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.

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.

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.

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.

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 study addresses the optimal control problem for continuous-time nonlinear systems with unknown dynamics. A policy iterative-based optimization algorithm is proposed to solve this problem by leveraging a novel neural network representation termed multivariable neural network linear differential inclusion (MVNNLDI). MVNNLDI approximates the initial nonlinear model with a linear differential equation formulation that includes bounded disturbances. Based on this linearized representation, the relevant adaptive optimal control and disturbance compensation approach are derived to tackle the nonlinear optimization problem. Capitalizing on model-free control principles, the optimal solutions can be obtained using only measured state and input data, thus simplifying algorithmic complexity and accelerating convergence speed substantially. Finally, we use two simulation experiments to demonstrate the feasibility and effectiveness of the proposed method.

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.

In this article, a composite control scheme, consisting of the conditional disturbance compensation controller and control allocation, is proposed for a manned submersible vehicle (MSV). First of all, a composite disturbance estimation method combining nonlinear disturbance observer (NDOB) and fuzzy logic system (FLS) is used to estimate the external disturbance and model uncertainty, which can acquire better accuracy than the conventional estimation method with a single NDOB. Second, a new disturbance characterization index (DCI) is proposed for the MSV, which not only indicates whether the disturbance/uncertainty is beneficial to MSV, but also reflects the beneficial degree of the disturbance/uncertainty. A conditional disturbance compensation controller is then developed on the basis of DCI, where the detrimental disturbance/uncertainty is eliminated and the favorable disturbance/uncertainty is reserved to further improve the system performance. Furthermore, a control allocation scheme is proposed to solve the overactuated problem of MSV, which can save energy consumption of the thruster system by making full use of the azimuth thrusters in MSV. Finally, the semiglobal asymptotic stability of the MSV system is rigorously analyzed. The effectiveness of the proposed composite control scheme is also verified by the simulations.

This article considers the finite-horizon H$_{\infty }$tracking problem for a class of discrete linear time-varying systems. Two reinforcement learning (RL) methods—policy iteration (PI) and Q-learning—are proposed to solve this problem. The latter can obtain the H$_{\infty }$controller without system dynamics. In the field of RL control, most studies focus on infinite-horizon control and time-invariant systems, and few studies have investigated finite-horizon control or time-varying systems. In contrast to infinite-horizon H$_{\infty }$tracking control, finite-horizon H$_{\infty }$tracking control involves a time-varying value function. While this introduces challenges, it empowers the algorithm to effectively handle time-varying problems. Within the finite-horizon framework, the value function is bounded, allowing the removal of the discount factor, thereby enhancing control performance. Additionally, there is no longer a need for an admissible control law for initialization, providing the proposed algorithms with the combined advantages of both PI and value iteration (VI). Two simulation examples are used to verify the effectiveness of the proposed algorithms.

To design a finite impulse response (FIR) filter for Markov jump linear systems (MJLSs), a fundamental problem is to avoid constructing the extended state-space model without knowing the mode sequence. This article proposes a new FIR filtering algorithm for MJLSs to address this problem. Under each mode, the variational inference approximates the posterior distribution as a product of Gaussian distribution and inverse gamma distribution by minimizing the Kullback–Leibler divergence. A recursion is then derived over a predefined estimation horizon, where the influence of abandoning the measurements beyond the horizon is compensated. By setting the estimation horizon length as a fixed number, the recursion achieved becomes a new FIR filter for MJLSs, while a new suboptimal Bayesian estimator appears when the horizon length is determined as the full horizon. A Newtonian tracking example as well a three degree-of-freedom hover model is presented to demonstrate that the proposed FIR method has good immunity against unpredicted modeling uncertainties at the cost of extra computational resources and memories, and its full-horizon form does not show this feature and may lose to some exiting algorithms when the underlying model is accurate.

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 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.

The robust online identification problem of linear systems is considered in this article using a faster robust recursive expectation–maximization (RREM) framework. To improve the convergence rate, the outliers, which would deteriorate the identified models, are accommodated with a Laplace distribution instead of Student's$t$-distribution. Then, the recursive transformation of the maximum likelihood function is realized with a recursive$Q$-function. The extensively recognized autoregressive exogenous (ARX) models are used for the description of general linear systems. As a result, the unknown parameters, including the regression coefficient vector of the ARX models, the variance of the noise without outliers, and the scale parameter of the Laplace distribution, are determined in a recursive manner. The performance of the proposed approach is tested with a simulated continuous fermentation reactor system example and a coupled-tank experiment.

Strictly nonwhite measurement noise (NMN) is observed in many industrial processes. Therefore, effective smoothing is often required to extract useful information about the process state with maximum accuracy. This article proposes a batch$q$-lag optimal finite impulse response (OFIR) smoother, operating under NMN with full block covariance matrices. It is shown that the OFIR smoother significantly outperforms the Rauch–Tung–Striebel (RTS) smoother and the unbiased FIR (UFIR) smoother. Testing is provided based on object tracking. The results are validated by a practical example of a three degree-of-freedom helicopter system, which confirms that OFIR smoothing provides better noise reduction than UFIR smoothing, RTS smoothing, and modified RTS smoothing using state augmentation and measurement differencing.

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.

With the application of electrical equipment, magnetically coupled resonant (MCR) wireless power transfer (WPT) technology has become an effective means to improve equipment intelligence. The MCR-WPT system is a loosely coupled system, and the resonant frequency may be split or detuned due to the changes of load or transferring distance, resulting in the system transfer efficiency (TE) greatly reduced. To solve the problems of limited speed and accuracy in the existing frequency tracking methods, this paper analyzes the relation between the detuning rate and the system TE, proposing an adaptive frequency tracking control method based on fuzzy radial basis function neural network control. The neural network outputs proportion–integration–differentiation parameters to adjust the inverter drive circuit, and the frequency of inverter drive circuit is adjusted nonlinearly in real time to ensure the accurate frequency tracking of the MCR-WPT system. The simulation and experimental results show that the proposed method can enhance the tracking ability of the resonant frequency, and effectively improve the system TE.

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.

This article explores a novel adaptive optimal control strategy for a class of sophisticated discrete-time nonlinear Markov jump systems (DTNMJSs) via Takagi–Sugeno fuzzy models and reinforcement learning (RL) techniques. First, the original nonlinear system model is represented by fuzzy approximation, while the relevant optimal control problem is equivalent to designing fuzzy controllers for linear fuzzy systems with Markov jumping parameters. Subsequently, we derive the fuzzy coupled algebraic Riccati equations for the fuzzy-based discrete-time linear Markov jump systems by using Hamiltonian–Bellman methods. Following this, an online fuzzy optimization algorithm for DTNMJSs as well as the associated equivalence proof is given. Then, a fully model-free off-policy fuzzy RL algorithm is derived with proved convergence for the DTNMJSs without using the information of system dynamics and transition probability. Finally, two simulation examples, respectively, related to the single-link robotic arm and the half-car active suspension are given to verify the effectiveness and good performance of the proposed 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.

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.

In this article, the multipass optimal finite impulse response (OFIR) filtering approach is developed for industrial processes with unknown initial conditions under temporary model mismatches. The forward and backward OFIR filters are derived in batch and fast iterative forms using recursions. The double-pass OFIR (DOFIR) filter supported by the unbiased FIR (UFIR) filter and triple-pass OFIR (TOFIR) filter starting with some initial values are designed and extensively investigated using simulations and experimental data. It is shown that the DOFIR and TOFIR filters are able to essentially improve the performance close to the initial values and are more robust against temporary model mismatches than the Kalman, OFIR, and UFIR filters.

In this article, a novel composite learning control scheme based on nonlinear disturbance observer (NDOB), neural network (NN), and model-based state observer (MSOB) is investigated for the manned submersible vehicle. First, an MSOB is employed to reconstruct the real output signals from noise-contained measurements. Second, a composite estimation is developed where an NDOB is designed to estimate external disturbance and an NN is employed for model uncertainty. Furthermore, a control allocation technique is used to address the overactuated problem of the manned submersible vehicle. The rigorous stability analysis of the closed-loop manned submersible system is given via the Lyapunov theorem. Finally, several representative simulation results illustrate the superior control performance of the composite learning control scheme for the manned submersible vehicle.

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 studies the H∞sliding mode control (SMC) problem for a class of discrete-time conictype nonlinear systems with time-delays and uncertainties. The nonlinear terms satisfy the conic-type constraint condition that lies in a know hyper-sphere with an uncertain center. By choosing a proper Lyapunov candidate, sufficient conditions are derived to ensure the asymptotic stability of the sliding mode dynamics while achieving a prescribed H∞disturbance attenuation level and finally converted into a minimization problem. The controller is constructed to guarantee the discrete-time reach condition and maintain the states on the prespecified sliding surface. A simulation result and a practical example related to the Chua's circuit are given at last to show the validity of our SMC strategy.

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.

This paper studies the online adaptive optimal controller design for a class of nonlinear systems through a novel policy iteration (PI) algorithm. By using the technique of neural network linear differential inclusion (LDI) to linearize the nonlinear terms in each iteration, the optimal law for controller design can be solved through the relevant algebraic Riccati equation (ARE) without using the system internal parameters. Based on PI approach, the adaptive optimal control algorithm is developed with the online linearization and the two-step iteration, i.e., policy evaluation and policy improvement. The convergence of the proposed PI algorithm is also proved. Finally, two numerical examples are given to illustrate the effectiveness and applicability of the proposed method.

This paper investigates the asynchronous resilient controller design problem for a class of nonlinear switched systems with time-delays and uncertainties in a given finite-time interval. By constructing proper multiple Lyapunov-Krasovskii functions and applying average dwell time methods, a switching law and the relevant asynchronous resilient controller are designed to guarantee the finite-time boundedness of the closedloop system with a specified H∞performance index. The H∞resilient controller design problems can be derived by solving a set of linear matrix inequalities. A practical example is employed to demonstrate the availability of the proposed methods.

The demand of indoor localization has recently grown quickly in industries. In general, a localization system is required to be reliable, fast, and have high accuracy. In this paper, the ultrawideband (UWB) technique is combined with the inertial navigation sensor (INS) to form a coupled UWB/INS localization framework, which inherits the advantages from both components. A minimum variance unbiased finite impulse response (MVU FIR) method is then applied to obtain accurate position and velocity estimations from noisy measurements. Two experiments and several simulations are conducted. Compared with the traditional Kalman filter (KF) and particle filter, the MVU FIR filter exhibits better immunity to the errors about a priori knowledge of noise variances. It can handle the kidnapped problem, and recover from some extreme failures satisfactorily. Moreover, the MVU FIR filtering algorithm is fast and easily implementable. Its online computational time is even lower than that of the KF, which is favorable in localization applications.

The finite-time sliding mode controller design problem of a class of conic-type nonlinear systems with time-delays and mismatched external disturbance is studied. The time-delay conic nonlinearities are considered to lie in a known hypersphere with an uncertain center. A scalar selection criterion dependent sliding mode control (SMC) law is constructed to drive the state trajectories onto the specified sliding surface during any assigned short time interval. By using slack matrix approach, a delay-dependent sufficient condition is derived to ensure the finite-time boundedness of the closed-loop systems over the finite-time interval. Then, the algorithm for designing the finite-time SMC law is established. Finally, two examples related to the time-delayed Chua's circuit is given to demonstrate the effectiveness of the developed methods.

It is proved that the iterative computation form for the mean square error (MSE) matrix of the batch unbiased finite impulse response (UFIR) filter exactly equals that of the iterative UFIR filter form, unlike what was previously thought. Based on the iterative MSE matrix form, we suggest two strategies for defining the optimal horizon length for the UFIR filter. The results are verified using the two-state polynomial and harmonic models.

It is crucial to determine the optimal number of clusters for the clustering quality in cluster analysis. From the standpoint of sample geometry, two concepts, i.e., the sample clustering dispersion degree and the sample clustering synthesis degree, are defined, and a new clustering validity index is designed. Moreover, a method for determining the optimal number of clusters based on an agglomerative hierarchical clustering (AHC) algorithm is proposed. The new index and the method can evaluate the clustering results produced by the AHC and determine the optimal number of clusters for multiple types of datasets, such as linear, manifold, annular, and convex structures. Theoretical research and experimental results indicate the validity and good performance of the proposed index and the method.

In this survey, the authors examine the trade‐off between the unbiased, optimal, and in‐between solutions in finite impulse response (FIR) filtering. Specifically, they refer to linear discrete real‐time invariant state‐space models with zero mean noise sources having arbitrary covariances (not obligatorily delta shaped) and distributions (not obligatorily Gaussian). They systematically analyse the following batch filtering algorithms: unbiased FIR (UFIR) subject to the unbiasedness condition, optimal FIR (OFIR) which minimises the mean square error (MSE), OFIR with embedded unbiasedness (EU) which minimises the MSE subject to the unbiasedness constraint, and optimal UFIR (OUFIR) which minimises the MSE in the UFIR estimate. Based on extensive investigations of the polynomial and harmonic models, the authors show that the OFIR‐EU and OUFIR filters have higher immunity against errors in the noise statistics and better robustness against temporary model uncertainties than the OFIR and Kalman filters.

We propose a fast iterative algorithm for optimal finite impulse response (OFIR) filtering of linear discrete time-invariant state-space models in white Gaussian noise. The OFIR filter is known to have the BIBO stability and better robustness against the Kalman filter (KF). The iterative OFIR algorithm is KF-like; that is, its estimate appears much faster than in the batch OFIR filter. A dramatic reduction of computation time is demonstrated in the full-horizon iterative OFIR algorithm which operates as fast as KF. We also notice a considerable reduction of the computational resources allowed by iterations.

This paper addresses the problem of robust fuzzy L2-L∞ filtering for a class of uncertain nonlinear discrete-time Markov jump systems (MJSs) with nonhomogeneous jump processes. The Takagi-Sugeno fuzzy model is employed to represent such nonlinear nonhomogeneous MJS with norm-bounded parameter uncertainties. In order to decrease conservation, a polytope Lyapunov function which evolves as a convex function is employed, and then, under the designed mode-dependent and variation-dependent fuzzy filter which includes the membership functions, a sufficient condition is presented to ensure that the filtering error dynamic system is stochastically stable and that it has a prescribed L2-L∞ performance index. Two simulated examples are given to demonstrate the effectiveness and advantages of the proposed techniques.

This study considers the minimum mean square error estimation problem for a class of jump Markov linear systems with unknown mode‐dependent state delays. In order to show the difficulties caused by the unknown delays, the online Bayesian equation of the investigated system is firstly developed by incorporating the time‐delay estimation into the recursion of system states. However, computing such optimal estimation causes an exponential increase in the requirement of computation and storage load. Therefore two different approximation techniques: interacting multiple‐model approximation and detection–estimation method are utilised to obtain two suboptimal but executable filtering algorithms, respectively. Simulation results of the proposed methods for a system are presented to illustrate the effectiveness.

This paper studies the finite-timeH∞control problem for time-delay nonlinear jump systems via dynamic observer-based state feedback by the fuzzy Lyapunov-Krasovskii functional approach. The Takagi-Sugeno (T-S) fuzzy model is first employed to represent the presented nonlinear Markov jump systems (MJSs) with time delays. Based on the selected Lyapunov-Krasovskii functional, the observer-based state feedback controller is constructed to derive a sufficient condition such that the closed-loop fuzzy MJSs is finite-time bounded and satisfies a prescribed level ofH∞disturbance attenuation in a finite time interval. Then, in terms of linear matrix inequality (LMIs) techniques, the sufficient condition on the existence of the finite-timeH∞fuzzy observer-based controller is presented and proved. The controller and observer can be obtained directly by using the existing LMIs optimization techniques. Finally, a numerical example is given to illustrate the effectiveness of the proposed design approach.