This article investigates a conflict-constrained control method for switched multiagent systems with nonaffine nonlinear faults. Existing studies on state constraints often assume that the reference signal always stays within the constraint set. However, in practice, constraints may be dynamically detected during system operation and conflict with predefined reference signals, causing brief violations of the constraint boundaries. When the reference signal cannot remain within the prescribed range, many backstepping control methods based on barrier Lyapunov function and nonlinear transformations become ineffective. To address this, a new safe reference signal is constructed by using a virtual circle approach, and a conflict-constrained control method is proposed. By combining a common Lyapunov function and the radial basis function neural network, the effects of switching behavior and nonaffine nonlinear faults can be effectively compensated. A shift function is also introduced in the coordinate transformation to further relax constraints on initial values. The proposed method is validated through multiple simulation experiments.

This article explores an adaptive security formation control issue for nonlinear multiagent systems (MASs) against unknown deception attacks. The stable operation of multiagent formation is highly dependent on effective communication transmission between agents. To realize the formation control task, first, a state observer is developed to estimate the states under FDI attacks. Then, the nonlinear state-dependent function is introduced to cope with the asymmetric constraints to ensure a safe and stable operation environment of the agent formation. Furthermore, with the help of coordinate transformation and fuzzy logic systems (FLSs), a fuzzy adaptive formation control scheme with an attack compensation mechanism is developed so that various desired formation patterns are achieved with free collision. Under the proposed scheme, the resulting formation tracking error is uniformly ultimately bounded, and the state constraint of the multiagent system is always maintained, even if the agents are subjected to malicious unknown deception attacks. The effectiveness of the control method is validated through simulation examples.

This paper addresses the tracking control problem for nonlinear multi-agent systems by developing a quantitative prescribed performance control framework. Under the backstepping design architecture, all virtual control signals and actual control inputs are constructed using quantized signals at each recursive step. A novel continuous and differentiable quantization function is employed to eliminate discontinuities commonly associated with traditional discrete quantizers, thereby ensuring smooth control transitions and reducing communication load. To strictly guarantee adherence to the prescribed tracking performance, a dynamic performance function incorporating quantized errors is further introduced. Moreover, the proposed scheme exhibits robustness against actuator faults and maintains satisfactory control accuracy in faulty scenarios. The effectiveness and superiority of the proposed approach are validated through comprehensive simulation.

This article investigates a predefined-time optimized consensus secure control problem for nonlinear multiagent systems, where the leader to follower and follower to neighbor agent network communication are subjected to deferred denial-of-service (DoS) attacks. To observe the leader's state and mitigate the adverse effects of DoS attacks on the system, a switching consensus leader observer is designed. Through the synergistic use of backstepping control, predefined-time control theory, and adaptive dynamic programming, a Hamilton–Jacobi–Bellman equation is constructed for each subsystem to ensure the optimal control performance of the overall system. The identifier neural network is incorporated to approximate the unknown uncertainties existing in the system. Through the construction of the critic network, the proposed controller satisfies the Bellman optimality principle, from which the optimal controller of the system is derived. By employing the Lyapunov stability theorem, it is proven that all system signals remain bounded within the predefined time interval, and the followers' outputs ultimately synchronize with the leader's state. Finally, simulation studies are conducted to validate the feasibility and effectiveness of the proposed control scheme.

Recently, camera-based solutions have been extensively explored for scene semantic completion (SSC). Despite their success in visible areas, existing methods struggle to capture complete scene semantics due to frequent visual occlusions. To address this limitation, this paper presents the first satellite-ground cooperative SSC framework, i.e., SGFormer, exploring the potential of satellite-ground image pairs in the SSC task. Specifically, we propose a dual-branch architecture that encodes orthogonal satellite and ground views in parallel, unifying them into a common domain. Additionally, we design a ground-view guidance strategy that corrects satellite image biases during feature encoding, addressing misalignment between satellite and ground views. Moreover, we develop an adaptive weighting strategy that balances contributions from satellite and ground views. Experiments demonstrate that SG-Former outperforms the state of the art on SemanticKITTI and SSCBench-KITTI-360 datasets. Our code is available on https://github.com/gxytcrc/SGFormer.

Visual-inertial odometry (VIO) has made significant progress in various applications. However, one of the key challenges in VIO is the efficient and robust fusion of visual and inertial measurements, particularly while mitigating the impact of sensor failures. To address this challenge, we propose a new learning-based VIO system, i.e., DW-VIO, which is able to integrate multiple sensors and provide robust state estimations. To this end, we design a novel deep learning-based data-fusion approach that dynamically associates information from multiple sensors to predict sensor weights for optimization. Moreover, in order to improve the efficiency, we present several real-time optimization techniques including a fast patch graph constructor and an efficient GPU-accelerated multi-factor bundle adjustment layer. Experimental results show that DW-VIO outperforms most state-of-the-art (SOTA) methods on the EuRoC MAV, ETH3D-SLAM, and KITTI-360 benchmarks across various challenging sequences. Additionally, it maintains a minimum of 20 frames per second (FPS) on a single RTX 3060 GPU with high-resolution input, highlighting its efficiency.

This paper addresses the issue of distributed predefined-time output optimization for heterogeneous multi-agent systems with equality constraints. A novel distributed algorithm is proposed to enable state tracking of compensator variables under regulator equations assumption. By utilizing the properties of compensator variables and strongly convex functions, a predefined-time output optimization algorithm is proposed to achieve optimal output while satisfying given constraints. A key advantage of this algorithm is its flexibility, allowing convergence time is irrespective of control parameter constraints. Furthermore, a simplified segmented predefined-time optimization algorithm is presented to achieve output optimality within the specified time, reducing the computational and transmission information among agents. Finally, the practicality of the theoretical results is validated through a numerical example.

This paper researches the predefined-time fuzzy adaptive dynamics surface consensus control problem for nonlinear multi-agent systems with input saturation. With regard to nonlinear functions existing in the control systems, fuzzy logic system is employed to estimated them. A novel improved predefined-time dynamics surface filter is designed that can avoid the explosion of complexity problem, and the proposed filter satisfies predefined-time stable simultaneously. With the support of piecewise function, the singularity problem that may exist in virtual controller can be dodged greatly. Furthermore, a ameliorative predefined-time auxiliary dynamic system is presented to cope with input saturation. Combining adaptive backstepping control and predefined-time theory, a predefined-time adaptive fuzzy dynamics surface controller is presented that can assure the systems are predefined-time bounded, though the followers exists in the control saturation.Note to Practitioners—Numerous actual physical systems and devices can be modeled as uncertain nonlinear MAS. Furthermore, MAS also can be widely applied to disaster relief, spacecraft and multitudinous fields. On the one hand, the practical systems may exist in input saturation phenomenon due to human factors and in most of the relevant literatures, which can affect the system performance or bring about instability. On the other hand, the initial values of actual systems usually cannot be chosen freely because of the influence of environment and other factors. In addition, it is currently ordinarily supposed that realize the stabilization of controlled systems when time approaches infinity. Consequently, a predefined-time adaptive fuzzy controller is presented for MAS with control saturation, which can achieve the predefined-time stabilization of MAS.

This research considers the problem of event-based adaptive fault-tolerant control for nonlinear cyber-physical systems with deception attacks and actuator faults via intermittent available signals. Through the application of fuzzy logic systems, the unknown nonlinear functions of the systems are approximated. Then, a novel state observer is designed that can be driven by actuator faults and intermittent available signals arising from triggered attack-state signals, which can realize directly triggering the states after deception attacks and avoid the problem of virtual controller non-differentiability under the backstepping framework. In order to avoid the complexity explosion problem, the dynamics surface control method is introduced. Meanwhile, the dynamics surface filter signals also realize event-triggered control, and it results in substantial savings in communication resources. To verify the validity of the proposed method, two illustrative examples are presented. Note to Practitioners—Cyber-physical systems are frequently applied in modern industrial processes such as smart grids, industrial Internet of Things and so on. Nevertheless, the open network environment makes system components more vulnerable to attacks, posing a significant security threat to the operation of cyber-physical systems. When attackers deliver deceptive information to the sensors, the system state becomes inaccessible, which is a challenging issue based on the signals available after the attacks. Moreover, when actuators are affected by faults, system performance deteriorates, potentially leading to instability. Consequently, the event-triggered control problem of cyber-physical systems, considering both actuator faults and deceptive attacks, presents challenges for controller design. In order to resolve the issues outlined above, a fault-tolerant state observer based on intermittent available signals is developed. This design employs backstepping recursion and adaptive techniques to achieve stability for system under deception attacks and greatly alleviate communication burden, thereby enhancing the practicality of the proposed control strategy.

This article studies a prescribed finite-time consensus problem for uncertain nonlinear multiagent systems (MASs) with event-triggered updates. First, the novel finite-time performance boundaries are proposed to ensure that consensus deviations converge to the predefined steady-state zones within a preassigned time, and by using asymmetrically parallel boundaries to constrain consensus errors to narrow feasible regions, small overshoots of consensus errors are assured. Second, by utilizing the inherent approximation property of fuzzy logic systems (FLSs), a fuzzy state observer is devised to recover the unmeasurable states. Based on the observation outcomes, an improved event-triggered output-feedback controller is synthesized so that the number of control input updates is reduced without incurring an evidently deteriorated control performance. The salient merits of the proposed approach are that all consensus errors are free from great overshoots, while settling time can be explicitly assigned in advance. Finally, two examples are given to verify the validity of theoretical results.

One of the key challenges of visual Simultaneous Localization and Mapping (SLAM) in large-scale environments is how to effectively use global localization to correct the cumulative errors from long-term tracking. This challenge presents itself in two main aspects: first, the difficulty for robots in revisiting previous locations to perform loop closure, and second, the considerable memory resources required to maintain point-cloud-based global maps. Recent solutions have resorted into neural networks, using satellite images as the references for ground-level localization. However, most of these methods merely provide cross-view patch-matching results, which leads to unfeasible in integration with the SLAM system. To address these issues, we present a semantic-based cross-view localization method. This approach combines semantic information with a reward and penalty mechanism, enabling us to obtain a global probability map and achieve precise 3-degree-of-freedom (3-DoF) localization. Based on that, we develop a SLAM system that capitalizes on satellite imagery for global localization. This strategy effectively bridges the gap between SLAM and real-world coordinates while also substantially reducing accumulated errors. Our experimental results demonstrate that our global localization method significantly outperforms existing satellite-based systems. Moreover, in scenarios where the robot struggles to find loop closures, employing our localization method improves the SLAM accuracy.

This article investigates the cooperative control problem for stochastic multiagent systems (MASs) with dynamic constraints. A new universal barrier function is proposed, which is applicable to many systems with different types of constraint functions, even unconstrained systems. Several mapping functions are constructed to constrain the state variables directly without feasibility conditions, and the tracking control is achieved for stochastic MASs with deferred full-state constraints under the backstepping framework. In order to regulate the tracking error more precisely, the funnel error transformation is improved and the deferred funnel controller is developed by introducing a preassigned finite-time function. Based on the deferred funnel controller, the tracking error can be maintained within the predetermined funnel in the preassigned time. The convergence time can be defined according to the actual requirements, and it is independent of the design controller parameters and initial conditions. Finally, some simulation results are given to demonstrate the effectiveness of the proposed control algorithm.

In this article, a new leader-following tracking control approach is investigated for stochastic multiagent systems with multibridge-hole output constraints. The multibridge-hole output constraints mean that the output of the system is constrained in some intervals and unconstrained in other intervals. The constrained and unconstrained intervals can be set arbitrarily. By designing a new shift function to construct the barrier Lyapunov function, the optimal controller is constructed by combining the backstepping technique with the adaptive dynamic programming technique. The model network is used to estimate the unknown disturbances and uncertainty terms in the system. The critic network and the actor network are constructed such that the designed controller adheres to the Bellman optimality principle and gives the optimal solution of the system. The proposed control method is versatile and compatible with various types of output constrained control problems, such as unconstrained control problems, constrained control problems, and delay constrained problems without changing the structure of the controller. Finally, some simulation results are given to verify the effectiveness of the method.

This article investigates the tracking control problem for nonlinear systems. An adaptive model is proposed to represent the dead-zone phenomenon and solve its control challenge with a Nussbaum function in conjunction. Drawing inspiration from the existing prescribed performance control schemes, a novel dynamic threshold scheme is developed that fuses a proposed continuous function with a finite-time performance function. A dynamic event-triggered strategy is applied to reduce the redundant transmission. The proposed time-varying threshold control strategy has fewer updates than the traditional fixed threshold and improves the efficiency of resource utilization. A command filter backstepping approach is employed to prevent the complexity explosion faced by the computation. The suggested control strategy ensures that all system signals are bounded. The validity of the simulation results has been verified.

This article investigates the problem of adaptive fuzzy consensus control for a class of interconnected nonlinear multiagent systems. To effectively address the dual requirement of full-state constraints and prescribed performance, we propose a novel unified barrier function that effectively constrains the states and errors separately. This approach eliminates the need for tedious computations, allowing for direct presetting of state and error bounds. The concept of “bridge hole” is introduced, referring to the states' transition from unconstrained to constrained and back to unconstrained. The control method demonstrates the capability to handle multiple bridges, with the number of bridges being able to be${\bm {n}}$or 0. Furthermore, event-triggered mechanisms are also considered among neighbors and the internal event-triggered mechanism in the actuator-to-controller channel, with the aim of minimizing the communication burden. Finally, some simulation results are provided to validate the effectiveness of the approach.

This article studies a preassigned time adaptive tracking control problem for stochastic multiagent systems (MASs) with deferred full state constraints and deferred prescribed performance. A modified nonlinear mapping is designed, which incorporates a class of shift functions, to eliminate the constraints on the initial value conditions. By virtue of this nonlinear mapping, the feasibility conditions of the full state constraints for stochastic MASs can also be circumvented. In addition, the Lyapunov function codesigned by the shift function and the fixed-time prescribed performance function is constructed. The unknown nonlinear terms of the converted systems are handled based on the approximation property of the neural networks. Furthermore, a preassigned time adaptive tracking controller is established, which can achieve deferred prescribed performance for stochastic MASs that provide only local information. Finally, a numerical example is given to demonstrate the effectiveness of the proposed scheme.

Performing accurate localization while maintaining the low-level communication bandwidth is an essential challenge of multi-robot simultaneous localization and mapping (MR-SLAM). In this paper, we tackle this problem by generating a compact yet discriminative feature descriptor with minimum inference time. We propose descriptor distillation that formulates the descriptor generation into a learning problem under the teacher-student framework. To achieve real-time descriptor generation, we design a compact student network and learn it by transferring the knowledge from a pre-trained large teacher model. To reduce the descriptor dimensions from the teacher to the student, we propose a novel loss function that enables the knowledge transfer between two different dimensional descriptors. The experimental results demonstrate that our model is 30% lighter than the state-of-the-art model and produces better descriptors in patch matching. Moreover, we build a MR-SLAM system based on the proposed method and show that our descriptor distillation can achieve higher localization performance for MR-SLAM with lower bandwidth.

This article investigates the distributed fuzzy adaptive containment control problem of stochastic nonlinear multiagent systems under a directed communication topology suffering denial-of-service (DoS) attacks. First, considering unknown stochastic disturbance and nonlinear characteristics for the followers, the mathematical models are modeled as It$\hat{o}$stochastic nonlinearity terms approximated through fuzzy logic systems. Second, the proposed dynamically adjusted event-triggered condition can effectively avoid inefficient transmission behavior. Moreover, an adaptive compensation protocol for input saturation is constructed to eliminate the effects of nonlinearities caused by input saturation. Finally, instead of the previous uniformly ultimately bounded containment control results, stability and asymptotic performance are guaranteed through valid reasonable adaptive control laws acquired through the backstepping control approach despite suffering DoS cyberattacks. Moreover, a simulation is given to verify the feasibility of the proposed method.

In this article, the leaderless adaptive fuzzy consensus control problem is studied for a class of stochastic nonlinear multiagent systems with unknown measurement sensitivity under false data injection attacks. Unknown measurement sensitivity and false data injection attacks can prevent sensors from obtaining right state information and make it difficult to design controllers and adaptive laws. The existing works considered only one of these cases for deterministic systems. In this article, the coexistence of both cases is considered with the help of Nussbaum functions and fuzzy logic systems, and the corresponding controllers and auxiliary variables are not only codesigned to solve the problem, but also extended to stochastic multiagent systems. Then, an improved switching threshold event-triggered mechanism is proposed to reduce the communication burden of the control channel. Furthermore, the leaderless asymptotic consensus control scheme for stochastic multiagent systems is proposed. The boundedness of all signals and leaderless asymptotic consensus control performance are guaranteed via the Lyapunov stability theorem. Finally, two simulation examples are given to verify the effectiveness of the proposed control scheme.

In this article, the fixed-time fuzzy adaptive tracking control problem is studied for a class of one-link manipulator systems with stochastic disturbances and multiple constraints. First, a modified dynamic surface control technique is proposed for the stochastic system, which provides a useful filtering solution to the fixed-time control of the stochastic nonlinear systems. Then, an extended stochastic fixed-time stability criterion is introduced to simplify the complex discussion process of stability analysis instead of existing fixed-time stability criteria. By using the unified barrier function, the constrained nonlinear system can be transformed into the nonconstrained nonlinear system. Moreover, a fuzzy adaptive controller is constructed with the funnel error transformation function, which improves the transient response of the system. The control objective of this article is that the steady-state error can be driven to the prespecified funnel in a fixed time based on the Lyapunov stability theorem and the extended stochastic fixed-time stability criterion. Finally, a simulation example is given to demonstrate the effectiveness of the proposed method.

A distributed control law synthesized several effective tools which called adaptive compensators solve a containment control problem. Distinguish from many studies on tracking, the situation where external system information is not available to network nodes is considered in a multileader system under a structurally balanced topology, the designed compensator is used to estimate the adaptive state of the convex hull to each follower. Considering the special case of external system information, a novel adaptive internal model compensator is proposed to dispose of the system robustness problem. Moreover, a novel Sylvester equation is given without considering all closed-loop system information and present an algorithm for calculating adaptive solutions of Sylvester equations based on containment control.

This article studies the command-filter-based fixed-time bipartite containment control problem for a class of nonlinear stochastic multiagent systems (MASs). The considered stochastic MASs in nonstrict feedback form is subject to unknown nonlinear functions and stochastic disturbances, which can be solved by exploiting the universal approximation property of radial basis function neural networks. In addition, the event-triggered mechanism is used to improve the utilization of communication resources while avoiding Zeno behavior. The control protocol based on the command-filtered backstepping technique is proposed to ensure that the followers can converge to the convex hull formed by the leaders. Moreover, the closed-loop stability of stochastic MASs is proved to be semiglobal practical fixed-time stability. Finally, a numerical example simulation and an actual system simulation about a group of five single-link manipulator systems are presented to verify the effectiveness of the proposed method.

The RGB-Thermal (RGB-T) information for semantic segmentation has been extensively explored in recent years. However, most existing RGB-T semantic segmentation usually compromises spatial resolution to achieve real-time inference speed, which leads to poor performance. To better extract detail spatial information, we propose a two-stage Feature-Enhanced Attention Network (FEANet) for the RGB-T semantic segmentation task. Specifically, we introduce a Feature-Enhanced Attention Module (FEAM) to excavate and enhance multi-level features from both the channel and spatial views. Benefited from the proposed FEAM module, our FEANet can preserve the spatial information and shift more attention to high-resolution features from the fused RGB-T images. Extensive experiments on the urban scene dataset demonstrate that our FEANet outperforms other state-of-the-art (SOTA) RGB-T methods in terms of objective metrics and subjective visual comparison (+2.6% in global mAcc and +0.8% in global mIoU). For the 480 × 640 RGB-T test images, our FEANet can run with a real-time speed on an NVIDIA GeForce RTX 2080 Ti card.

This article studies the distributed observer-based event-triggered bipartite tracking control problem for stochastic nonlinear multiagent systems with input saturation. First, different from conventional observers, we construct a novel distributed reduced-order observer to estimate unknown states for the stochastic nonlinear systems. Then, an event-triggered mechanism with relative threshold is introduced to reduce the burden of communication. In addition, the bipartite tracking controller is proposed for stochastic multiagent systems by using fuzzy logic systems and the backstepping approach. Meanwhile, it is proved that the designed method can guarantee that all the signals in the closed-loop systems are bounded in probability, and the distributed consensus tracking errors can converge to a small neighborhood of the origin via the Lyapunov stability theory. Finally, a simulation example is given to prove the effectiveness of the designed scheme.

Using rules to extract protein-protein interactions (PPI) from biomedical literatures has shown recognized positive effect, but the process of making rules is time-costing and expensive. Relation dictionary-based rule is an effective way to solve the problem, while it also encounters a new problem: how to design an excellent dictionary fast and correctly. This paper proposes a weakly supervised method to construct the PPI relation dictionary, and presents a slot-filling method to learn PPI relation rules automatically according to the position of proteins and relation words. Moreover, this method does not depend on much more manual intervention. We conduct the experiment using 5 types of authoritative biomedical PPI corpus, and the results show that our method can improve the PPI extraction effect obviously.

Predicting disease genes in PPI network has attracted a lot of attention over the years. Based on the assumption that the phenotypes of the genes in the same complex where candidate gene located in are more similar to disease, the candidate gene is more possible to be disease gene, we propose a new disease gene identification method based on protein complex phenotype similarity. First, our method mines protein complexes in PPI network by resolution-limit-free clustering algorithm and maps the candidate genes to complexes. Second, we define phenotype similarity according to phenotype ontology, and calculate phenotype similarity value between gene and disease. Third, we add up the phenotype similarity value of whole genes in the complex as weight of candidate gene and rank the candidate gene according to the sum of phenotype similarity value. Finally, we test our method by leave-one-out cross validation. The results show that our method is effective and outperforms other methods such as NetRank, NetScore, NetZcore, Flow, RWR and NDRC. Importantly, we predict the disease gene of Prader-Willi syndrome (MIM: 176270) and Renal tubular dysgenesis (MIM: 267430) successfully, which do not exist in our disease-gene datasets but exists in online databases and scientific publications.