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Based on the new data of drilling, seismic, logging, test and experiments, the key scientific problems in reservoir formation, hydrocarbon accumulation and efficient oil and gas development methods of deep and ultra-deep marine carbonate strata in the central and western superimposed basin in China have been continuously studied. (1) The fault-controlled carbonate reservoir and the ancient dolomite reservoir are two important types of reservoirs in the deep and ultra-deep marine carbonates. According to the formation origin, the large-scale fault-controlled reservoir can be further divided into three types: fracture-cavity reservoir formed by tectonic rupture, fault and fluid-controlled reservoir, and shoal and mound reservoir modified by fault and fluid. The Sinian microbial dolomites are developed in the aragonite-dolomite sea. The predominant mound-shoal facies, early dolomitization and dissolution, acidic fluid environment, anhydrite capping and overpressure are the key factors for the formation and preservation of high-quality dolomite reservoirs. (2) The organic-rich shale of the marine carbonate strata in the superimposed basins of central and western China are mainly developed in the sedimentary environments of deep-water shelf of passive continental margin and carbonate ramp. The tectonic-thermal system is the important factor controlling the hydrocarbon phase in deep and ultra-deep reservoirs, and the reformed dynamic field controls oil and gas accumulation and distribution in deep and ultra-deep marine carbonates. (3) During the development of high-sulfur gas fields such as Puguang, sulfur precipitation blocks the wellbore. The application of sulfur solvent combined with coiled tubing has a significant effect on removing sulfur blockage. The integrated technology of dual-medium modeling and numerical simulation based on sedimentary simulation can accurately characterize the spatial distribution and changes of the water invasion front. Afterward, water control strategies for the entire life cycle of gas wells are proposed, including flow rate management, water drainage and plugging. (4) In the development of ultra-deep fault-controlled fractured-cavity reservoirs, well production declines rapidly due to the permeability reduction, which is a consequence of reservoir stress-sensitivity. The rapid phase change in condensate gas reservoir and pressure decline significantly affect the recovery of condensate oil. Innovative development methods such as gravity drive through water and natural gas injection, and natural gas drive through top injection and bottom production for ultra-deep fault-controlled condensate gas reservoirs are proposed. By adopting the hierarchical geological modeling and the fluid-solid-thermal coupled numerical simulation, the accuracy of producing performance prediction in oil and gas reservoirs has been effectively improved.

Abstract Fracture is an important factor that affects the oil and gas productivity of carbonate reservoirs. Much researches have been done on the origin of high-angle fractures in carbonate reservoirs, but few efforts have been made on the genetic mechanical mechanism of low-angle fractures. Based on the seismic data, core data, conventional logging data and rock mechanics experimental data, combined with three-dimensional in situ stress field simulation methods, the features, formation geological conditions and genetic mechanical mechanism of low-angle fractures (LAFs) were analyzed by applying Coulomb–Moore criterion, Griffith criterion and non-coordination criterion. The proportion of the number of shear fractures is as high as 90.2%, while that of tensile fractures is only 9.8% in the study area. Shear fractures are mainly unfilled fractures, and tensile fractures are mainly partially filled fractures. The LAFs were formed in the second tectonic movement, in which the knee-fold structure with high in the west and low in the east developed in the study area. The buried depth of most parts of the KT-I formation is 800 m when the study area develops the knee-fold structure, with a maximum depth of 1800 m and a minimum buried depth of 70 m, and the dip angle of the steepest part of the stratum is about 20°. A large number of LAFs were formed in the study area under the joint influence of tectonics and abnormally high pressure of water, including near-horizontal LAFs in the non-weak fabrics section (type I low-angle shear fractures), the LAFs having a certain angle with bedding in the non-weak fabrics section (type II low-angle shear fractures) and near-horizontal LAFs in the weak fabrics section (type III low-angle shear fractures). The formation of type I and type II low-angle shear fractures follows the Coulomb–Moore criterion. Type I low-angle shear fractures are formed in strata with a certain dip angle, while type II low-angle shear fractures are formed in near-horizontal strata. Type III low-angle shear fractures are formed under the comprehensive influence of pre-existing weak fabrics and strong horizontal extrusion, which follows the non-coordination criterion. Low-angle tensile fractures are mainly caused by abnormally high pressure and reverse faults in the study area, following Griffith’s criterion. The research in this paper not only reveals the formation mechanical mechanism of LAFs in pre-salt carbonate reservoirs but also provides guidance for the prediction of LAFs and solving the problem of water channeling caused by LAFs in oil fields.

The focus of this paper was to explore the stability issues associated with delayed neural networks (DNNs). We introduced a novel approach that departs from the existing methods of using quadratic functions to determine the negative definite of the Lyapunov-Krasovskii functional's (LKFs) derivative $ \dot{V}(t) $. Instead, we proposed a new method that utilizes the conditions of positive definite quadratic function to establish the positive definiteness of LKFs. Based on this approach, we constructed a novel the relaxed LKF that contains delay information. In addition, some combinations of inequalities were extended and used to reduce the conservatism of the results obtained. The criteria for achieving delay-dependent asymptotic stability were subsequently presented in the framework of linear matrix inequalities (LMIs). Finally, a numerical example confirmed the effectiveness of the theoretical result.

This research investigated the fixed-time (FXT) synchronization of fractional-order fuzzy cellular neural networks (FCNNs) with delays and interactions based on an enhanced FXT stability theorem. By conceiving proper Lyapunov functions and applying inequality techniques, several sufficient conditions were obtained to vouch for the fixed-time synchronization (FXTS) of the discussed systems through two categories of control schemes. Moreover, in terms of another FXT stability theorem, different upper-bounding estimating formulas for settling time (ST) were given, and the distinctions between them were pointed out. Two examples were delivered at length to demonstrate the conclusions.

Automated Guided Vehicles (AGVs) are the fastest commercially available application of unmanned driving technology, and the research significance of unmanned vehicle technology remains substantial. This paper investigates the driving mode of AGVs and proposes a method to extend the kinematic model of center-driven unmanned vehicles to front-wheel drive. This change in driving force enables unmanned vehicles to achieve faster tracking and higher consistency, solving the problems of long tracking time and insufficient accuracy in complex environments and reducing production costs. By analyzing the posture relationship of the unmanned vehicle system during movement, we established a posture error system to analyze the trajectory tracking problem. Utilizing Lyapunov stability theory and the concept of backstepping, we designed a control scheme that uses linear velocity and heading angular velocity as variables for the posture error system. This control scheme aims to stabilize the system and achieve synchronized trajectory tracking control of the unmanned vehicle. The impact of control parameters in the controller on tracking performance is also discussed. The final experimental simulation results show that the system error stabilizes, and the unmanned vehicle accurately follows the predetermined trajectory, verifying the feasibility of our proposed method and control scheme.

To construct Caputo fractional-order time-delayed cellar neural networks (FOTDCNNs) that characterize real environments, this article introduces partial uncertainties, fuzzy operators, and nonlinear activation functions into the network models. Specifically, both the fuzzy AND operator and the fuzzy OR operator are contemplated in the master–slave systems. In response to the properties of the considered cellar neural networks (NNs), this article designs a new class of mixed control protocols that utilize both the error feedback information of systems and the sampling information of impulse moments to achieve network synchronization tasks. This approach overcomes the interference of time delays and uncertainties on network stability. By integrating the fractional-order comparison principle, fractional-order stability theory, and hybrid control schemes, readily verifiable asymptotic synchronization conditions for the studied fuzzy cellar NNs are established, and the range of system parameters is determined. Unlike previous results, the impulse gain spectrum considered in this study is no longer confined to a local interval (−2, 0) and can be extended to almost the entire real number domain. This spectrum extension relaxes the synchronization conditions, ensuring a broader applicability of the proposed control schemes.

This article explores the finite-time control problem associated with a specific category of non-homogeneous hidden semi-Markov jump systems. Firstly, a hidden semi-Markov model is designed to characterize the asynchronous interactions that occur between the true system mode and the controller mode, and emission probabilities are used to establish relationships between system models and controller modes. Secondly, a novel piecewise homogeneous method is introduced to tackle the non-homogeneous issue by taking into account the time-dependent transition rates for the jump rules between different modes of the system. Thirdly, an asynchronous controller is developed by applying Lyapunov theory along with criteria for stochastic finite-time boundedness, ensuring the specified H∞ performance level is maintained. Finally, the effectiveness of this method is verified through two simulation examples.

To construct a nonlinear fractional-order neural network reflecting the complex environment of the real world, this paper considers the common factors such as uncertainties, perturbations, and delays that affect the stability of the network system. In particular, not only does the activation function include multiple time delays, but the memristive connection weights also consider transmission delays. Stemming from the characteristics of neural networks, two different types of discontinuous controllers with state information and sign functions are devised to effectuate network synchronization objectives. Combining the finite-time convergence criterion and the theory of fractional-order calculus, Mittag-Leffler synchronization conditions for fractional-order multi-delayed memristive neural networks (FMMNNs) are derived, and the upper bound of the setting time can be confirmed. Unlike previous jobs, this article focuses on applying different inequality techniques in the synchronous analysis process, rather than comparison principles to manage the multi-delay effects. In addition, this study removes the restrictive requirement that the activation function has a zero value at the switching jumps, and the discontinuous control protocol in this paper makes the networks achieve synchronization over a finite time, with some advantages in terms of the convergence speed.

Chaotic or hyperchaotic systems have a significant role in engineering applications such as cryptography and secure communication, serving as primary signal generators. To ensure stronger complexity, memristors with sufficient nonlinearity are commonly incorporated into the system, suffering a limitation on the physical implementation. In this paper, we propose a new four-dimensional (4D) hyperchaotic system based on the linear memristor which is the most straightforward to implement physically. Through numerical studies, we initially demonstrate that the proposed system exhibits robust hyperchaotic behaviors under typical parameter conditions. Subsequently, we theoretically prove the existence of solid hyperchaos by combining the topological horseshoe theory with computer-assisted research. Finally, we present the realization of the proposed hyperchaotic system using an FPGA platform. This proposed system possesses two key properties. Firstly, this work suggests that the simplest memristor can also induce strong nonlinear behaviors, offering a new perspective for constructing memristive systems. Secondly, compared to existing systems, our system not only has the largest Kaplan-Yorke dimension, but also has clear advantages in areas related to engineering applications, such as the parameter range and signal bandwidth, indicating promising potential in engineering applications.

Abstract This article studies the observer‐based integral sliding mode control (ISMC) problem for continuously uncertain neutral semi‐Markovian jumping systems with time‐varying delays (TDs). Firstly, based on the designed state observer, an ISMC method is proposed for the first time. Then, building an appropriate stochastic Lyapunov–Krasovskii functional by taking into account more information about TDs, a novel sufficient condition is established for the robustly stochastic stability of the overall system made up of the error system and the sliding mode dynamics system. Furthermore, an ISMC law is devised to ensure the reachability of the integral sliding surface in a finite time. Additionally, the proposed method can be reduced to the known state case, thus, the ISMC problem for continuously uncertain neutral semi‐Markovian jumping systems with TDs is also investigated in this article. Finally, three numerical examples explain the effectiveness of the results obtained.

This paper shows solicitude for the generalized projective synchronization of Caputo fractional-order uncertain memristive neural networks (FOUMNNs) with multiple delays. By extending the constant scale factor to the time-varying function matrix, we establish an extraordinary synchronization mode called time-varying function matrix projection synchronization (TFMPS), which is a generalized version of traditional matrix projection synchronization, modified projection synchronization, complete synchronization, and anti-synchronization. To achieve the goal of TFMPS, we design a novel mixed controller including the open loop feedback control and impulsive control, which employs the state information in the time-delayed interval and the sampling information at the impulse instants. It has a prominent advantage that impulse intervals are not restricted by time delays. To establish the connection between the error system and the auxiliary system, a generalized fractional-order comparison theorem with time-varying coefficients and impulses is established. Applying the stability theory, the comparison theorem, and the Laplace transform, new synchronization criteria of FOUMNNs are acquired under the mixed impulsive control schemes, and the derived synchronization theorem and corollary can effectively expand the correlative synchronization achievements of fractional-order systems.

Considering the diversity of battery data under dynamic test conditions, the stability of battery working data is affected due to the diversity of charge and discharge rates, variability of operating temperature, and randomness of the current state of charge, and the data types are multi-sourced, which increases the difficulty of estimating battery SOH based on data-driven methods. In this paper, a lithium-ion battery state of health estimation method with sample transfer learning under dynamic test conditions is proposed. Through the Tradaboost.R2 method, the weight of the source domain sample data is adjusted to complete the update of the sample data distribution. At the same time, considering the division methods of the six auxiliary and the source domain data set, aging features from different state of charge ranges are selected. It is verified that while the aging feature dimension and the demand for target domain label data are reduced, the estimation accuracy of the lithium-ion battery state of health is not affected by the initial value of the state of charge. By considering the mean absolute error, mean square error and root mean square error, the estimated error results do not exceed 1.2% on the experiment battery data, which highlights the advantages of the proposed methods.

In this paper, a reliable $ H_\infty $ control approach under a novel adaptive event-triggering mechanism (AETM) considering actuator faults for networked control systems (NCSs) is addressed. Firstly, the actuator faults are described by a series of independent stochastic variables obeying a certain probability distribution. Secondly, a novel AETM is presented. The triggering threshold can be dynamically adjusted according to the fluctuating trend of the current sampling state, resulting in saving more limited network resources while preserving good control performance. As a result, considering the packet dropout and packet disorder caused by the communication network, the sampling-data model of NCSs with AETM and actuator faults is constructed. Thirdly, by removing the involved auxiliary function and replacing it with a sequence of integrals only related to the system state, a novel integral inequality can be used to reduce conservatism. Thus, a new stability criterion and an event-triggered reliable $ H_\infty $ controller design approach can be obtained. Finally, the simulation results are presented to verify the progressiveness of our proposed approach.

Abstract In recent years, abundant natural gas has been found in microbial carbonates in the fourth member of the Leikoupo Formation in Western Sichuan Basin. In this study, from the observation of 626 microbial thin sections, four types of microbial carbonates are classified based on the differences of mesostructures. Among them, thrombolites and stromatolites are subdivided into eight types based on the differences of microstructures. Six types of microbial microstructure association (MSA) are identified, and are mainly developed in microbial mounds. The energy of sedimentary environment and hydrodynamic conditions of them from low to high is MSA-5, MSA-1, MSA-6, MSA-3, MSA-4 and MSA-2. Because of the arid climate in the Annie Period, a restricted platform are developed in the upper sub-member of the Leikoupo Formation in Western Sichuan Basin, and the sedimentary facies are lagoon (gypsiferous lagoon or salt lake in evaporate conditions), microbial mounds, shoals, inner platform shoals and open sea from the east to the west. Microbial microstructures not only affect the pore evolution of microbial carbonate reservoirs, but also affect the diagenesis of microbial carbonate reservoirs of the fourth member of the Leikoupo Formation.

In this work, a prescribed-time second-order sliding mode (SOSM) controller is constructed to address a class of nonlinear systems in the presence of asymmetric output constraints. To handle the asymmetric constraints, a frequently-used asymmetric barrier Lyapunov function is employed. Meanwhile, a time-varying scaling function is introduced to ensure the prescribed-time convergence of the closed-loop sliding mode system. On this basis, a prescribed-time SOSM controller is constructed by inserting the asymmetric BLF and time-varying scaling function into a modified power integrator technique. The rigorous theoretical analysis indicates that under the proposed prescribed-time SOSM control scheme, the overall system is prescribed-time stable without violating the asymmetric constraints. The distinguishing feature is that the convergence time of the overall system can be specified beforehand and does not depend on any system initial conditions. Finally, an academic simulation example is provided to demonstrate the effectiveness of the presented prescribed-time control scheme.

This paper investigates the issue of asynchronous $ H_{\infty} $ tracking control for nonlinear semi-Markovian jump systems (SMJSs) based on the T-S fuzzy model. Firstly, in order to improve the performance of network control systems (NCSs) and the efficiency of data transmission, this paper adopts a double quantization strategy which quantifies the input and output of the controllers. Secondly, for the purpose of reducing the burden of network communication, an adaptive event-triggered mechanism (AETM) is adopted. Thirdly, due to the influence of network-induce delay, the system mode information can not be transmitted to the controller synchronously, thus, a continuous-time hidden Markov model (HMM) is established to describe the asynchronous phenomenon between the system and the controller. Additionally, with the help of some improved Lyapunov-Krasovski (L-K) functions with fuzzy basis, some sufficient criteria are derived to co-guarantee the state stability and the $ H_{\infty} $ performance for the closed-loop tracking control system. Finally, a numerical example and a practical example are given to verify the effectiveness of designed mentality.

This paper considers the finite-time cluster synchronization (FTCS) problem for a general class of nonlinear coupled neural networks (NCNN) with time-varying delays based on a new finite-time analysis method. By employing the theories of finite-time control and Lyapunov functional, novel sufficient conditions on FTCS of NCNN with transmission delays and coupled delays are proposed. Not resembling several previous results, the settling time estimation schemes are established without using classical finite-time stability lemmas. The new finite-time analysis method used in this paper combines the inequality method and Lyapunov functional theory and then obtains corresponding results according to the definition of finite-time stability. Furthermore, the results of proposed FTCS are obtained in a more general framework, which has a wider range of practical applications. Finally, a numerical example is presented to confirm the validity of the obtained control scheme.

The Late Permian strata of the Xuanwei Formation in the eastern Yunnan region exhibit extensive diverse morphological features within siderite deposits. These variations in siderite deposits suggest potential differences in their formation processes. In this study, fieldwork and comprehensive indoor studies revealed four distinct forms of siderite deposits: stratiform-laminated, lens-like nodule, sandstone cementation, and fracture filling. The stratiform-laminated siderite, varying in color from bluish-grey to dark grey, is composed of uniformly sized microcrystalline to fine-grained siderite along with detrital matter, displaying precise layering and banding structures that suggest direct deposition from cyclic iron-rich seawater under reducing conditions. Lens-like-nodule siderite, which appears grey-yellow, is composed of mud microcrystalline siderite, medium to coarse-grained pseudo-ooids, and glauconite. It shows conformable distribution characteristics resulting from the diagenetic differentiation of iron-rich sediments under reducing conditions during the diagenetic and early diagenetic periods. Siderite as sandstone cementation exhibits a yellow-brown color and consists of dispersed colloidal siderite and cemented siderite clumps that fill intergranular pores of detrital particles. It precipitated under reducing conditions within those intergranular pores. Siderite filling fractures typically appear as vein-like or network-like structures intersecting bedding at large angles. They exhibit grain structures with significant variations in size. These siderite deposits exhibit exceptional purity and result from siderite dissolution during sedimentary periods, followed by reprecipitation within regional extensional fractures during the diagenetic phase. The primary occurrence of siderite deposits in the study area is within coal-bearing strata, as revealed by the integration of sedimentary profiles and sedimentary facies analysis. The coal-bearing strata, influenced by the Emeishan large igneous province, underwent iron enrichment during and after volcanic eruptions while developing a reducing environment, which was facilitated by abundant vegetation. Consequently, geological processes led to siderite layers, lens-like siderite nodules, and siderite cementation. The Yanshan orogeny induced extensive high-angle fracture development in epigenetic coal-bearing strata, facilitating fluid circulation and the redistribution of soluble siderite. This geological activity resulted in the formation of vein-like structures composed of siderite.

In this paper, the issue of security sliding mode control (SMC) is addressed for networked nonlinear singular systems with semi-Markov switching parameters via the Takagi-Sugeno (T-S) fuzzy strategy. The goal is to guarantee a good steady performance of dynamical systems under the framework of deception attacks. First, the common sliding surface is proposed to avoid the potential instability caused by repeated jumps of sliding surface. Then, based on the partly unknown transition rate, sufficient conditions are constructed to realize the stochastic admissibility for the corresponding system. Moreover, a fuzzy security SMC law is designed to drive the state trajectory onto the specified sliding region. Finally, a practical example is shown to validate the proposed algorithm.

This work develops some novel approaches to investigate the stability analysis issue of linear systems with time-varying delays. Compared with the existing results, we give three innovation points which can lead to less conservative stability results. Firstly, two novel integral inequalities are developed to deal with the single integral terms with delay-dependent matrix. Secondly, a novel Lyapunov-Krasovskii functional with time-varying delay dependent matrix, rather than constant matrix is constructed. Thirdly, two improved stability criteria are established by applying the newly developed Lyapunov-Krasovskii functional and integral inequalities. Finally, three numerical examples are presented to validate the superiority of the proposed method.

Abstract In this letter, a novel sext‐band bandpass filter (BPF) using two split‐type multiple‐mode resonators is proposed. The filter is composed of two sets of resonators which are respectively designed to realize Filter 1 with two passbands and Filter 2 with quad passbands. Then the two BPFs are combined by parallel coupling feed lines for sext‐band responses, and each of the six centre frequencies in the proposed sext‐band BPF is able to be controlled due to the design freedom. To validate the design and analysis, a prototype filter has been fabricated with six passbands centred at 1.88/2.59/3.48/5.26/5.82/6.75 GHz. The measured result of the fabricated filter agrees well with the simulation, which shows that the proposed structure is a good candidate for sext‐band BPF designs and validates the proposed design flow well.

Support vector machine (SVM) is one of the most powerful technologies of machine learning, which has been widely concerned because of its remarkable performance. However, when dealing with the classification problem of large-scale datasets, the high complexity of SVM model leads to low efficiency and become impractical. Due to the sparsity of SVM in the sample space, this paper presents a new parallel data geometry analysis (PDGA) algorithm to reduce the training set of SVM, which helps to improve the efficiency of SVM training. The PDGA introduce Mahalanobis distance to measure the distance from each sample to its centroid. And based on this, proposes a method that can identify non support vectors and outliers at the same time to help remove redundant data. When the training set is further reduced, cosine angle distance analysis method is proposed to determine whether the samples are redundant data, ensure that the valuable data are not removed. Different from the previous data geometry analysis methods, the PDGA algorithm is implemented in parallel, which greatly saving the computational cost. Experimental results on artificial dataset and 6 real datasets show that the algorithm can adapt to different sample distributions. Which significantly reduce the training time and memory requirements without sacrificing the classification accuracy, and its performance is obviously better than the other five competitive algorithms.

The classification based on Electroencephalogram (EEG) is a challenging task in the brain-computer interface (BCI) field due to data with a low signal-to-noise ratio. Most current deep learning based studies in this challenge focus on designing a desired convolutional neural network (CNN) to learn and classify the raw EEG signals. However, only CNN itself may not capture the highly discriminative patterns of EEG due to a lack of exploration of attentive spatial and temporal dynamics. To improve information utilization, this study proposes a Dual Attentive Fusion Model (DAFM) for the EEG-based BCI. DAFM is employed to capture the spatial and temporal information by modeling the interdependencies between the features from the EEG signals. To our best knowledge, our method is the first to fuse spatial and temporal dimensions in an interactive attention module. This module improves the expression ability of the extracted features. Extensive experiments implemented on four publicly available datasets demonstrate that our method outperforms state-of-the-art methods. Meanwhile, this work also indicates the effectiveness of Dual Attentive Fusion Module.

In recent years, Ordos Basin has been the largest petroleum field in China and represents significant potential for gas exploration in the Lower Paleozoic carbonate reservoirs. Taking the central-eastern Ordos Basin as an example, this work studies the reservoir characteristics, origin, and controlling factors of the crystalline dolomites of the Sanshanzi Formation by means of petrological and geochemical studies. The Sanshanzi Formation dolostones comprise three kinds: crystalline dolostone with grain-shadow (Rd1), very fine–fine crystalline dolostone (Rd2), and medium-coarse crystalline dolostone (Rd3). The pore spaces include intercrystalline pores and intercrystalline solution pores, residual intergranular pores, vugs, and fractures. The medium-coarse crystalline dolostone is the best reservoir. The average porosity is 4.61%, and the average permeability is 0.91 mD. The sedimentary environment in the upper Cambrian was a large area of tidal flats. Under the influence of penecontemporaneous meteoric water leaching, dissolution developed at the top of the dolomite flat–lagoon depositional cycle, with vugs developing in the very fine–fine crystalline dolostone. Penecontemporaneous dolomitization enhanced the compaction resistance and effectively preserved the pore space of the reservoir. In addition, recrystallization improved the crystalline texture, turning the intergranular pores into residual intergranular pores and intercrystalline micropores into intercrystalline pores. In this case, the very fine–fine crystalline dolostone was transformed into medium-coarse crystalline dolostone. The results of this study provide a case for the in-depth understanding of the development and preservation mechanism of ancient dolomite reservoirs and also point to the research pathways for oil and gas exploration in the Cambrian in the Ordos Basin in the future.

This study discusses the global asymptotical synchronization of fractional-order multi-delay coupled neural networks (FMCNNs) via hybrid control schemes. In addition to internal delays and different coupling delays, more importantly, multi-link complicated structures are introduced into our model. Unlike most existing works, the synchronization target is not the special solution of an isolated node, and a more universally accepted synchronization goal involving the average neuron states is introduced. A generalized multi-delay impulsive comparison principle with fractional order is given to solve the difficulties resulting from different delays and multi-link structures. To reduce control costs, a pinned node strategy based on the principle of statistical sorting is provided, and then a new hybrid impulsive pinning control method is established. Based on fractional-order impulsive inequalities, Laplace transforms, and fractional order stability theory, novel synchronization criteria are derived to guarantee the asymptotical synchronization of the considered FMCNN. The derived theoretical results can effectively extend the existing achievements for fractional-order neural networks with a multi-link nature.

This article proposes a discrete proportional-integral-derivative (PID) load frequency control (LFC) scheme to investigate the dissipative analysis issue of restructured wind power systems via a non-fragile design approach. Firstly, by taking the different power-sharing rates of governors into full consideration, a unified model is constructed for interconnected power systems containing multiple governors. Secondly, unlike existing LFC schemes, a non-fragile discrete PID control scheme is designed, which has the performance of tolerating control gain fluctuation and relieving the huge computational burden. Further, by constructing a discrete-type Lyapunov–Krasovskii functional, improved stability criteria with a strict dissipative performance index are established. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed control method.

The flight control of UAVs can be implemented and theoretically analyzed using multi-agent systems (MASs), and tracking control is one of the important control technologies. This paper studies multi-group tracking control for multi-agent systems of UAV, in which the control scheme combines event-triggered technology and impulsive theory. The advantage of multi-group tracking control lies in its ability to realize multiple groups of tracking targets and make the UAV complete multiple groups of tasks. The tracking control makes use of a novel dynamic event-triggered control (DETC) proposed in this paper, in which it can better regulate and optimize the triggering frequency by adjusting the parameters. Furthermore, several forms of network interference that may affect the safety of UAV tracking control have also been resolved. Lastly, simulations are presented with numerical examples to showcase the efficacy of the proposed tracking control.

From the classic automatic guided vehicle system, the system of the unmanned rear-wheel drive vehicle (URWDV) based on a dynamic analysis is studied. In the URWDV system, the relationship among the position information, velocity, and the heading angular velocity of the unmanned vehicle is established in the plane coordinate system and the coordinate system centered vehicle itself. The velocity and heading angular velocity values are obtained through a dynamic analysis and are used as control parameters. The synchronized tracking control of the unmanned vehicle is realized by the control scheme of the velocity and the heading angular velocity. Finally, the simulation examples show the effectiveness of the tracking control.

This article addresses the issue of drive–response synchronization in fractional-order multi-link memristive neural networks (FMMNN) with multiple delays, under hybrid impulsive feedback control. To address the impact of multiple delays on system synchronization, an extended fractional-order delayed comparison principle incorporating impulses is established. By leveraging Laplace transform, Mittag–Leffler functions, the generalized comparison principle, and hybrid impulsive feedback control schemes, several new sufficient conditions are derived to ensure synchronization in the addressed FMMNN. Unlike existing studies on fractional-order single-link memristor-based systems, our response network is a multi-link model that considers impulsive effects. Notably, the impulsive gains αi are not limited to a small interval, thus expanding the application range of our approach (αi∈(−2,0)∪(−∞,−2)∪(0,+∞)). This feature allows one to choose impulsive gains and corresponding impulsive intervals that are appropriate for the system environment and control requirements. The theoretical results obtained in this study contribute to expanding the relevant theoretical achievements of fractional-order neural networks incorporating memristive characteristics.

In the existing research on time-series event prediction (TSEP) methods, most of the work is focused on improving the algorithm for classifying subsequence sets (sets composed of multiple adjacent subsequences). However, these prediction methods ignore the timing dependence between the subsequence sets, nor do they capture the mutual transition relationship between events, the prediction effect on a small sample data set is very poor. Meanwhile, the sequence labeling problem is one of the common problems in natural language processing and image segmentation. To solve this problem, this paper proposed a new framework for time-series event prediction, which transforms the event prediction problem into a labeling problem, to better capture the timing relationship between the subsequence sets. Specifically, the framework used a sequence clustering algorithm for the first time to identify representative patterns in the time series, then represented the set of subsequences as a weighted combination of patterns, and used the eXtreme gradient boosting algorithm (XGBoost) for feature selection. After that, the selected pattern feature was used as the input of the long-term short-term memory model (LSTM) to obtain the preliminary prediction value. Furthermore, the fully-linked conditional random field (CRF) was used to smooth and refine the preliminary prediction value to obtain the final prediction result. Finally, the experimental results of event prediction on five real data sets show that the CX-LC method has a certain improvement in prediction accuracy compared with the other six models.

This paper is concerned with event-driven filtering for networked switched systems with unreliable links. In order to improve the anti-interference performance of the filter, stochastic cyber-attacks and random parametric perturbations are considered in the filter design. The update of the measurement is determined by a newly proposed intermittent event-driven mechanism, which helps to save limited bandwidth resources. By constructing a new Lyapunov-Krasovskii functional (LKF) without positivity and continuity constraints in a triggering interval, sufficient conditions are obtained such that the filtering error system is exponentially mean-square stable with a weighted $H_{\infty }$ performance. The resilient filter and event-driven mechanism are then co-designed. Finally, the results obtained are applied to a switched circuit system.

This paper focuses on the design of new optimal control algorithms for battery-supercapacitor hybrid energy storage system (HESS) with input saturation and time delay. Different from the existing research results, a new state feedback system to track the output DC bus voltage and battery current is developed via the state-space averaged model method. By fully considering the state delay information, a proper Lyapunov-Krasovskii functional (LKF) is constructed. In order to reduce the inherent conservation, Wirtinger-based integral inequality is firstly employed to establish novel stability criteria of battery-supercapacitor HESS. Meanwhile, for the sake of shortening the transition time and realizing the smooth transition of some key variables during the load switch, new optimal control algorithms with actuator saturation can be achieved in terms of linear matrix inequalities (LMIs). Finally, numerical results are presented to verify the effectiveness and superiority of the analysis results.

This paper studies the problem of H filtering for nonlinear Markov jump systems based on Takagi-Sugeno model. Firstly, we propose a hybrid event-triggered mechanism with an adjustable threshold, which not only helps to save more limited communication resources, but also excludes Zeno behavior while preserving the merits of continuous triggering. Secondly, given the threat of cyber-attacks to network security, a stochastic variable is introduced to describe the considered deception attacks in filter design. Thirdly, a less restrictive Lyapunov-Krasovskii functional (LKF), which is not required to be continuous and positive definite in a triggering interval, is constructed to establish sufficient condition on the exponential mean-square stability for the filtering error system with a weighted H∞ performance. Meanwhile, co-design of the desired filter and event-triggered mechanism is achieved. Finally, a tunnel diode circuit system is provided to illustrate the effectiveness and advantage of the obtained results.

Tracking trajectory with high precision under wind perturbations is a difficult problem of a quadrotor. In response to this problem, a novel wind perturbation estimator using neural network is proposed. The structure of wind estimator is designed based on deep understanding of a quadrotor, and training process is optimized to solve overfitting problem in the presence of sensor noise. With the consideration of wind perturbations and rotor dynamics, cascaded Lyapunov functions are used to derive full backstepping controller. Compared with traditional wind estimator, the proposed estimator is simple to be carried out and shows better robustness to sensor noise. To the best of our knowledge, the proposed controller is more robust to wind with less power cost than existing controllers. A series of simulations show the process to optimize wind estimator, and comparison between different controllers demonstrates that the proposed controller is robust to wind and energy-efficient. Finally, experiments have strengthened the effectiveness of our proposed method.

Social networks are becoming increasingly collaborative and interdependent for achieving a broader market and profits. However, the coupled social networks which caused by the collaboration and interdependence bring more significant security risks. Rumors will spread more effectively and more rampant in coupled social networks than in separate social networks. Rumors spread in a single social network have attracted considerable attention. In coupled networks, whereas, the research remains to be explored. In coupled networks, in view of the fact that the public will face more rumors, this paper proposes a SIHR rumor spreading model with the “hesitation” psychological state for studying the dynamic course of rumors. The mean-field equation is given for describing and analyzing the diffusion course. The various propagation performances of SIHR model are explained by theoretical and experimental analysis. At last, targeted security scheme is discussed.

In this paper, the robust exponential $H_{\infty }$ fault tolerant control problem is investigated, which is concerned with uncertainties, disturbances and actuator failures. Determined by whether the actuator fails or not, the continuous-time system is remodeled as a switched system. Then a sampled-data controller is designed. Through Lyapunov functional theory and the admissible edge-dependent average dwell time method, some sufficient conditions are derived to ensure that the closed-loop system is robustly exponentially stable with exponential $H_{\infty }$ performance. The corresponding controller gains can also be obtained via linear matrix inequalities (LMIs). Finally, two examples are presented to verify the validity of the relevant results.

This paper focuses on the asymptotic stability (AS) problem of the delay dynamical system (DDS). An improved delay-product-type functional (DPTF) strategy is applied to construct a more general Lyapunov-Krasovskii functional (LKF). This method introduces more relevant information and improves the information capacity of LKF. In addition, the definition of the conditions $V_{1} (x_{t}) $ and $V_{5} (x_{t}) $ are further relaxed, thereby developing new relaxed conditions. Then, based on the appropriate integral inequalities and the reciprocally convex combination lemma (RCCL). Finally, the feasibility of the method in this paper is verified based on two numerical examples.

The fracture-cave systems of carbonate reservoirs have almost stored 30% of recoverable oil and gas around the world. However, it is still doubtful about the mechanism of hydrocarbon migration in the fracture-cave systems. In this work, deducing from the Eshelby’ solution, we derived the equation to calculate the bulk strain of elliptic cylinder caves applied by stresses. Calculated results indicate that the bulk strain of caves negatively increases with the radius ratio of the elliptic cave axials under fixed stresses. In the case of the effective horizontal stress increasing from 30 MPa to 80 MPa, the bulk strain difference of a cave could be up to 0.5%. It may result in 0.4% of the total cave volume of fluid transporting through the fracture-cave systems within such a stress cycle. Since the tectonic stresses transform in a cyclic way, the volumetric ratio of new-to-old fluid in a cave would increase with the number of stress cycles. As a result, we proposed that the periodic fluid flow induced by cyclic tectonic stresses could be an important mechanism for hydrocarbon migration in the fracture-cave systems of carbonate reservoirs.

This paper studies the modeling and exponential stability problems for markovian jumping memristor-based neural networks (MJMNNs) via event-triggered impulsive control scheme (ETICS). The purpose is to design memristor-based neural networks (MNNs) which has markovian jumping parameters and hybrid time-vary delays to make the MNNs more general. Meanwhile, a state estimator is introduced to estimate system states through a vailable output measurements. Furthermore, the proposed event-triggered scheme (ETS), which is also determined by markovian parameters, is used to determine whether there is an impulse and whether the system need to transmit the sampled state information. Then, by using Lyapunov-Krasovskii functional (LKF) and an improved inequality, exponential stable criterions are established. Finally, a numerical example is given to support the results.

In this article, the stabilization issues for probabilistic Boolean Networks (PBNs) with time delays are discussed. This article's objective is designing an efficient algorithm to choose suitable nodes to be pinning controlled for PBNs with time delays. By using the semi-tensor product (STP) of matrices, a PBN with time delays can be converted into a discrete-time linear system, and the transition matrix also can be obtained. Then, the necessary and sufficient conditions in the form of algebraic expression are given for the existence and solvability of the pinning feedback controllers with minimum pinning nodes for PBNs with time delays. Besides, three algorithms are proposed for designing and solving minimum pinning controllers.

Aiming at the attitude control problem of three-axis stabilization gimbal, an algorithm combining interval type-2 fuzzy control and sliding-mode control was designed. Firstly, the mathematical model of the three-axis stabilization gimbal actuator was established after analyzing the mechanical structure of the three-axis stabilization gimbal. Then, a sliding-mode controller was designed based on the position ring of the actuator of three-axis stabilization gimbal. An interval type-2 control was used to reduce chattering in sliding-mode control. The switching function and its derivative are taken as the input of fuzzy control, and the rate of change of the approach law is taken as the output of fuzzy control. Fuzzy control can adjust the gain of sliding-mode surface dynamically, enhance the adaptability on external random disturbance, improve the speed of convergence and weaken chattering. The simulation results show that the output of the controller designed in this paper is more stable, and the tracking of the three-axis stabilization gimbal is more rapid and accurate.

Sliding Mode Control and Adaptive Control are widely studied in the area of Rotor UAV in recent years. Although the performance of Rotor UAV with these controllers show high command tracking ability and good robustness, they are limited by model accuracy so that they cannot take place PID. In this paper, a novel method named State damping control is proposed to be a candidate for the traditional PID method. Our proposed State Damping Control is inspired by the format of air resistance. The method is based on the general idea that resistance will make a system easy to stabilize. State damping control is independent of model accuracy and just uses three parameters to control attitude, so it is easy to realize. Krasovskii Theorem is used to give the evidence that State damping control is asymptotic stable in our considered state space. Finally, simulations are implemented in C++ on VS2017, it demonstrates that State damping control is easy to be tuned and robust to wind attack and inertial parameters. Compared with PID, our proposed method is robust to wind disturbances obviously.

This work focuses on the issue of static output feedback control for Takagi-Sugeno (T-S) fuzzy systems in the discrete-time domain. Both the fading channel and actuator faults are determined by a set of stochastic variables. Specifically, an actuator fault is described by a nonhomogeneous Markov chain, and fading channels are characterized by the l-order Rician fading model. Using the Lyapunov-Krasovskii function, sufficient conditions are established, and controller gains are developed. Finally, the practical mass-spring-damping model is expressed to verify the practicability of the theoretical results.

This study focuses on the input-to-state stability issue for impulsive Gilpin-Ayala competition model with reaction diffusion and delayed feedback. By using a fixed point theorem, variational method and Lyapunov function method, the unique existence of globally asymptotical input-to-state stability of positive stationary solution is established under Dirichlet zero boundary value. Remarkably, it is the first paper to derive the unique existence of the stationary solution of Reaction-Diffusion (RD) Gilpin-Ayala competition model, which is globally asymptotical input-to-state stability. In the end, simulation results are presented to validate the effectiveness and feasibility of the proposed results.

In this paper, the operating mechanism of proposed bandpass filters with a single multimode resonator loaded with branches is introduced. Based on the design procedure, the center frequencies of the proposed bandpass filters can be controlled due to the design freedom. Meanwhile, the proposed bandpass filters (BPFs) feature compact sizes and small insertion loss. To validate the design and analysis, a prototype was fabricated and measured with six passbands centered at 1.23/1.76/2.38/4.24/5.23/6.75 GHz. The measured result of the fabricated filter agrees well with the simulation, which indicates that the proposed structure can serve as a potential candidate for multiband BPF designs.

This study addresses the observer-based proportional-integral-derivative (PID) security control problem for nonlinear discrete-time fuzzy systems subject to deception attacks. In order to reduce the burden of communication, a distributed dynamic event-triggered mechanism (DETM) is proposed, and each sensor node is equipped with an event-trigger generator. A new discrete-time Takagi–Sugeno (T-S) fuzzy model is established, in which both DETM and deception attacks are taken into consideration. Then, based on Lyapunov stability theory, the sufficient conditions for the input-to-state stability (ISS) are proposed, and the desired fuzzy PID controllers are given. Finally, a simulation example is provided to show the usefulness of the proposed approach.

This study focuses on the secure stability issue of networked control systems (NCSs) under injection cyber-attacks (ICAs), which have important research value of applications for network security. Firstly, a more general Lyapunov–Krasovskii functional (LKF) is constructed based on the time-delay phenomenon in systems. Secondly, a novel boundary looped function (BLF) is developed depending on the sampling period, fully considering delay and sampling information, and the initial constraints of the criteria on the matrices are effectively relaxed. In addition, appropriate integral inequalities are used, making the obtained criteria less conservative in this paper. Then, a new event-trigger mechanism (ETM) controller under ICAs is designed to control the asymptotical stability (AS) of NCSs. Finally, a numerical experiment verifies the correctness and feasibility of the theory.

In this work, novel stability results for load frequency control (LFC) system considering time-varying delays, nonlinearly perturbed load, and time-varying disturbance of system parameters are proposed by using proportional-integral control strategy. Considering the nonlinearly exogenous load disturbance and system parameters disturbance, an improved stability criterion in the form of linear matrix inequalities (LMIs) is derived by novel simple Lyapunov–Krasovskii functionals (LKFs). The delay-dependent matrix in quadratic term, cross terms of variables, and quadratic terms multiplied by 1st, 2nd, and 3rd degrees of scalar functions are included in the new simple LKF. Taking the single-area and two-area LFC system installed with proportional-integral (PI) controller as example, our results surpass the previous maximum allowable size of time delay. Meanwhile, the relationship between time delay varying rate, load disturbance degree, gains of PI controller, and delay margin of the LFC system is researched separately. The results can provide guidance to tune the PI controller for achieving maximum delay margin, in which the LFC system can withstand without losing stability. At last, the simulation results verify the effectiveness and superiority of the proposed stability criterion.

In this paper, the synchronization problem of uncertain neutral-type neural networks (NTNNs) with sampled-data control is investigated. First, a mixed-delay-dependent augmented Lyapunov–Krasovskii functional (LKF) is proposed, which not only considers the interaction between transmission delay and communication delay, but also takes the interconnected relationship between neutral delay and transmission delay into consideration. Then, a two-sided looped functional is also involved in the LKF, which effectively utilizes the information on the intervals [tk,t], [tk−τ,t−τ],[t,tk+1),[t−τ,tk+1−τ). Furthermore, based on the suitable LKF and a free-matrix-based integral inequality, two synchronization criteria via a sampled-data controller considering communication delay are derived in forms of linear matrix inequalities (LMIs). Finally, three numerical examples are carried out to confirm the validity of the proposed criteria.