Genome-scale metabolic models (GEMs) can effectively facilitate many fields in synthetic biology, biomanufacturing, and biomedicine. Reconstructing high-quality GEMs is crucial for accurate phenotype predictions of organisms. However, draft GEMs generated by automated reconstruction tools contain many knowledge gaps, especially missing reactions. The existing machine learning-based gap-filling approaches need to be further developed. In this article, we propose a novel HyperGraph Learning approach with Multi-dimensional metabolite feature extractions and static-dynamic Attention mechanisms (HGLMA) for predicting and teasing out missing reactions in GEM gap-fillings. HGLMA simultaneously uses two pretrained language models to proceed multi-dimensional metabolite feature extractions, which are further fused and regarded as node embeddings for graph learning. The directed and high-order associations between metabolites in reactions of GEMs are deeply mined by successively employing a directional graph network and a hypergraph neural network. Before outputting the predicted confidence score for candidate reactions, the static-dynamic multi-head attention mechanism is utilized to automatically learn attention weights and to identify key metabolites within any candidate reaction. The five-fold cross-validation results on 108 BiGG GEMs show that HGLMA significantly outperforms other state-of-the-art machine learning-based approaches both in prediction performances and in the ability of discovering missing reactions from metabolic reaction pools. The ablation study shows the contributions of multi-dimensional feature extractions and static-dynamic attention mechanisms. In addition, the phenotype prediction results of 24 bacterial organisms demonstrate the effectiveness and superiority of gap-fillings by HGLMA.

Untethered magnetically actuated soft microrobots are promising for biomedical and industrial tasks, yet their practical deployment remains limited by poor adaptability to heterogeneous environments and insufficient motion controllability. This paper presents a soft magnetic microrobot, termed the Bio-mimetic Mudskipper Bot (BM-bot), inspired by the morphology and crawling mechanism of amphibious mudskippers. The BM-bot consists of a compliant body and a pair of soft-magnetic half-wheel feet, enabling crawling locomotion driven solely by magnetic torque–induced foot rotation without onboard actuation. A vision-based pose estimation framework integrating temporal differencing and adaptive histogram equalization is developed for real-time localization, achieving a mean position error of approximately 0.3 mm across multiple experimental conditions. For motion control, a Stepwise Dynamic Compensation (SDC) strategy is introduced, which decouples local motion execution from global path planning and performs discrete heading corrections based on real-time visual feedback. This control scheme allows the BM-bot to maintain a median trajectory deviation of 0.33 mm during long-duration path-following tasks in dynamic environ-ments. Experimental results demonstrate that the proposed robot and control framework enable stable and repeatable locomotion across diverse terrains, including granular media, soil, shallow water, constrained maze environments, and ex vivo soft and dynamic biological tissues. The presented system provides a practical solution for achieving robust, high-precision locomotion in magnetically actuated soft microrobots under unstructured, compliant, and dynamically changing conditions.

Magnetic microrobotic swarms are increasingly studied for their potential in precise motion control under complex and uncertain environments. One of the core challenges in swarm-level control lies in achieving accurate trajectory tracking in the presence of time-varying dynamic parameters, such as fluid resistance. To address such problem, we incorporate error integration into the swarms control framework and derive a kinematic model through model transformation. Based on this model, a Lyapunov-based adaptive robust control strategy is developed to ensure closed-loop stability and compensate for environmental uncertainties. The proposed controller dynamically adapts to unknown environmental variations, and rigorous theoretical analysis establishes the asymptotic stability of the system. A series of experiments are conducted on an electromagnetic actuation platform using silicone oil environments with different viscosities (5 cSt, 10 cSt, and 15 cSt) as well as under a rapidly varying viscosity generated by syringe pump to validate the method. Experimental results confirm that the proposed strategy enables the microrobotic swarm to achieve stable and precise trajectory tracking under varying resistance conditions, demonstrating its robustness and adaptability.

This paper addresses the state estimation problem for new operation modes when there is insufficient measurement data available to learn model parameters. The proposed method, called the transfer state estimator, is formulated using variable-structure multiple model estimation, which enables one to improve estimation performance by transferring model knowledge from different source modes to the target mode. Specifically, first, to track system parameter changes, this work utilizes residuals, which represent the deviations between the actual state and the predicted state. These residuals play a crucial role in determining which knowledge needs to be updated. The transfer state estimator is then derived by integrating knowledge from source models. Through this fusion process, the estimator leverages the existing knowledge to handle the new mode in the target domain. Finally, we provide numerical examples and practical simulations to show the efficacy of the proposed method. The results illustrate that the proposed state estimator is a competitive alternative to various existing state estimation methods when dealing with state estimation in the presence of a new mode.

Three-axis orthogonal six-coil electromagnetic actuation (EMA) systems are widely used for magnetic microrobot control due to their structural simplicity and large workspace. However, such systems suffer from rank deficiency in their actuation matrix, which often leads to singularities and inaccurate force generation when using conventional current allocation methods based on the Moore-Penrose pseudoinverse. This approach tends to unevenly distribute currents, resulting in premature saturation of certain coils and limiting the overall magnetic force output. To overcome these limitations, this paper introduces a novel actuation strategy that enhances both trajectory tracking accuracy and electromagnetic performance. The proposed method consists of two key components: first, an angle selection scheme based on the desired magnetic force, which ensures solvability and introduces redundancy in the current solution; second, an infinity-norm current optimization strategy that minimizes the maximum coil current, thereby increasing the achievable magnetic force under current constraints. Simulation analysis demonstrates that the proposed method increases the maximum magnetic force by 41.5% compared to the traditional approach. Experimental validations were conducted on planar trajectory tracking, including figure-eight and spiral paths with increasing velocity. These validations confirm significant improvements in both actuation accuracy and force capability.

This paper investigates the robust control problem of Markov jump linear systems (MJLSs) with unknown transition probabilities (TPs). While existing temporal difference learning (TDL) methods eliminate the requirement for the precise value of TPs, they often overlook the rapidity of the method convergence. Therefore, we propose an online transfer-enabled temporal difference learning (TTDL) method that explores prior knowledge from similar yet different systems to accelerate convergence and improve the estimation accuracy of decision matrices. Specifically, a transfer estimator is constructed by combining control parameters from the source domain with mode trajectories from the target domain to approximate the target decision matrices. At the beginning of each learning episode, this estimator is incorporated into the value function through an adaptive transfer mechanism. The mechanism uses source knowledge only when reliable and suppresses it near convergence, effectively avoiding negative transfer and yielding rapid policy updates. The theoretical analysis provides a rigorous proof of the convergence for the value function in the online TTDL method. Comparative experiments validate its effectiveness and highlight its reliability under data-scarce scenarios. An application on an aero-engine system further validates the practical applicability and efficiency of the proposed method.

This study proposes a novel synchronization framework for memristive chaotic systems (MCSs) through an enhanced deep reinforcement learning (DRL) approach, featuring an improved proximal policy optimization (PPO) algorithm. Distinguished from traditional linear/nonlinear control paradigms that necessitate precise mathematical modeling, our DRL-based methodology operates without prior knowledge of system dynamics or analytical model requirements. The developed data-driven control strategy demonstrates significant advantages by reducing the required control forces from four to three dimensions, thereby substantially decreasing control complexity and operational costs compared to conventional item-by-item control methods. Through systematic optimization of the reward function architecture in classical PPO algorithms, we achieve accelerated synchronization convergence rates for MCSs, in which an optimal exponential parameter is obtained accordingly. Finally, the practical efficacy of our DRL-driven synchronization framework is successfully validated in image encryption applications. Comprehensive numerical simulations and comparative analyses demonstrate that the proposed methodology not only maintains robust performance under Gaussian noise perturbations but also achieves synchronization efficiency improvements.

Since the general representations produced by pre-trained feature extractors are often insensitive to intra-class variations, existing anomaly detection methods that store them directly in memory banks are constrained in performance. In addition, the distribution discrepancy among different modalities may cause cross-modal interference, further weakening the ability to discriminate anomalies. To address these issues, we propose an Adaptive Prototype Guidance Network (APG-Net) for multi-sensor anomaly detection. First, to avoid cross-modal feature interference, we construct independent anomaly detection branches for multi-sensor data including RGB images, point clouds and infrared images. Then, we introduce a non-parametric feature space reshaping paradigm for each modality. This paradigm adds no additional trainable parameters, ensuring efficiency and ease of deployment. It first learns guiding prototypes from normal samples and matches them to the general representations produced by pretrained extractors. Subsequently, a two-stage adaptive prototype guidance strategy is applied to reshape the feature distributions. This strategy enlarges the separation between normal and anomalous features in the feature space. Finally, we perform decision-level fusion to integrate the anomaly detection strengths from all sensor data. Extensive experiments demonstrate that our method achieves an object-AUROC of 97.4% on the MulSen-AD multi-sensor anomaly detection benchmark, surpassing previous state-of-the-art approaches.

This article is concerned with the asynchronous $H_{\infty }$ control design based on model-free policy iteration (PI) algorithm for a class of discrete-time hidden Markov jump system, where a hidden Markov model is developed to characterize the asynchronous phenomenon between the controller modes and the system modes. A pair of zero-sum asynchronous control and disturbance strategies are constructed to achieve a tradeoff between value function and control performance. The presented approach shows two pivotal aspects: 1) the asynchronous PI algorithm is not dependent on strict temporal alignment between the controller and the system's dynamics, enhancing flexibility of the control scheme and 2) it relies on the collected data to solve the algebraic Reccati equation iteratively, which avoids the need for system-internal and transfer probability information, and circumvents the interference of coupled terms. Subsequently, it is verified that the designed PI algorithm monotonically converges to an optimal solution and the system based on this optimal solution is stochastically stable in the mean-square sense. Finally, the effectiveness of this approach is validated by conducting a simulation experiment on a DC motor device system.

A chain-patterned approach is proposed to achieve the novel leader-following consensus (LFC) with prescribed time-bound and order for multiagent system under directed chain interaction with increasing scales. Using chain-patterned polynomial encodings, this approach confines all effects of scale variation, thereby accommodating increasing scales without requiring prior knowledge of every interaction at all open moments, like the existing studies. Moreover, time-bound-based generator are embedded in this approach to guarantee the novel LFC with prescribed time and bound, while avoiding the infinity-approaching time-varying parameters. Furthermore, the important property order is further enforced and obtained under the proposed approach, conforming to the unidirectional information flow characteristic of chains. Finally, the validity and superiority of the proposed chain-patterned approach are demonstrated by comparative examples.

Anomaly detection is a key technology in quality control for automated production lines. Currently, 2D-based anomaly detection methods fail to identify geometric structure anomalies in products. To address this limitation, this paper proposes a multimodal anomaly detection model using 3D point clouds and RGB images. To ensure the single-domain inference capability of each modality, we design an attention-enhanced dual memory bank to separately store local point cloud features and RGB features. The attention mechanism enhances the informativeness and discriminability of the feature descriptors, significantly improving the data quality in the memory bank. During the inference phase, the local point cloud features in the dual memory bank guide the RGB features in calculating anomaly scores in the 2D modality. This memory-guided approach strengthens the correlation between information across different modalities. Moreover, to improve the overall segmentation precision of the model, we propose an anomaly scoring scheme based on a weight map of signed distance values. The final anomaly detection results are obtained by integrating the advantages of point cloud data in geometric structure anomaly detection and RGB data in color anomaly detection. Extensive experiments demonstrate that the proposed method achieves superior segmentation precision compared to other advanced methods on the MVTec 3D-AD and Eyecandies datasets.

BACKGROUND: A gene regulatory network (GRN) is a graph-level representation that describes the regulatory relationships between transcription factors and target genes in cells. The reconstruction of GRNs can help investigate cellular dynamics, drug design, and metabolic systems, and the rapid development of single-cell RNA sequencing (scRNA-seq) technology provides important opportunities while posing significant challenges for reconstructing GRNs. A number of methods for inferring GRNs have been proposed in recent years based on traditional machine learning and deep learning algorithms. However, inferring the GRN from scRNA-seq data remains challenging owing to cellular heterogeneity, measurement noise, and data dropout. RESULTS: In this study, we propose a deep learning model called graph representational learning GRN (GRLGRN) to infer the latent regulatory dependencies between genes based on a prior GRN and data on the profiles of single-cell gene expressions. GRLGRN uses a graph transformer network to extract implicit links from the prior GRN, and encodes the features of genes by using both an adjacency matrix of implicit links and a matrix of the profile of gene expression. Moreover, it uses attention mechanisms to improve feature extraction, and feeds the refined gene embeddings into an output module to infer gene regulatory relationships. To evaluate the performance of GRLGRN, we compared it with prevalent models and performed ablation experiments on seven cell-line datasets with three ground-truth networks. The results showed that GRLGRN achieved the best predictions in AUROC and AUPRC on 78.6% and 80.9% of the datasets, and achieved an average improvement of 7.3% in AUROC and 30.7% in AUPRC. The interpretation discussion and the network visualization were conducted. CONCLUSIONS: The experimental results and case studies illustrate the considerable performance of GRLGRN in predicting gene interactions and provide interpretability for the prediction tasks, such as identifying hub genes in the network and uncovering implicit links.

Protein-ligand binding affinity measures the strength of interactions between proteins and ligands. Accurately predicting this value is crucial for drug discovery and estimating enzyme kinetic parameters. In recent years, various computational models based on deep learning algorithms have been developed for predicting protein-ligand binding affinity. Most of these require data on protein structure or pockets in addition to protein sequences and ligand SMILES strings. Although integrating structural or pocket information can enhance prediction performances, sequence-based affinity prediction methods using only protein sequences and ligand SMILES strings are more convenient and efficient in practice. We have developed a novel sequence-based deep learning model, called PLMAM-PLA, to predict protein-ligand binding affinity. This model simultaneously extracts global and local features from both protein sequences and ligand SMILES by leveraging pretrained language models (ESM-2 and MolFormer) and dilated convolutional neural networks. The features are enhanced by SKNets and SENets and are further fused by successively using cross-attention and self-attention mechanisms. The output module provides the final affinity prediction value. Ablation studies emphasize the important contributions of the different modules, while visualization experiments demonstrate the efficacy of PLMAM-PLA in capturing meaningful feature representations. Additionally, case studies highlight the powerful generalization capabilities of the model, while comparisons with state-of-the-art models confirm its superior performance in predicting protein-ligand binding affinities.

This article investigates the global predefined-time consensus (PTC) of multiagent system (MAS) via constructing a duplex communication network. Unlike the traditional finite-/fixed-time convergence, our method allows the upper-bound of settling-time to be an explicit constant, which is tunable and can be set beforehand without relating with the network information, controlling parameters, and initial conditions. In particular, our approach uses a smooth, nonchattering consensus scheme that avoids conventional discontinuous functions like signum and absolute value functions. By the Lyapunov stability analysis, the sufficient criterion is deduced for ensuring the PTC of MAS. Finally, simulations confirm the effectiveness of our proposed nonchattering scheme.

Integrating the advantage of the unbiased finite impulse response (UFIR) filter into the Kalman filter (KF) is a practical yet challenging issue, where how to effectively borrow knowledge across domains is a core issue. Existing methods often fall short in addressing performance degradation arising from noise uncertainties. In this article, we delve into a Bayesian transfer filter (BTF) that seamlessly integrates the UFIR filter into the KF through a knowledge-constrained mechanism. Specifically, the pseudo marginal measurement likelihood of the UFIR filter is reused as a constraint to refine the Bayesian posterior distribution in the KF. To optimize this process, we exploit the Kullback-Leibler (KL) divergence to measure and reduce discrepancies between the proposal and target distributions. This approach overcomes the limitations of traditional weight-based fusion methods and eliminates the need for error covariance. Additionally, a necessary condition based on mean square error criteria is established to prevent negative transfer. Using a moving target tracking example and a quadruple water tank experiment, we demonstrate that the proposed BTF offers superior robustness against noise uncertainties compared to existing methods.

This article proposes an asynchronous gain-scheduling secure control framework for nonlinear cyber-physical systems subject to complex and uncertain mode transition behaviors. To address the challenge that ideal probabilistic information is often unavailable in practical industrial environments, a dual-domain polynomial methodology is developed to enhance control performance under realistic conditions. In the structural domain, complex transition probabilities—comprising imprecise and partially unknown components—are reconstructed into a polytopic form, enabling a flexible and accurate abstraction of stochastic system dynamics. In the control design domain, homogeneous polynomial Lyapunov functions and controller structures are utilized to reduce conservatism and improve robustness. The proposed method guarantees exponential mean-square stability and desired performance under cyber attacks. Numerical simulations and hardware-in-the-loop (HIL) experiments on a nonlinear active suspension system confirm the superiority of the approach in terms of feasible design region and performance$\gamma _{\min }$optimization of at least 32.4%.

To identify a reliable model for a dynamic system with nonideal measurements, this article develops a novel parameter transfer identification (PTI) algorithm that leverages the knowledge from a heterogeneous source system. Specifically, a mapping matrix is proposed to transform source parameters into intermediate parameters with dimensions matching the target parameters. By treating the intermediate parameter and mapping matrix as latent variables, variational Bayesian (VB) inference is introduced to efficiently approximate intractable posterior distributions of all unknown parameters, with variances reflecting their uncertainty levels. A probabilistic PTI is then proposed to derive the transfer posterior conditioned on the intermediate parameters, whose analytical form is vital for carrying out VB. Based on this, a heterogeneous PTI is established under the VB framework such that variational posterior distributions for all unknown parameters can be updated iteratively. Finally, an atmospheric fermenter example verifies that the proposed algorithm can bring in model accuracy improvement as high as 60% compared with the nontransfer identification approach, when dealing with nonideal measurements.

Transcription factors (TFs) are proteins essential for regulating genetic transcriptions by binding to transcription factor binding sites (TFBSs) in DNA sequences. Accurate predictions of TFBSs can contribute to the design and construction of metabolic regulatory systems based on TFs. Although various deep-learning algorithms have been developed for predicting TFBSs, the prediction performance needs to be improved. This paper proposes a bidirectional encoder representations from transformers (BERT)-based model, called BERT-TFBS, to predict TFBSs solely based on DNA sequences. The model consists of a pre-trained BERT module (DNABERT-2), a convolutional neural network (CNN) module, a convolutional block attention module (CBAM) and an output module. The BERT-TFBS model utilizes the pre-trained DNABERT-2 module to acquire the complex long-term dependencies in DNA sequences through a transfer learning approach, and applies the CNN module and the CBAM to extract high-order local features. The proposed model is trained and tested based on 165 ENCODE ChIP-seq datasets. We conducted experiments with model variants, cross-cell-line validations and comparisons with other models. The experimental results demonstrate the effectiveness and generalization capability of BERT-TFBS in predicting TFBSs, and they show that the proposed model outperforms other deep-learning models. The source code for BERT-TFBS is available at https://github.com/ZX1998-12/BERT-TFBS.

This paper develops model-free optimal tracking policies for Markov jump systems by solving non-zero-sum games (NZSGs). First, coupled action and mode-dependent value functions (CAMDVFs) are built for solving a two-player NZSG and getting Nash equilibrium solutions. Second, we propose a value iteration (VI) algorithm to parallelly update policies under each mode by collecting data on different operation modes within each iterative window. Moreover, the iterative increasing convergence of the CAMDVFs is proved by introducing auxiliary functions between two adjacent iterations. It is worth pointing out that an influence function is introduced to remove abnormal data to improve the learning capability of the VI algorithm effectively. Finally, the tracking policies' validity, self-adaptability and application potential are verified by a numerical example and a generalized economic model.

This letter aims to facilitate the identification proce-dure for dynamic systems by utilizing knowledge from different but related systems. By introducing the transfer gain matrix and constructing the transfer identification criterion, a novel parameters-transfer identification method is developed for the system with low-quality measurements. Meanwhile, the condi-tion for avoiding negative transfer is exploited to theoretically guarantee the effectiveness of knowledge transfer. Moreover, the size and elements of the transfer gain matrix depend on all measurements, a recursive form of the proposed method is derived to overcome the curse of dimensionality. Finally, a mass-spring-damper example and a continuous fermentation reactor example are simulated to demonstrate the advantages and capabilities of the proposed methods.

A novel completely mode-free integral reinforcement learning (CMFIRL)-based iteration algorithm is proposed in this article to compute the two-player zero-sum games and the Nash equilibrium problems, that is, the optimal control policy pairs, for tidal turbine system based on continuous-time Markov jump linear model with exact transition probability and completely unknown dynamics. First, the tidal turbine system is modeled into Markov jump linear systems, followed by a designed subsystem transformation technique to decouple the jumping modes. Then, a completely mode-free reinforcement learning algorithm is employed to address the game-coupled algebraic Riccati equations without using the information of the system dynamics, in order to reach the Nash equilibrium. The learning algorithm includes one iteration loop by updating the control policy and the disturbance policy simultaneously. Also, the exploration signal is added for motivating the system, and the convergence of the CMFIRL iteration algorithm is rigorously proved. Finally, a simulation example is given to illustrate the effectiveness and applicability of the control design approach.

This article examines the distributed filtering problem for a general class of filtering systems consisting of distributed time-delayed plant and filtering networks with semi-Markov-type topology switching (SMTTS). The SMTTS implies the topology sojourn time can be a hybrid function of different types of probabilistic distributions, typically, binomial distribution used to model unreliable communication links between the filtering nodes and Weibull distribution employed to depict the cumulative abrasion failure. First, by properly constructing a sojourn-time-dependent Lyapunov-Krasovski function (STDLKF), both time-varying topology-dependent filter and topology-dependent filter are designed. Second, a novel nonmonotonic approach with less design conservatism is developed by relaxing the monotonic requirement of STDLKF within each topology sojourn time. Moreover, an algorithm with less computational effort is proposed to generate a semi-Markov chain from a given Markov renewal chain. Simulation examples, including a microgrid islanded system, are presented to testify the generality and elucidate the practical potential of the nonmonotonic approach.

This article proposes a robust Bayesian inference approach for linear state-space models with nonstationary and heavy-tailed noise for robust state estimation. The predicted distribution is modeled as the hierarchical Student- t distribution, while the likelihood function is modified to the Student- t mixture distribution. By learning the corresponding parameters online, informative components of the Student- t mixture distribution are adapted to approximate the statistics of potential uncertainties. Then, the obstacle caused by the coupling of the updated parameters is eliminated by the variational Bayesian (VB) technique and fixed-point iterations. Discussions are provided to show the reasons for the achieved advantages analytically. Using the Newtonian tracking example and a three degree-of-freedom (DOF) hover system, we show that the proposed inference approach exhibits better performance compared with the existing method in the presence of modeling uncertainties and measurement outliers.

In most industrial processes, some measurements are sampled frequently while other measurements are available infrequently and often slow rate. To utilize the slow rate measurements better for improving the accuracy of estimation, this article proposes a powerful unifying estimation framework for Markovian jump linear systems with multirate measurements based on the transfer learning strategy. Specifically, the form of knowledge transferred is designated as the observation predictor derived using the slow rate measurements. We define the universal evaluation of relatedness between the distribution transferred knowledge and ideal posterior distribution from the perspective of Kullback–Leibler (KL) divergence. A smoothing method is then proposed to compute one-step-behind posterior estimates of the state since the estimates obtained using the slow rate measurements are less than the fast ones. Based on this, an iterative transfer state estimator that includes the transferred observation predictor derived using the slow rate measurements is developed, whenever the slow rate measurements are available. Finally, a moving-target example and an experiment with GPS tracking for the ship-board echo sounder show that the proposed approach can be regarded as a competitive alternative of various existing fusion methods when slow rate measurements arrive.

This article addresses the design issue of fuzzy asynchronous fault detection filter (FAFDF) for a class of nonlinear Markov jump systems by an event-triggered (ET) scheme. The ET scheme can be applied to cut down the transmission times from the system to FAFDF. It is assumed that the system modes cannot be obtained synchronously by the filter, and instead, there is a detector that can measure the estimated modes of the system. The asynchronous phenomenon between the system and the filter is characterized via a hidden Markov model with partly accessible mode detection probabilities. Applying the Lyapunov function methods, sufficient conditions for the presence of FAFDF are obtained. Finally, an application of a wheeled mobile manipulator with hybrid joints is employed to clarify that the devised FAFDF can detect the faults without any incorrect alarm.

In this article, the problem of the asynchronous fault detection (FD) observer design is discussed for 2-D Markov jump systems (MJSs) expressed by a Roesser model. In general, the FD observer cannot work synchronously with the system, that is, the mode of the observer varies with the mode of the system in line with some conditional transitional probabilities. For dealing with this difficult point, a hidden Markov model (HMM) is employed. Then, combining the$H_{\infty }$attenuation index and$H_{\_{}}$increscent index, a multiobjective solution to the FD problem is formed. In terms of linear matrix inequality technology, sufficient conditions are gained to guarantee the existence of the asynchronous FD. Simultaneously, an asynchronous FD algorithm is generated to acquire the optimal performance indices. Finally, a numerical example concerned with the Darboux equation is demonstrated to exhibit the soundness of the developed approach.

Based on the interval type-2 fuzzy (IT2F) approach, this article investigates the fault detection filter design problem for a class of nonhomogeneous higher level Markov jump systems with uncertain transition probabilities. Considering that the mode information of the system cannot be obtained synchronously by the filter, the hidden Markov model can be seen as a detector to handle this asynchronous problem, and the parameter uncertainty can be processed by the IT2F approach with the lower and upper membership functions. Then, the asynchronous IT2F filter is designed to deal with the fault detection problem. Furthermore, the Gaussian transition probability density function is introduced to describe the uncertainty transition probabilities of the system and the filter. Based on the Lyapunov theory, the existence of the designed asynchronous IT2F filter and the dissipativity of the filter error system can be well ensured. In this article, the simulation study on a quarter-car suspension system verifies that the designed asynchronous IT2F filter can detect faults without error alarms.

In this article, a new sensor fault estimation algorithm is proposed for industrial processes described by linear discrete-time systems, where the fault dynamics are modeled as a stochastic process. By performing the variational Bayesian inference, the potential sensor fault, as well as the system states, is estimated simultaneously in a probabilistic framework. It is shown that the target fault signal can be satisfactorily estimated through the proposed method, without knowing the statistics of measurement noise and fault coefficient matrix. The efficiency and superiority of the proposed method are demonstrated through numerical simulations and experimental tests performed on a hybrid tank system.

This article investigates the finite-time asynchronous control problem for continuous-time positive hidden Markov jump systems (HMJSs) by using the Takagi-Sugeno fuzzy model method. Different from the existing methods, the Markov jump systems under consideration are considered with the hidden Markov model in the continuous-time case, that is, the Markov model consists of the hidden state and the observed state. We aim to derive a suitable controller that depends on the observation mode which makes the closed-loop fuzzy HMJSs be stochastically finite-time bounded and positive, and fulfill the given L2performance index. Applying the stochastic Lyapunov-Krasovskii functional (SLKF) methods, we establish sufficient conditions to obtain the finite-time state-feedback controller. Finally, a Lotka- Volterra population model is used to show the feasibility and validity of the main results.

In this study, we characterized the variations in ionospheric irregularities and medium-scale traveling ionospheric disturbances (MSTIDs) simultaneously at mid-latitudes over central China based on high-fidelity observations of the total electron content (TEC) from a Beidou geostationary satellite and explored their quantitative relationships during 2016–2017. Ionospheric irregularities generally occurred during 20:00–03:00 local time (LT) in summer and reached a peak rate of ~30% at night; during the daytime in summer, the irregularities had a weak occurrence peak of not more than ~10%. The occurrence rate of nighttime MSTIDs showed a major peak of ~45% in summer and a secondary peak of ~20% in winter, whereas the main peak in winter was ~30% during the daytime. Additionally, the period, velocity, wavelength, and propagation direction of nighttime MSTIDs were different from those of daytime MSTIDs. There was a strong correlation between ionospheric irregularities and MSTIDs at night when the rate of ionospheric irregularities occurring during MSTIDs reached 90%. However, the situation during the daytime was different; the rate of ionospheric irregularities associated with MSTIDs did not exceed ~20%. These results indicate that nighttime MSTIDs could play an important role in producing ionospheric irregularities, but daytime MSTIDs do not. The different diurnal relationships between ionospheric irregularities and MSTIDs can be attributed to the different generation processes of MSTIDs. Our results from Beidou GEO TEC observations provide a new perspective for understanding ionospheric irregularities and MSTIDs and their quantitative relationships.

Despite rapid sensor technology developments, monitoring a biological process using regular sensor measurements is challenging, making the process very difficult to characterize. Designing an optimal estimator is an attractive alternative to soft-sensing for such complicated hybrid systems. In this article, the variational Bayesian learning algorithms are proposed to estimate the continuous fermenters' actual states. Special attention is given to the random transition probability matrix (TPM), which is a prerequisite to improving estimation performance. Under the assumption of a time-invariant but random TPM, the Dirichlet distribution is utilized to specify the property of TPM. We then estimate it together with the system state and modal state to approximate the conditional posterior joint distribution. Testing the proposed algorithms using the fermenter model shows that the variational Bayesian learning algorithm can satisfactorily estimate conditions and track TPM in high accuracy.

This article focuses on the finite-time asynchronous output feedback control scheme for a class of Markov jump systems subject to external disturbances and nonlinearities. The conic-type nonlinearities hold a constraint condition which locates in a known hyper-sphere with an indefinite center. In addition, the asynchronization phenomenon occurs between the system and the controller, which can be represented by means of a hidden Markov model. A sufficient condition is derived not only to guarantee the finite-time boundedness of the acquired closed-loop systems but also to possess a desired$H_{\infty }$performance on the basis of Lyapunov functional technique. Finally, the validity and feasibility of the proposed method are demonstrated with a dc-motor experiment.

This paper is concerned with the sliding mode control (SMC) via finite-time stabilization (FTS) for a class of conic-type nonlinear semi-Markovian jumping systems (SMJSs). Comparing with the classical Markovian jumping systems, the transition rates of SMJSs are related to the random sojourn-time g. Based on this, a suitable SMC law for driving the state trajectories to the designed sliding surface within a finite-time interval is given. Then, the FTS over reaching phase and sliding motion phase is further proved to guarantee the FTS of the whole SMJSs. Finally, the effectiveness of the proposed method is demonstrated by a time-delayed Chua's circuit simulation.

This article study the asynchronous control problem for a class of discrete-time Markovian jumping Lur’e systems (MJLSs) over the finite-time interval. The partial accessibility of system modes with respect to the designed controller is described by a hidden Markov model (HMM). The asynchronous control law consists of two parts, i.e., the states and the nonlinearities involved in the dynamics of the controlled system. By selecting the appropriate Lyapunov functional and applying the modified sector condition, the finite-time stabilization conditions under the control constraints are derived. Finally, the effectiveness of the designed method is verified by an illustrative simulation.

To solve the problem of high‐order moment Gaussian distribution (HGD) noise in state estimation, a fusion filter for Markov jump linear systems (MJLSs) with high‐order moment information obtained from sensor data is designed. To obtain high‐order moment information, the multi‐sensor MJLS is converted to a single‐mode system composed of high‐order moment components by using a cumulant generating function. Next, a filter design based on Bayesian theory is established to achieve state estimation with a high‐order moment information form according to the transformed single‐mode deterministic system. Subsequently, a high‐order moment fusion technique based on entropy theory is proposed to obtain a more accurate estimation result of the state by using the high‐order moment information obtained from various sensors. Comparing the first‐ and second‐order moment information obtained by traditional Gaussian distribution, the HGD introduces higher‐order moment information and makes the fusion process more reasonable. In this way, a more precise and reasonable performance of the state estimation is achieved, depending on the sensor fusion technique. To confirm the effectiveness and advantages of the proposed method, a numerical simulation example is provided with various fusion methods. Thus, the performance of the proposed fusion filter design is verified.