal and rock during virtual prototype simulation of mining equipment, this study aims to expedite the development of intelligent mining machinery. Using coal samples from the 4602 working face of Yangcun Coal Mine, operated by Yanzhou Coal Mining Group, we conducted coal rock packing experiments and uniaxial compression tests to obtain the packing angle and compressive strength of the coal samples. Based on the experimental results, we designed Plackett–Burman experiments (PB experiments), steepest ascent experiments, and Box-Behnken experiments to study the influence of particle contact mechanics parameters and bonding mechanics parameters on the packing angle and compressive strength, using the packing angle and compressive strength as response variables. Our objective is to minimize the relative error between the simulated packing angle and the measured packing angle. We solved and optimized the parameter calibration model and conducted simulation calibration experiments based on the optimization results. A comparative analysis with the actual test results revealed that the maximum relative errors between the simulated and measured values for the packing angle and compressive strength were only 2.9% and 5.0%, respectively. Additionally, a discrete element model of a typical working face coal wall was established based on the parameters obtained from this calibration method. A bidirectional coupling model of the cutting process between the coal and rock was created using EDEM-RecurDyn to simulate the rigid-flexible coupling of the coal cutter. An experimental coal wall model was constructed based on similarity theory, and both simulation and physical experiments were conducted. The evaluation metrics for comparison were the time-domain and frequency-domain characteristics of the drum’s vibration signals. The maximum relative error for the time-domain signal characteristics between the two experimental setups was only 4.19%, while the maximum relative error for the frequency-domain signal characteristics was 3.75%. This validates the feasibility of the proposed calibration method for the discrete element coal wall model and the accuracy of the calibration results. Furthermore, it demonstrates that the constructed discrete element coal wall accurately represents the actual coal and rock properties. The virtual simulation based on this model effectively replicates the interaction process between mining machinery and coal, providing a safe, efficient, and low-cost technological approach for performance analysis of mining equipment and intelligent control of supporting devices.

Dynamic modelling and predictive position/force control of a plant-inspired growing robot.

This paper presents the development and control of a dynamic model for a plant-inspired growing robot, termed the "vine-robot," using the Euler-Lagrangian method. The unique growth mechanism of the vine-robot enables it to navigate complex environments by extending its body. We derive the dynamic equations of motion and employ model predictive control (MPC) to regulate the task space position, orientation, and interaction forces. Simulation experiments are conducted to evaluate the performance of the proposed model and control strategy. The results demonstrate that the model effectively achieves sub-millimeter precision in the position control in both static and time varying refrence trajectroies, and sub micronewton in force control.

A high-effective swarm intelligence-based multi-robot cooperation method for target searching in unknown hazardous environments

To solve target searching problems for multi-robot cooperation with inaccurate target distance perception in unknown hazardous environments, a hybrid adaptive robotic particle swarm optimizer (RPSO) and grey wolf optimizer (GWO) based algorithm with continuous virtual target guidance is proposed for high effective path planning in the searching. In the initial searching stages, both the wolf behavior-generated position and the gbest position and the pbesti positions from RPSO are employed to guide the motions of robots. With the information provided by these initial robot movement paths, a geometric model is established to generate potential targets. The K-means cluster algorithm is introduced to estimate a virtual target position online from potential targets, with new robot-presenting route information to update the history path information. Then the virtual position is employed as one of the direction components to help the robots approach the actual target. In addition, to avoid mobile robots falling into local convergence, a heuristic moving direction determination scheme is utilized to make robots circumvent obstacles in swarm motions, as well as a mutual repulsion algorithm to keep them in a scattering state. Simulation experiments on different types of unknown environments with varied robot numbers and adaptability testing for a dynamic target are carried out to verify the feasibility of the proposed target searching method with comparisons to the other three famous target searching algorithms. It is verified from the results that the presented method can not only contribute a 100% success rate in all runs of searching for a stochastic dynamic target under a limited maximal velocity, but also produce both the shortest path length and minimum iterations in terms of statistical metrics over the comparative methods.

Real-time Rectifying Flight Control Misconfiguration Using Intelligent Agent

Configurations are supported by most flight control systems, allowing users to control a flying drone adapted to complexities such as environmental changes or mission alterations. Such an advanced functionality also introduces a significant problem - misconfiguration settings. It may cause drone instability, threaten drone safety, and potentially lead to substantial financial loss. However, detecting and rectifying misconfigurations across different flight control systems is challenging because 1) (mis)configuration-related code snippets might be syntactically correct and thus hard to identify through traditional code analysis; 2) the response to each configuration varies under different flying scenarios. In this paper, we propose and implement a novel rectification approach, Nyctea , to detect instability caused by misconfigurations and conduct an on-the-fly rectification. Nyctea first continuously inspects state changes over consecutive time intervals and calculates the overall deviations to determine whether a drone is in a transition of instability to control loss. When a potential instability is reported, Nyctea instantly invokes a pre-trained intelligent agent to automatically generate proper configurations and then re-configure the drone against entering a state of loss of control. This process of reconfiguration is conducted iteratively until the instability is eliminated. We integrated Nyctea with the widely used flight control system, Ardupilot and PX4 . The simulated and practical experiment results showed that Nyctea successfully eliminates instabilities caused by 85% of misconfigurations. For each misconfiguration, Nyctea averagely generated 4 to 5 configurations to achieve a successful rectification.

Impact of temperature on sphalerite chemistry: Simulation experiments of sphalerite in MVT Zn-Pb sulfide deposits in the Beiba Dome, northern Sichuan Basin, China

Many sedimentary metal deposits are rich in organic matter, and some organic deposits are metal deposits themselves. These metal deposits, represented by MVT Zn-Pb deposits, are widely distributed in the world, and their industrial types (Pb + Zn > 5 %) are also very rich, which has important theoretical and economic significance. However, there is no systematic research on the metallogenic mechanism of MVT Zn-Pb deposits in the participating organic matter. Therefore, geochemical simulation experiments and thermodynamic calculations have been used to examine the role of organic matter in reconstructing the ore-forming process of MVT Zn-Pb sulfide deposits. Additionally, the precipitation chemistry of sphalerite under the mineralization process mediated by organic matter is explained. Guided by the evidence on the homogenization temperature of fluid inclusion, the temperature range, from 60 °C to 200 °C, has drawn a close relationship between the temperature and the ore-forming mechanism. Notably, this reflects sphalerite’s chemical composition and morphology, even assisting in interpreting its chemical formation. The results showed that 80 °C is the beginning temperature for ZnS formation calculated from the inductively coupled plasma mass spectrometry analysis (ICP-MS) under the precipitation simulation experiments with the participation of organic matter. With the increased temperature, the rate of ZnS formation showed a trend of rapid growth in the early stage (80–140 °C) and relatively slow in the later stage (160–200 °C). This finding suggests a closer coupling relationship between metal mineralization and the maturation-genesis of organic matter, mainly the formation of the ancient oil reservoir. Following the continued increase in temperature, the morphology of ZnS was significantly different from that of the scanning electron microscope (SEM), with an apparent fine granular structure at high temperature. Likewise, more ZnO was also found through the X-ray photoelectron spectroscopy analysis (XPS), reducing the purity of the ZnS in sphalerite. Thus, this study helps reveal the metallogenic mechanism of MVT Zn-Pb sulfide deposits from the viewpoint of sphalerite chemistry under the participation of organic matter from temperature change. Furthermore, it provides a valuable theoretical and experimental basis for the later prospecting of the metal deposits.

Future Changes in the Vertical Structure of Severe Convective Storm Environments over the U.S. Central Great Plains

Abstract The effect of warming on severe convective storm potential is commonly explained in terms of changes in vertically integrated (“bulk”) environmental parameters, such as CAPE and 0–6-km shear. However, such events are known to depend on the details of the vertical structure of the thermodynamic and kinematic environment that can change independently of these bulk parameters. This work examines how warming may affect the complete vertical structure of these environments for fixed ranges of values of high CAPE and bulk shear, using data over the central Great Plains from two high-performing climate models (CNRM and MPI). To first order, projected changes in the vertical sounding structure are consistent between the two models: the environment warms approximately uniformly with height at constant relative humidity, and the shear profile remains relatively constant. The boundary layer becomes slightly drier (−2% to 6% relative humidity) while the free troposphere becomes slightly moister (+1% to 3%), with a slight increase in moist static energy deficit aloft with stronger magnitude in CNRM. CNRM indicates enhanced low-level shear and storm-relative helicity associated with stronger hodograph curvature in the lowest 2 km, whereas MPI shows near-zero change. Both models strongly underestimate shear below 1 km compared to ERA5, indicating large uncertainty in projecting subtle changes in the low-level flow structure in climate models. The evaluation of the net effect of these modest thermodynamic and kinematic changes on severe convective storm outcomes cannot be ascertained here but could be explored in simulation experiments. Significance Statement Severe thunderstorms and tornadoes cause substantial damage and loss of life each year, which raise concerns about how they may change as the world warms. We typically use a small number of common atmospheric parameters to understand how these localized events may change with climate change. However, climate change may alter the weather patterns that produce these events in ways not captured by these parameters. This work examines how climate change may alter the complete vertical structure of temperature, moisture, and wind and discusses the potential implications of these changes for future severe thunderstorms and tornadoes.

An integrated decision-execution framework of cooperative control for multi-agent systems via reinforcement learning

Cooperative control is both a crucial and hot research topic for multi-agent systems (MASs). However, most existing cooperative control strategies guarantee tracking stability under various non-ideal conditions, while the path decision capability is often ignored. In this paper, the integrated decision-execution (IDE) framework is newly presented for cooperative control of multi-agent systems (MASs) to accomplish the integrated task of path decision and cooperative execution. This framework includes a decision layer and a control layer. The decision layer generates a continuous trajectory for the virtual leader to reach the target from its initial position in an unknown environment. To achieve the goal of this layer, (1) the Step-based Adaptive Search Q-learning (SASQ-learning) algorithm is proposed based on reinforcement learning to efficiently find the discrete path, (2) an Axis-based Trajectory Fitting (ATF) method is developed to convert the discrete path into a continuous trajectory for mobile agents. In the control layer, this trajectory is used to regulate the following MASs to achieve cooperative tracking control with the presence of input saturation. Simulation experiments are presented to demonstrate the effectiveness of this framework.

An Inquiry Into How Simulation Experiment Can Facilitate Speaking Skills of Foreign Language Learners

An Inquiry Into How Simulation Experiment Can Facilitate Speaking Skills of Foreign Language Learners

Effect of Substrate Rotation on The Growth Behavior and Topography of the [formula omitted] Film Deposited Over a Large Area Using DC Magnetron Sputtering with a Rectangular Target: Simulation approach and experiment

It is often challenging to achieve a uniform film with the necessary surface properties when using deposition techniques. If the film is not uniformly deposited or lacks suitable surface roughness, it may not meet performance standards. To understand and control the factors controlling the deposition process, this paper presents a simulation approach to predict the growth behavior of a Ti-film deposited on substrates during the bombardment of the target with argon ions at low pressure. The simulation of the deposition process consists of several Monte Carlo simulations, which include predicting the source of the atoms flow from the target surface and tracing its trajectories through the vacuum chamber until it falls or reaches the surface of the substrates, then predicting the thickness distribution and topography of film. After conducting a comparison between simulation predictions and experimental data, it was found that the measurements obtained using the Profilometer, Scanning Electron Microscope, and Atomic Force Microscopy were relatively close. The results indicate that substrate rotation during deposition leads to a thinner film that is more uniform and less rough, it was also found that the coefficient of variation in the film thickness distribution was improved from 3.96% in the case of a non-rotating substrate to 0.33% in the case of a rotating substrate. The proposed approach can be used to improve the design of magnetron sputtering systems and to provide theoretical guidance for optimizing the thickness distribution and topographical characteristics of the performance specifications of high-quality film without economic constraints.

Nonlinear second order plus time delay model identification and nonlinear PID controller tuning based on extended linearization method

PID control systems based on the first order plus time delay model (FOPTD), which approximate the full system dynamics, are well-accepted for a wide range of linear processes. While such controllers can be applied to overdamped nonlinear processes, they often experience excessive overshoots and oscillations for general nonlinear processes. To overcome this limitation, we propose a novel method to design a nonlinear PID controller based on the second order plus time delay (SOPTD) model. The system nonlinearity requires parameter adjustments of the linearized model across operational ranges. Hence, in this work, it is handled by the extended linearization method (ELM), ensuring local stability under the assumptions of slow and small changes in operating points. Importantly, the model achieves global input-to-output stability even without the above constraints, provided there are no structural and parametric errors. The resulting nonlinear SOPTD model can describe changes in process gain and two time constants as the operation point varies. We demonstrate the applicability of our approach with a polymerization reactor simulation and liquid-level control experiments.

Development of prebiotic steamed buns and their influence on gut microbiota and the glycemic index in simulated digestion

This study investigated the effects of adding purple potato anthocyanin (PPA) and Longjing tea powder (LTP) as prebiotics to steamed buns on gut microbiota (GM) and blood glucose levels. The effects of simulated digestion experiments on GM and blood glucose, as well as the results of high-throughput 16S rRNA sequencing, were analyzed. The combined results showed that both kinds of prebiotics significantly promoted the proliferation of Lactobacillus paracasei K56, Lactobacillus plantarum LP-ONLLY, and Lactococcus lactis LC-Plasma. The addition of 50 mg/mL LTP water extract and 2.5 mg/mL PPA water extract had considerable inhibitory effects on Escherichia coli ATCC25922, yielding inhibition zone diameters of 9.16 ± 0.02 and 11.17 ± 0.03 mm, respectively. Both PPA and LTP buns increased the abundance of probiotics in human GM. Prebiotic–xylitol steamed buns could regulate the structure of GM by decreasing the abundance of harmful bacteria (including Shigella and Enterobacter) and increasing the abundance of beneficial bacteria (including Bifidobacterium, Lactobacillus, and Veillonella). Prebiotic steamed buns could significantly reduce the rate of reducing sugar release, the estimated glycemic index (eGI), and the estimated glycemic load (eGL), providing products with a medium glycemic index and potential natural prebiotics as resources for the development of functional staple foods.

Modular hydrogel selectively adsorbs phosphates and hexavalent chromium while enabling phosphate recovery

Electroplating wastewater containing high concentrations of phosphates and hexavalent chromium Cr(VI) poses serious environmental pollution. Moreover, phosphorus, as a non-renewable resource, necessitates its recovery to meet sustainable development goals. To address this issue, this study used sodium alginate as the scaffold module, synthesized lanthanum carbonate in situ within a chitosan module to serve as the phosphate adsorption module, and employed polyethyleneimine (PEI) modules to enhance the adsorption capacity for Cr(VI), successfully fabricating a modular hydrogel (LC-CSP). LC-CSP exhibits a complex porous structure and surface morphology, forming an ultra-low-density fiber network with good strength and elasticity, ensuring uniform distribution and exposure of active sites. Under optimal conditions for single-component adsorption, LC-CSP achieved adsorption capacities of 232.02 mg/g for phosphates and 474.61 mg/g for Cr(VI). Additionally, LC-CSP demonstrated excellent reusability, retaining over 83 % of its performance after five cycles. In simulated electroplating wastewater experiments with various interfering substances, LC-CSP maintained high removal efficiencies (>90.72 %) for phosphates and Cr(VI). Post-experiment, enriched water after phosphate desorption was further treated to recover phosphorus resources in complex water environments. Multiple characterization techniques elucidated the adsorption mechanisms of LC-CSP: phosphate adsorption primarily involved ligand exchange, electrostatic interactions, and hydrogen bonding, while Cr(VI) adsorption included electrostatic interactions, hydrogen bonding, and reduction reactions. Finally, fixed-bed simulated wastewater adsorption experiments validated the technical potential of LC-CSP for practical electroplating wastewater management.

A fuzzy activation function based zeroing neural network for dynamic Arnold Map image cryptography

As an effective method for time-varying problems solving, zeroing neural network (ZNN) has been frequently applied in science and engineering. In order to improve its performances in practical applications, a fuzzy activation function (FAF) is designed by introducing the fuzzy logic technology, and a fuzzy activation function based zeroing neural network (FAF-ZNN) model for fast solving time-varying matrix inversion (TVMI) is proposed. Rigorous mathematical analysis and comparative simulation experiments with other models guarantee its superior convergence and robustness to noises. In addition, based on the proposed FAF-ZNN model, a new dynamic Arnold map image cryptography algorithm is designed. Specifically, in the new dynamic image encryption, a dynamic key matrix is introduced, and the FAF-ZNN model is applied to fast compute the inversion of the dynamic key matrix for the dynamic Arnold map image cryptography decryption process. The effectiveness of the dynamic image encryption algorithm is verified by experiment results, which enhances the security of existing image encryption algorithms.

A Nonaxial-Type Swirling Cavitating Nozzle for Exploiting Natural Gas Hydrate

Summary Natural gas hydrates (NGHs) have garnered widespread attention in the new energy sector, owing to their efficient and clean combustion properties. NGHs are ice-like substances formed by methane and water under high-pressure and low-temperature conditions, abundantly deposited in seabeds and frozen soil of highlands on Earth. However, the rock shelves of NGH reservoirs are mostly fragile and sparsely colloidal. Traditional mechanical mining methods can easily cause rock-shelf collapses, leading to mining accidents. Long-term indoor experiments and pilot mining projects have shown that cavitating nozzles can provide a feasible solution to the problem of efficient mining of NGHs. To further improve the efficiency of cavitating nozzle mining for NGHs, we have optimized and designed a nonaxial-type swirling cavitating nozzle (NASCN) based on traditional swirling cavitating nozzles (SCNs). Both numerical simulations and indoor experiments have verified the higher mining performance of this nozzle. In the numerical simulation experiments, we analyzed the cavitation performance, erosion performance, and energy consumption characteristics of different cavitating nozzles using the mixture multiphase flow model and the renormalization group (RNG) k-ε turbulence model. In the indoor experiments, we utilized a jet erosion experimental device for NGHs to analyze the erosion effects of different cavitating nozzles on hydrate samples. The results of these experiments indicate that the NASCN reduces energy consumption by 12% compared with traditional nozzles when there is little difference in cavitation performance and erosion performance. Moreover, under similar energy consumption, the NASCN improves erosion efficiency by 35.2% compared with traditional nozzles. These results demonstrate that the NASCN has good application value in the mining engineering of NGHs.

Failure behavior study of EB-PVD TBCs under CMAS corrosion and thermal shock cycles

Abstract Calcia-magnesia-alumino-silicate (CMAS) erosion has become a major obstacle, limiting the operating temperature and service life of Thermal barrier coatings (TBCs) in aircraft engines. Constructing simulation environments that replicate TBCs’ working conditions and exploring online, non-destructive detection techniques are reliable approaches to studying coatings’ failure, representing both a global research hotspot and a challenge in this field. The paper presents an initial endeavor to establish a simulation experiment for TBCs in aviation-engine within a CMAS environment. Experimental results show that electron beam physical vapor deposition (EB-PVD) Y2O3-stabilized ZrO2 (YSZ), one of the mainstream TBCs technologies, produced 20% surface spallation after 50 thermal-shock cycles under simulated CMAS corrosion conditions. Testing and analysis of the macroscopic and microscopic structures of the failed samples, combined with SEM, EDS, and XRD findings, revealed significant physical and chemical interactions between the ceramic layer and CMAS deposits, as well as phase transformation within the coatings, leading to substantial alterations in mechanical properties and ultimately causing the failure of EB-PVD YSZ.

Source localization in signed networks based on dynamic message passing algorithm

Sign characteristics of social networks play a crucial role in the process of information dissemination, and structural balance theory is a significant property of signed networks. Despite some progress in source localization, the sign features of network structures are widely overlooked. Furthermore, the impact of structural balance on tracing remains unclear. In this paper, we explore a model of information propagation in signed networks and investigate source localization in the context of a given network state snapshot. We first propose a modified signed susceptibility-infection-recovery (S-SIR) propagation model, combining the positive/negative transmission rates based on structural balance theory, which is designed to better approximate the real-world social network structure. Next, a signed dynamic message passing algorithm (S-DMP) is proposed for source localization. Simulation experiments show that the S-DMP algorithm can fully utilize both the positive and negative attributes of the edges in the signed network and outperforms other methods in terms of source localization accuracy. These experiments prove that the proposed method can be used for source localization in the early stage of information transmission. Furthermore, we find that the accuracy of source localization is highest when there is a balance between the number of positive and negative edges.

Underwater Refractive Stereo Vision Measurement and Simulation Imaging Model Based on Optical Path

When light passes through air–glass and glass–water interfaces, refraction occurs, which affects the accuracy of stereo vision three-dimensional measurements of underwater targets. To eliminate the impact of refraction, we developed a refractive stereo vision measurement model based on light propagation paths, utilizing the normalized coordinate of the underwater target. This model is rigorous in theory, and easy to understand and apply. Additionally, we established an underwater simulation imaging model based on the principle that light travels the shortest time between two points. Simulation experiments conducted using this imaging model verified the performance of the underwater stereo vision measurement model. The results demonstrate that the accuracy achieved by the new measurement model is comparable to that of the stereo vision measurement model in the air and significantly higher than that of the existing refractive measurement model. This is because the light rays from the camera’s optical center to the refraction point at the air–glass interface do not always intersect. The experiments also indicate that the deviation in the refractive index of water lead to corresponding systematic errors in the measurement results. Therefore, in real underwater measurements, it is crucial to carefully calibrate the refractive index of water and maintain the validity of the calibration results.

Interpretable ADMM-CSNet for interrupted sampling repeater jamming suppression

Interrupted sampling repeater jamming (ISRJ) is a category of coherent jamming that greatly influences radars' detection performance. Since the ISRJ has greater power than true targets, ISRJ signals can be removed in the time domain. Due to frequency band loss, grating lobes will be produced if pulse compression (PC) is performed directly, which may generate false targets. Compressive sensing (CS) is an effective method to restore the original PC signal. However, it is challenging for classic CS approaches to manually select the optimization parameters (e.g., penalty parameters, step sizes, etc.) in different ISRJ backgrounds. In this article, a network method based on the Alternating Direction Method of Multipliers (ADMM), named ADMM-CSNet, is introduced to solve the problem. Based on the strong learning capacity of the deep network, all parameters in the ADMM are learned from radar data utilizing back-propagation rather than manually selecting in traditional CS techniques. Compared with classic CS approaches, a higher ISRJ removal signal restoration accuracy is reached faster. Simulation experiments indicate the proposal performs effectively and accurately for ISRJ removal signal reconstruction.