This paper investigates the inverse reinforcement learning (IRL) problem for fuzzy switched systems characterized by sojourn probabilities. Classical fuzzy Markov jump system approaches rely on transition probabilities, which are often difficult to estimate, whereas sojourn probabilities provide a more realistic and tractable description of switching behavior. To address the challenges of nonlinear dynamics, uncertain model residence, and the lack of known cost functions, a novel IRL framework is proposed. Different from existing methods, the framework (i) establishes IRL under sojourn-probability-based fuzzy switched systems, (ii) incorporates a dynamic learning rate mechanism that adapts to switching conditions, mitigating oscillations and accelerating convergence, and (iii) introduces constraint-based parameter updates together with a new penalty-matrix update strategy to ensure convergence and reduce computational complexity. Rigorous theoretical analysis demonstrates the stability and convergence of the proposed algorithms. Simulation study on a benchmark tunnel diode circuit confirm that the learner system can accurately reconstruct equivalent cost functions, reproduce expert trajectories, and achieve superior performance compared with conventional IRL methods.

The cooperative pursuit games of multiple unmanned surface vessels (USVs) that coordinate the actions of USVs to capture specific targets are of great significance for ensuring maritime security. The existing pursuit algorithms primarily encounter challenges including unclear target capture criteria, limited real-time performance and efficiency, and inadequate environmental adaptability. Combining multi-agent deep reinforcement learning (RL) with threat potential fields (TPFs), a game-based cooperative hunting algorithm is developed for underactuated USVs. The new features of the proposed hunting strategy are threefold: 1) The criteria for successful target capture, including the explicit mathematical expression, are established using a shunting environment and a boundary constraint-free kinematic model. 2) By integrating a multi-head attention (MHA) mechanism into the RL strategy, a new multi-agent proximal policy optimization (MAPPO) framework is developed, which alleviates the sparse reward problem and significantly improves convergence efficiency in maritime scenarios with multiple evaders. 3) To be more suitable for complex marine environments, a new TPFs-based reward mechanism is constructed, which not only helps USVs avoid environmental obstacles but also facilitates efficient cooperative hunting. At last, numerical simulations and experimental results are presented to demonstrate the effectiveness of the developed hunting algorithm.

Multi-object tracking (MOT) has garnered considerable attention due to its relevance in practical applications such as automated devices in smart cities. However, under complex conditions, existing trackers often fail to accurately capture or characterize target motion patterns, exhibiting limitations in flexibility and interpretability. To address these challenges, this paper introduces CHTracker, a confidence-guided hierarchical association paradigm for MOT. By integrating spatial features with varying confidence levels, CHTracker enhances the granularity of motion pattern modeling in edge-case scenarios where conventional trackers are prone to association ambiguity. Our paradigm adaptively utilizes distinct tracking cues and assignment metrics tailored to hierarchical target structures, thereby enabling collaborative tracking. Additionally, CHTracker incorporates the diagonal length of the target bounding box as a state variable during position prediction, which significantly improves the robustness against diverse motion noise. Extensive experimental results on multiple benchmarks, including Dance-Track, MOT17, MOT20, and Singapore Maritime Dataset (SMD), demonstrate that CHTracker achieves the state-of-the-art performance in accuracy, robustness, and generalization. Furthermore, our association paradigm is extended to a visible-infrared fusion version for evaluation on the multimodal CAMEL dataset, underscoring its practical potential to fulfill heterogeneous modality requirements in real-world scenarios. Our code will be available at https://github.com/ZyanChenyang/CHTracker.

This study introduces a novel integrated control and communication framework specifically designed for two-dimensional systems. First, a super-twisting-based second-order sliding mode control strategy is proposed to against uncertainties and effectively eliminate chattering. Second, a self-triggered communication protocol is developed, markedly reducing communication overhead by proactively scheduling transmissions without continuous error monitoring. Third, a dynamic binary encoding strategy is introduced, dynamically adjusting quantization intervals according to real-time historical data, thus improving signal accuracy and reducing quantization errors. Explicit sufficient conditions are derived, guaranteeing both practical reachability of the sliding manifold and the ultimate boundedness of the closed-loop system. Additionally, an optimization framework employing the Grey Wolf Optimizer algorithm is formulated to optimize sliding mode parameters, further reducing the convergence bounds. Comprehensive simulation results illustrate the superior performance and effectiveness of the proposed framework compared to existing methods.

Tracking-by-Detection paradigms shine in generic multi-object tracking (MOT), while their compact construction hinders the real-time applications. In this work, we attribute the substantial computational burden to two expensive components, i.e. detection and re-identification. Building upon the principle of adaptively maintaining acceptable inference efficiency, we present Adaptively Sparse Detection with attention-guided refinement (ASDTracker) for efficient tracking. In specific, our ASDTracker rapidly assess the short-term and long-term occlusion, dynamically determining the usage of the expensive detector. For non-key frames, we efficiently refine small-size crops out of Kalman Filter predictions and introduce the noisy shadow labels to robustly train this refinement network. Additionally, we substitute the lightweight appearance representation for the heavy ReID network, which efficiently extracts sufficient appearance cues in the coarsely quantized color spaces. Extensive experiments on four benchmarks demonstrate that ASDTracker achieves competitive performance in generalization and robustness under favorable inference speed. Moreover, the efficient tracking deployment is further implemented to an unmanned surface vehicle with high accuracy and low latency in real-world scenarios.

Convolutional Neural Network (CNN) and Vision Transformer (ViT) have revolutionized the field of image super-resolution (SR). However, their complexity poses challenges for resource—constrained scenarios, particularly due to the high computational demands of Transformers and their excessive reliance on global information. To tackle these challenges, we propose a Multi-Channel Feature Integration Network (MCFINet), designed to maximize input pixel utilization while minimizing computational overhead. It integrates both local and global features within the channels, thereby exploiting their complementary advantages. First, the designed Feature Integration Block (FIB) effectively captures local information and improves visual quality by enhancing the mapping of non-local features. Subsequently, we utilize the Adaptive Channel Fusion Block (ACFB), which strengthens the interaction between features and channels while maintaining computational efficiency. Finally, for SR task on resource-constrained surface scenarios, we propose a more suitable pre-training method, which further boosts the model’s learning ability. Evaluation results indicate that the proposed MCFINet achieves a better balance between lightweight design and high-quality restoration on both standard evaluation datasets and water surface target datasets. Specifically, compared to the traditional SwinIR-L, MCFINet reduces model training time and runtime by 12% on the test set, while also decreasing model complexity by 43%. Our codes are available at https://github.com/Lcasjz/MCFINet .

This article investigates the leader-following bipartite time-varying formation (BTVF) control problem for switched multiagent systems (MASs) under the changeable directed signed topologies. The communication topology switches to obey an average dwell-time condition, capturing realistic network dynamics. Two critical challenges are addressed in the controller design, one of which is that the real states are inaccessible and the other is that we know nothing about the active leader input. These constraints significantly increase the design complexity. Against this backdrop, we develop a novel control protocol that is capable of achieving the BTVF tracking even given the uncertain leader input without using the real states. The designed control protocol can perform well to deliver reliable commands to realize the BTVF under switched topologies. Through Lyapunov stability analysis and recursive algorithms, we rigorously prove convergence to the desired BTVF. Notably, the protocol is also shown to guarantee bipartite consensus as a special case. In the end, the effectiveness of the proposed control scheme is validated through simulations involving the clusters of the wheeled mobile robot and autonomous aerial vehicle models in different working scenarios.

Unsupervised semantic segmentation algorithms aim to identify meaningful semantic groups without annotations. Recent approaches leveraging self-supervised transformers as pre-training backbones have successfully obtained high-level dense features that effectively express semantic coherence. However, these methods often overlook local semantic coherence and low-level features such as color and texture. We propose integrating low-level visual cues to complement high-level visual cues derived from self-supervised pre-training branches. Our findings indicate that low-level visual cues provide a more coherent recognition of color-texture aspects, ensuring the continuity of spatial structures within classes. This insight led us to develop IL2Vseg, an unsupervised semantic segmentation method that leverages the complementation of low-level visual cues. The core of IL2Vseg is a spatially-constrained fuzzy clustering algorithm based on color affinities, which preserves the intra-class affinity of spatially-adjacent and similarly-colored pixels in low-level visual cues. Additionally, to effectively couple low-level and high-level visual cues, we introduce a feature similarity loss function to optimize the feature representation of fused visual cues. To further enhance consistent feature learning, we incorporate contrast loss functions based on color invariance and luminosity invariance, which improve the learning of features from different semantic categories. Extensive experiments on multiple datasets, including COCO-Stuff-27, Cityscapes, Potsdam, and MaSTr1325, demonstrate that IL2Vseg achieves state-of-the-art results.

In this paper, we propose a novel covert transmission scheme for reconfigurable holographic surface (RHS)-aided integrated sensing and communication (ISAC) system with imperfect channel state information (CSI). Considering full and partial channel uncertainty models, we jointly devise the digital and holographic beamforming along with the receive filter to maximize the worst-case and outage-constrained achievable rate (AR) of communication users while guaranteeing the sensing capability and covertness requirement. The resulting optimization problems are difficult to solve owing to the non-convexity caused by the semi-infinite constraints (SICs) and the coupled design variables. After approximating the worst-case and outage constraints by exploiting the S-procedure, successive convex approximation (SCA) and Bernstein-type inequality, we propose a secure solution that efficiently optimizes all variables by using convex optimization methods. To understand the proposed algorithm better, both the convergence and computational complexity are discussed. Simulation results show that by incorporating the RHS technique into the optimization design, the covert transmission performance of ISAC systems are improved while ensuring a certain level of sensing performance.

In this paper, we numerically analyze the factors determining localization precision and resolution in single emitter localization-based imaging systems. While previous studies have considered a limited set of parameters, our numerical approach incorporates additional parameters with significant reference values, yielding a more comprehensive analysis of the results. We differentiate between the effects of additive and multiplicative noise on localization precision using numerical modeling and take the influence of the sampling frequency into account, computing the optimal sampling frequency for varying resolution requirements. Leveraging a suite of derived equations, we systematically simulate and quantify how variations in these parameters influence system performance. Furthermore, we provide guidelines for optimizing signal-to-noise ratio (SNR) requirements and pixel size selection based on point spread function (PSF) width in single emitter localization-based imaging systems. This numerically driven research offers critical insights for the analysis of more complex imaging systems.

Path following is a fundamental capability for autonomous surface vessels (ASVs). A typical path-following algorithm comprises two main modules: guidance and control. In the guidance domain, the vector field (VF) approach has been widely adopted due to its demonstrated superiority over alternative guidance strategies. Consequently, it has been successfully applied to a range of unmanned systems, including ASVs, airships, and quadrotors. However, most existing VF guidance laws are constrained to simple geometric paths, such as straight lines and circular orbits. Therefore, their practical applicability is limited in real-world engineering scenarios. To address this limitation, this paper introduces a novel, continuously differentiable VF capable of handling general curved paths, thereby significantly broadening the application scope of the VF methodology. In terms of control, a major challenge for ASVs lies in achieving long-endurance operation while maintaining robustness against uncertainties. The finite-time uncertainty observer (FTUO)-based adaptive dynamic programming (ADP) approach has been shown to be effective in addressing this challenge. Nevertheless, common FTUO-based ADP methods often suffer from drawbacks such as system chattering, observer peaking, and asymptotic convergence. In response to this situation, this paper proposes a modified FTUO-based ADP method for ASVs. This approach eliminates undesirable effects, i.e., chattering and peaking, and further saves control energy. Both simulation and experimental results verify the effectiveness and advantages of the proposed path-following algorithm.

This article presents several adaptive event-triggered control (ETC) strategies for a class of uncertain nonlinear cyber-physical systems (CPSs) under constant or time-varying full-state constraints, as well as deception attacks via the senior network. Initially, the system under investigation is reformulated into a new system that encompasses both the original system state and the compromised state, enabling the use of compromised states for feedback control. Subsequently, two innovative asymptotic integral barrier Lyapunov functions (IBLFs) are developed by directly imposing constraints on the compromised state, thereby eliminating the necessity to convert state constraints into error constraints as required by traditional BLFs-based methods. Furthermore, controllers designed using relative/switched threshold event-triggered strategies ensure that all signals within the entire closed-loop system remain bounded, that the constant or time-varying full-state constraints are not breached, and that asymptotic stability is attained without Zeno behavior. Ultimately, simulation results validate the efficacy of the proposed strategies through a practical example.

In this article, the problem of adaptive dynamic event-triggered nonsingular predefined-time (PT) tracking control for a third-order fully heterogeneous vehicle platoon system is investigated. First, an improved nonsingular PT adaptive tracking controller is constructed by introducing a piecewise continuous function within the control signal in each step of the backstepping procedure, which avoids the singularity problem of the traditional PT control signals in the existing literature and guarantees the existence of the values for all the terms within the control signals in the real number field. Second, a class of universal barrier Lyapunov function (UBLF) is adopted to set restrictions on distance, which ensures that collisions are avoided and communication connectivity is maintained. In addition, based on a dynamic auxiliary variable, the communication resources are saved with the use of the dynamic event-triggered control scheme. Finally, through the PT stability criterion, it is proven that the PT stability of the whole vehicle platoon is ensured, and the effectiveness of the control algorithm is verified by the simulation results.

This paper develops a bilayer predefined time fuzzy reinforcement learning (PT-FRL) control strategy to improve the efficiency of autonomous vehicle formation execution and reduce energy consumption. First, the fixed constraints of communication connectivity and collision avoidance are reconstructed into performance constraints, and normalized error mapping techniques are used to transform them into a new unconstrained error system. Then, based on the system, a cost function was constructed that balances cost control and performance. The control strategy adopts a bilayer architecture: In the first layer, a feedforward controller is designed to provide a more concise control object for subsequent PT-FRL controllers by compensating for known nonlinear coupling terms in advance, and can significantly reduce fuzzy logic systems computational load. and in the second layer, a PT optimal formation controller is designed using FRL to ensure that the autonomous vehicles complete the formation task within the predefined time. Finally, the effectiveness of the proposed method was verified through simulation and experiments.

Aiming at autonomous massive transportation by sea, economically condition-based cooperative control solution remains unrevealed and is highly desirable for collective swarming of distributed unmanned surface vehicles (USVs) suffering from narrow-band communication and unstructured unknowns. In this paper, an event-triggered self-organizing swarm control (ESSC) scheme is innovated to flexibly helm a herd of USVs, and features main contributions as follows: 1) A suite of self-organizing swarm mechanism consisting of aggregation, collision avoidance and heading alignment is holistically established, such that emerging behaviors of swarm kinetics can be self-evolved for flexible morphology; 2) Within adaptive dynamic programming framework, an event-triggered optimal solution to USV swarm control is worked out by deriving optimization-oriented event-triggering mechanism from swarm kinetics tracking errors, thereby making a rational balance between channel occupation and tracking accuracy; and 3) Approximately optimal control actions are acquired by employing actor-critic reinforcement learning networks to solve Hamilton-Jacobi-Bellman equation, thereby assuring communication parsimony and control optimality, simultaneously. Performance validations with intensive comparisons to time-triggered methods demonstrate the effectiveness and superiority in terms of tracking accuracy, channel occupancy and control optimality, in addition that extensive application to roundup scenario showcases the proposed ESSC scheme performs feasible extension to wide-range tasks.

Underwater imagery often suffers from light attenuation and color distortion, resulting in images with low contrast and blurriness. Enhancing these images is crucial yet challenging due to the complex degradation and noise inherent in underwater environments. In this study, we introduce a novel diffusion model, termed Underwater Image Enhancement(UIE) Diffusion, which leverages a global feature prior for effective underwater image enhancement. To our knowledge, this is the inaugural application of a diffusion model to the task of underwater image enhancement, setting a new benchmark in performance. Our approach begins with the introduction of a global feature prior to augment the diffusion model, mitigating the impact of noise and distortion during training. We then incorporate an underwater image degradation model to facilitate the learning of mappings between high-quality and degraded underwater images. To address over-enhancement caused by high-frequency components, we employ scaling factors to modulate the influence of frequency features during diffusion. Additionally, we enhance the model's stability during inference by integrating a backward diffusion process into its training. Comprehensive evaluations on multiple public datasets demonstrate that UIE Diffusion surpasses existing state-of-the-art methods in both subjective outcomes and objective assessments.

This study focuses on TSSDC for Takagi-Sugeno (T-S) fuzzy reaction-diffusion neural networks (NNs) under random sampling and network-induced delays. Fuzzy reaction-diffusion NNs extend traditional NNs by incorporating spatial dynamics through reaction-diffusion processes, offering improved modeling capabilities for complex systems. However, the combination of reaction-diffusion terms and fuzzy modeling increases the complexity of dynamic analysis, particularly in synchronization control. To address these challenges, a novel random TSSDC (RTSSDC) framework is developed. The proposed approach integrates random sampling across both temporal and spatial dimensions with an innovative random event-triggered communication scheme to increase resource utilization efficiency and accommodate real-world network conditions. A unified closed-loop model is established by introducing a packet loss scheduling strategy to handle data disorder caused by significant transmission delays. The framework incorporates switching gains to provide additional flexibility and robustness. Ultimately, numerical simulations are conducted to validate the superior synchronization performance and efficient resource utilization under random sampling and network-induced delays.

In this work, via proposing a novel$\tan $-type asymmetric barrier Lyapunov function (ABLF), we developed a command filtered adaptive fault tolerant control scheme for a class of uncertain nonlinear systems with predefined-time asymmetric and time-varying output constraints. First, such a class of constraints under interest is more practical. Unlike most of the existing constraints that need to be addressed at the moment of inception, the initial value requirement of the constrained object in this work is released. A predefined setting time can be installed in advance through the implementation of a shifting function embedded within the proposed ABLF. Second, the problem of unknown actuator faults is considered where the actuator faults can be both multiplicative and additive. Third, by combining the command filter and backstepping techniques, a twice-transformation design method is presented to construct the desired controller, under which the errors between the virtual control laws and the outputs of the command filters can be compensated thoroughly. Aside from avoiding the “explosion of complexity” issue, the designed controller also achieves the requisite tracking performance. Meanwhile, under such controller, the effect of the actuator faults can be estimated. Finally, the validity of the given scheme is evaluated through the use of a practice example.

In recent years, there has been an exponentially increased amount of point clouds collected with irregular shapes in various areas. Motivated by the importance of solid modeling for point clouds, we develop a novel and efficient smoothing tool based on multivariate splines over the triangulation to extract the underlying signal and build up a 3D solid model from the point cloud. The proposed method can denoise or deblur the point cloud effectively, provide a multi-resolution reconstruction of the actual signal, and handle sparse and irregularly distributed point clouds to recover the underlying trajectory. In addition, our method provides a natural way of numerosity data reduction. We establish the theoretical guarantees of the proposed method, including the convergence rate and asymptotic normality of the estimator, and show that the convergence rate achieves optimal nonparametric convergence. We also introduce a bootstrap method to quantify the uncertainty of the estimators. Through extensive simulation studies and a real data example, we demonstrate the superiority of the proposed method over traditional smoothing methods in terms of estimation accuracy and efficiency of data reduction.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

Due to the complex network environment, the feedback information cannot be timely received by the controller. This article proposes a method on the exponential synchronization for the Markovian jump neural networks, which is achieved by designing a new asynchronous delayed-feedback controller, with its feedback delay taken into account. The quantized relationship between the exponential synchronization and the feedback delay is derived from a new designed Lyapunov functional, to acquire delay boundaries. With the help of a hidden-Markov process, the designed controller shows asynchrony, which allows controller modes to run free. In particular, the detection probability is assumed to be bounded known, marking a breakthrough over existing results. Moreover, the proposed method proves to be applicable in both synchronous and asynchronous cases. By using the proposed method, the computation freedom of the controller gain matrix can be substantially augmented. Further, comparative numerical studies are implemented to validate the effectiveness and superiority of the proposed method.

A new type of containment maneuvering protocols for multiple marine surface vehicles (MSVs) is developed to follow a parameterized path in this work, where the tracking errors are constrained within finite time and the information needed to be transmitted is quantized during coordination. To achieve containment maneuvering of multiple MSVs, a two-objective coordinated control framework is proposed. For the geometric objective, by developing tan-type barrier Lyapunov functions (BLFs) and extended Lyapunov condition-based finite-time guidance laws, the performance of the parameterized line-of-sight guidance framework, including convergence speed and tracking error constraints, is improved. For the dynamic objective, based on quantized control strategy and smooth saturation functions, novel bounded containment maneuvering protocols are proposed to dramatically alleviate the burden of communications among MSVs and ensure more faster dynamic behavior on tracking the path updating speed. Both theoretical analysis and experimental tests with comparative studies illustrate the validity of the proposed containment maneuvering strategy.

This article studies the tracking control problem of uncertain Euler–Lagrange systems. Despite receiving widespread attention in recent years, the problem remains unresolved to a large content when considering response quality, optimality, robustness, and conservatism. The main challenge lies in how to integrate performance constraints into adaptive dynamic programming (ADP) algorithm and achieve a balance between robustness and conservatism within this framework. To that end, this study proposes a new performance constraint-handling sliding mode manifold, a new prescribed performance function, and a new ADP-oriented observer disturbance observer. The above theoretical findings, together with the fuzzy logic system-based ADP algorithm, realize the convergence of tracking errors to a prespecified residual set within a finite-time setting in an optimal manner, enhance robustness, and reduce conservatism. The proposed controller facilitates the practical application of tracking control for Euler–Lagrange systems. Simulations on an autonomous underwater vehicle demonstrate the effectiveness and benefits of the proposed method.

This article presents a prescribed-time tracking control method for underactuated unmanned surface vessels (USVs) using a neural reinforcement learning (RL) approach. First, the hand position approach, addressing the underactuated characteristic, is employed to convert the model of USV into the integral cascade form. Second, inheriting the advantages of prescribed performance control (PPC), the proposed controller not only stabilizes the tracking error within an asymmetric prescribed-time range, but also removes the limitation of initial conditions. Subsequently, the identifier—actor-critic architecture is introduced in the optimized backstepping design, which gives the solution of the Hamilton–Jacobi–Bellman (HJB) equation. Meanwhile, the relative threshold event-triggered mechanism is also considered to reduce the communication burden and executive frequency of actuators. Finally, employing the Lyapunov stability theory, it is proven that all signals in the closed-loop system are bounded, and the developed control scheme is demonstrated to be effective through simulation and experimental results.

In this paper, we investigate a neural adaptive formation control problem for underactuated unmanned surface vehicles (USVs). Considering the limitation of communication distance and the security of formation systems, collision-free and connectivity maintenance are guaranteed by defining a prescribed-time tuning function and proper error transformation. Furthermore, a new nonlinear first-order filter, solving the complexity problem, is designed to promote the system performance. Subsequently, neural networks (NNs) are used to approximate USVs’ dynamics and their transient performance is improved by prediction error. By blending prediction errors and neural approximation, it is guaranteed the general external disturbances and approximation errors are compensated via constructed disturbance observers (DOs), simultaneously. Meanwhile, utilizing the minimal number of learning parameters (MNLPs) methodology, the number of NNs’ learning parameters can be significantly reduced. It is rigorously proved that all signals in the closed-loop system are bounded via Lyapunov stability theorem. Finally, simulation and experimental studies are presented to verify the effectiveness and advantages of theoretical results.

Survival analysis is critical to cancer prognosis estimation. High-throughput technologies facilitate the increase in the dimension of genic features, but the number of clinical samples in cohorts is relatively small due to various reasons, including difficulties in participant recruitment and high data-generation costs. Transcriptome is one of the most abundantly available OMIC (referring to the high-throughput data, including genomic, transcriptomic, proteomic and epigenomic) data types. This study introduced a multitask graph attention network (GAT) framework DQSurv for the survival analysis task. We first used a large dataset of healthy tissue samples to pretrain the GAT-based HealthModel for the quantitative measurement of the gene regulatory relations. The multitask survival analysis framework DQSurv used the idea of transfer learning to initiate the GAT model with the pretrained HealthModel and further fine-tuned this model using two tasks i.e. the main task of survival analysis and the auxiliary task of gene expression prediction. This refined GAT was denoted as DiseaseModel. We fused the original transcriptomic features with the difference vector between the latent features encoded by the HealthModel and DiseaseModel for the final task of survival analysis. The proposed DQSurv model stably outperformed the existing models for the survival analysis of 10 benchmark cancer types and an independent dataset. The ablation study also supported the necessity of the main modules. We released the codes and the pretrained HealthModel to facilitate the feature encodings and survival analysis of transcriptome-based future studies, especially on small datasets. The model and the code are available at http://www.healthinformaticslab.org/supp/.

Motion control is critical in mobile robot systems, which determines the reliability and accuracy of a robot. Due to model uncertainties and widespread external disturbances, a simple control strategy cannot match tracking accuracy with disturbance immunity, while a complex controller will consume excessive energy. For precise motion control with disturbance immunity and low energy consumption, a control method based on an enhanced reduced-order extended state observer (ERESOBC) is proposed to control the motor-wheels dynamic model of a differential driven mobile robot (DDMR). In this method, only unknown state error and negative disturbance are estimated by the enhanced reduced-order extended state observer (ERESO), which reduces the required energy of the observer. In addition, a simple state-feedback-feedforward controller is used to track the reference signal and compensate for negative disturbance. Through numerical simulation and application example, the tracking performance and disturbance rejection performance of DDMR are compared with the traditional control method based on enhanced extended state observer (EESOBC), and the results show the superiority of the ERESOBC method.

This article addresses the design of an event-triggered finite-time singularity-free terminal sliding-mode control algorithm for the tracking of Euler–Lagrange (EL) systems subject to state/error constraints, unstructured dynamics, and external disturbances. First, a novel sliding-mode manifold (SMM), not only devoted to the finite-time convergence of tracking errors to a small residual set around zero but also ensuring stringent constraint requirements, is proposed. The SMM is available for both the constrained and unconstrained EL systems in a unified manner with no structural changes. Then, a fuzzy logic system is introduced to dynamically compensate for uncertainties in the system. An extra robustifying term is added to the training policy to accelerate online learning. Also, an event-triggered mechanism is integrated into the tracker design procedure to reduce the frequency of signal transmission. Stability analysis proves that all the closed-loop signals are uniformly bounded, and numerical simulations further illustrate the theoretical findings.

This article investigates the problem of path following for the underactuated unmanned surface vehicles (USVs) subject to state constraints. A useful control algorithm is proposed by combining the backstepping technique, adaptive dynamic programming (ADP), and the event-triggered mechanism. The presented approach consists of three modules: guidance law, dynamic controller, and event triggering. First, to deal with the "singularity" problem, the guidance-based path-following (GBPF) principle is introduced in the guidance law loop. In contrast to the traditional barrier Lyapunov function (BLF) method, this article converts the USV's constraint model to a class of nonlinear systems without state constraints by introducing a nonlinear mapping. The control signal generated by the dynamic controller module consists of a backstepping-based feedforward control signal and an ADP-based approximate optimal feedback control signal. Therefore, the presented scheme can guarantee the approximate optimal performance. To approximate the cost function and its partial derivative, a critic neural network (NN) is constructed. By considering the event-triggered condition, the dynamic controller is further improved. Compared with traditional time-triggered control methods, the proposed approach can greatly reduce communication and computational burdens. This article proves that the closed-loop system is stable, and the simulation results and experimental validation are given to illustrate the effectiveness of the proposed approach.

Macrophage immunotherapy is a promising clinical approach to treat cancer. However, low targeting efficiency severely limits the immunotherapeutic effect of macrophages. Here, we report a unique macrophage robot that can target and kill cancer cells using a combination of external acoustic and magnetic fields. First, the inactive macrophages (Mø) are magnetized by endocytosis of the$\gamma$-Fe2O3nanoparticles (FeNPs). Then, the magnetized M⊘can be moved towards the capillary wall under the influence of an acoustic radiation force generated from a lead zirconate titanate piezoelectric (PZT) transducer. Finally, the magnetized cells rotate forward under the action of alternating magnetic fields (AMF). During the process of magnetizing macrophages, FeNPs activate the anti-tumor immune activity of macrophages (M1) to induce cancer cell death. Overall, the present study highlights a novel cell robot that can target and kill cancer cells. Considering that the nanoparticles, macrophages, magnetic fields, and ultrasound technology have all been FDA approved for clinical settings, our targeted delivery system has tremendous clinical translational potential.

This article focuses on static output feedback control for fuzzy Markovian switching singularly perturbed systems (FMSSPSs) with deception attacks and asynchronous quantized measurement output. Different from the previous work, both the logarithmic quantizer and the static output feedback controller are dependent on the operation system; by means of hidden Markov models, their modes run asynchronously with that of FMSSPSs. Additionally, the deception attacks are guided by a Bernoulli variable, and nonlinear characteristics are modeled by the Takagi–Sugeno fuzzy model. By resorting to a mode-dependent Lyapunov functional, several criteria are acquired and strictly$(\mathscr {Q},\mathscr {S},\mathscr {R})\text{--}\gamma$-dissipative of FMSSPSs can be ensured. Finally, a dc motor model is expressed to illustrate the effectiveness of the asynchronous control scheme.

In this article, the fuzzy$\mathcal {H}_{\infty }$sliding mode control problem for continuous-time switched nonlinear systems is investigated. The switching signal conforms to the persistent dwell-time switching mechanism. The first objective of this article is to construct a switched integral sliding surface that not only accommodates the switched nonlinear model, but ensures that the sliding mode dynamics are globally uniformly exponentially stable and have an$\mathcal {H}_{\infty }$performance by the equivalent controller derived from the sliding surface. Another aim is to integrate the switched sliding mode control law to force the system trajectories to the sliding surface in a finite amount of time. Then, on the basis of the Lyapunov function technique, the specific form of the sliding mode control law gains is given first, and later the finite-time reachability of the sliding surface is ensured. Finally, the validity of the proposed switched sliding mode control method is validated by a numerical example.

In this article, the distributed fusion estimation issue with the dimensionality reduction strategy under DoS attacks and deception attacks is investigated for a class of stochastic uncertain systems with fading measurements. The stochastic uncertainties existed in the system and measurement equations are represented by state-dependent noises. The fading measurements are depicted by stochastic variables with known statistics. Then, a novel attack and compensation model is proposed to display the randomly occurring behaviors of the DoS attacks and the deception attacks within a unified framework. Furthermore, a distributed multisensor fusion estimation (DMSFE) algorithm is presented. An explicit form of dimensionality reduction is designed against attacks. Stability conditions are derived such that the mean square errors (MSEs) of the proposed DMSFE are bounded. A sequential covariance intersection fusion estimator (SCIFE) is designed to prevent the cross fusion covariance matrices calculating, which owns lower accuracy by smaller computation cost than DMSFE. An illustrative example is provided to show the effectiveness and merits of the proposed algorithm.

This paper investigates the issue of event-triggered adaptive finite-time state-constrained control for multi-input multi-output uncertain nonlinear systems. To prevent asymmetric time-varying state constraints from being violated, a tan-type nonlinear mapping is established to transform the considered system into an equivalent “non-constrained” system. By employing a smooth switch function in the virtual control signals, the singularity in the traditional finite-time dynamic surface control can be avoided. Fuzzy logic systems are used to compensate for the unknown functions. A suitable event-triggering rule is introduced to determine when to transmit the control laws. Through Lyapunov analysis, the closed-loop system is proved to be semi-globally practical finite-time stable, and the state constraints are never violated. Simulations are provided to evaluate the effectiveness of the proposed approach.

control and filtering problems for Markov jump singularly perturbed systems approximated by Takagi-Sugeno fuzzy models. The underlying transition probabilities (TPs) are assumed to vary randomly in a finite set, which is characterized by a higher level TP matrix. The mode- and variation-dependent fuzzy static output-feedback controller (SOFC) and filter are designed, respectively, to fulfill the control and filtering purposes. To facilitate the fuzzy SOFC synthesis, the closed-loop system is transformed into a fuzzy piecewise-homogeneous Markov jump singularly perturbed descriptor system (MJSPDS) by descriptor representation. A rigorous proof of mean-square exponential admissibility for the resulting fuzzy MJSPDS is presented. The criterion ensuring the mean-square exponential stability of the fuzzy filtering error system is further formed based on similar procedures. By setting the specific forms of the related matrix variables, the solutions for the predesigned fuzzy SOFC and filter are furnished, respectively. Finally, feasibility and validities of the developed fuzzy control and filtering results are verified by two practical examples.

Due to the complicated model characteristics, only a few results focusing on stability analysis have appeared on singularly perturbed descriptor systems (SPDSs). This article instead proposes an integral sliding-mode control strategy for a kind of Takagi-Sugeno fuzzy approximation-based nonlinear SPDSs under time-varying nonlinear perturbation. An appropriate fuzzy integral switching manifold that fully accommodates the system features is designed to completely reject the matched perturbation without amplifying the unmatched one. To facilitate the synthesis of the high-level controller (HLC), the sliding-mode dynamics (SMD) is transformed into an augmented form. Thanks to the adoptions of a novel singular perturbation Lyapunov function, Finsler's lemma, as well as the fixed-point principle, the existence and uniqueness of the solution and the exponential admissibility for the augmented SMD are analyzed. A solution for the designed HLC is further provided. To guarantee the sliding motion, a fuzzy integral sliding-mode controller (FISMC) is synthesized by analyzing the sliding motion reachability. An adaptive FISMC is also given to deal with the unknown upper bounds of the matched perturbation. Finally, the applicability of the developed FISMC strategy is testified by a practical example.

This article considers the problem of finite-time adaptive fuzzy output-feedback control design for multi-input–multioutput uncertain nonlinear systems subject to full state constraints. By employing the finite-time stability theory, a new finite-time adaptive fuzzy output-feedback control approach is proposed. An integral barrier Lyapunov functional is utilized to prevent all states from violating their constraints. Fuzzy logic systems are developed to approximate the uncertainties. A fuzzy state observer is constructed to estimate the unmeasurable states. Moreover, to handle the “explosion of complexity” issue in the backstepping control technique, a finite-time convergent differentiator is introduced to estimate the time derivatives of virtual control signals. The stability analysis showed that the control approach guarantees that all closed-loop signals are bounded, and the tracking errors converge to a small neighborhood of the origin in a finite time. Finally, the effectiveness of the proposed control scheme is confirmed by numerical simulations.

This article investigates an adaptive finite-time neural control for a class of strict feedback nonlinear systems with multiple objective constraints. In order to solve the main challenges brought by the state constraints and the emergence of finite-time stability, a new barrier Lyapunov function is proposed for the first time, not only can it solve multiobjective constraints effectively but also ensure that all states are always within the constraint intervals. Second, by combining the command filter method and backstepping control, the adaptive controller is designed. What is more, the proposed controller has the ability to avoid the "singularity" problem. The compensation mechanism is introduced to neutralize the error appearing in the filtering process. Furthermore, the neural network is used to approximate the unknown function in the design process. It is shown that the proposed finite-time neural adaptive control scheme achieves a good tracking effect. And each objective function does not violate the constraint bound. Finally, a simulation example of electromechanical dynamic system is given to prove the effectiveness of the proposed finite-time control strategy.

This article presents a fault-tolerant tracking control strategy for Takagi–Sugeno fuzzy model-based nonlinear systems which combines integral sliding mode control with adaptive control technique. Two common actuator faults: 1) loss of effectiveness and 2) increased bias input, are considered simultaneously. The fuzzy tracking control system is first established by incorporating the integral term of the output tracking error. Then, an appropriate fuzzy integral switching surface is designed such that the corresponding sliding motion only suffers from the unamplified unmatched disturbance. The solution of the nominal tracking controller can be transformed into a to convex optimization problem. In particular, an adaptive fuzzy sliding mode tracking controller is synthesized to ensure the accessibility of the sliding motion despite the effect of actuator faults and unknown disturbances. Finally, the proposed tracking strategy is verified by applying it to the dynamic positioning control of unmanned marine vehicles.

This paper is concerned with the sliding mode control problem for a class of continuous-time switched neural networks with mode-dependent average dwell time (MDADT). The considered continuous-time switched neural networks are motivated by biological neural networks which contain a nonlinear term and a changeable switched signal. The concept of MDADT is introduced, in which every subsystem has its own dwell time before switching to another subsystem. Moreover, a novel sliding mode controller is designed by an event-triggered mechanism which is based on the observer error and the system mode, where its triggered condition can be more conservative and practical than the existing triggered conditions. Sufficient conditions are derived to ensure that the closed-loop system is stochastically exponentially stable in terms of linear matrix inequalities. The designed sliding mode controller can promote the sliding mode motion of the system state. Finally, an illustrative example is provided to demonstrate the effectiveness and merits of the proposed method.

In this paper, the problems of output tracking control and filtering are investigated for Takagi-Sugeno fuzzy-approximation-based nonlinear descriptor systems in the discrete-time domain. Especially, the unreliability of the communication links between the sensor and actuator/filter is taken into account, and the phenomenon of packet dropouts is characterized by a binary Markov chain with uncertain transition probabilities, which may reflect the reality more accurately than the existing description processes. A novel bounded real lemma (BRL), which ensures the stochastic admissibility with H∞performance for fuzzy discrete-time descriptor systems despite the uncertain Markov packet dropouts, is presented based on a fuzzy basis-dependent Lyapunov function. By resorting to the dual conditions of the obtained BRL, a solution for the designed fuzzy output tracking controller is given. A design method for the fullorder fuzzy filter is also provided. Finally, two examples are finally adopted to show the applicability of the achieved design strategies.

In this paper, we study the exponential stabilization problem for continuous-time Takagi-Sugeno fuzzy systems subject to aperiodic sampling. By aiming to transmission reduction, an appropriate aperiodic event-triggered communication scheme with adaptive mechanism is put forward, which covers the existing periodic mechanisms as special cases. For the sake of reduction in design conservativeness, both the available information of sampling behavior and threshold error are fully acquired by constructing a novel time-dependent Lyapunov functional. Then, a new exponential stability criterion is presented to establish the quantitative relationship among the adaptive adjusted event threshold, the decay rate, the upper bound, and the lower bound of variable sampling period, simultaneously. By resorting to a matrix transformation, the corresponding stabilization criterion is further derived by which the sampled-data controller can be obtained. Finally, two illustrative examples are provided to demonstrate the virtue and applicability of proposed design method.

This paper investigates the problem of stabilization of sampled-data neural-network-based systems with state quantization. Different with previous works, the communication limitation of state quantization is considered for the first time. More specifically, it is assumed that the sampled state measurements from sensor to the controller are quantized via a quantizer. To reduce conservativeness, a novel piecewise Lyapunov-Krasovskii functional (LKF) is constructed by introducing a line-integral type Lyapunov function and some useful terms that take full advantage of the available information about the actual sampling pattern. Based on the new LKF, much less conservative stabilization conditions are derived to obtain the maximal sampling period and the minimal guaranteed cost control performance. The desired quantized sampled-data three-layer fully connected feedforward neural-network-based controllers are designed by a linear matrix inequality approach. A search algorithm is given to find the optimal values of tuning parameters. The effectiveness and advantage of proposed method are demonstrated by the numerical simulation of an inverted pendulum.

This paper addresses the sampled-data stabilization problem for chaotic systems represented by Takagi-Sugeno (T-S) fuzzy models. If the upper bounds for the time derivative of membership functions are available, combining the fuzzy blending for some quadratic functions together with the introduction of some new useful terms, a novel fuzzy time-dependent Lyapunov-Krasovskii functional (LKF) is proposed to fully capture the available characteristics of the actual sampling pattern and membership functions simultaneously. Based on the proposed LKF, a new criterion dependent on the upper bounds for the time derivative of membership functions is presented to guarantee the asymptotic stability of the whole closed-loop system. Moreover, a stability criterion independent of the upper bounds is also provided based on the corresponding common time-dependent LKF. Then, the designed fuzzy sampled-data controller can be synthesized by analyzing the corresponding stabilization conditions. Moreover, a search algorithm is provided to find the optimal tuning parameters. Finally, one practical example of the Lorenz system is given to illustrate that much less conservativeness can be achieved compared with the earlier results by using the corresponding common LKF, and the results can be further improved when adopting the fuzzy time-dependent LKF within large upper bounds.

This brief presents a new guaranteed performance state estimation criterion for delayed static neural networks. To facilitate the use of the slope information about activation function, the estimation error of activation function is separated into two parts for the first time. Then, a novel Lyapunov-Krasovskii functional (LKF) is constructed, which has fully captured the slope information of the activation. Based on the new LKF, a less conservative design criterion of estimator is derived to ensure the asymptotic stability of estimation error system with performance. The desired estimator gain matrices and the performance index are obtained by solving a convex optimization problem. The simulation results show that the proposed method has much better performance than the most recent results.