Bearing Rigidity-Based Flocking Control of AUVs via Semi-Supervised Incremental Broad Learning.

Flocking control of autonomous underwater vehicles (AUVs) has been regarded as the basis of many sophisticated marine coordination missions. However, there is still a research gap on the flocking of AUVs in weak communication and complex marine environment. This article attempts to fill up the above research gap from graph theory and intelligent learning perspectives. We first employ the bearing rigidity graph to describe the topology relationships of AUVs, through which an iterative gradient decent-based localization estimator is provided to obtain the position information. In order to improve the localization accuracy and energy efficiency, a min-weighted bearing rigidity graph generation strategy is developed. Along with this, we adopt the semi-supervised broad learning system (BLS) to design the model-free flocking controllers for AUVs in obstacle environment. The innovations of this article are summarized as follows: 1) the min-weighted bearing rigidity-based localization strategy can balance the localization accuracy and communication consumption as compared to the neighboring rule-based solutions and 2) the semi-supervised broad learning-based flocking controller can decrease the training time and solve the label limit over the supervised learning-based controllers. Finally, simulation and experimental studies are provided to verify the effectiveness.

Aß-Aggregation-Generated Blue Autofluorescence Illuminates Senile Plaques as well as Complex Blood and Vascular Pathologies in Alzheimer's Disease.

Senile plaque blue autofluorescence was discovered around 40 years ago, however, its impact on Alzheimer's disease (AD) pathology has not been fully examined. We analyzed senile plaques with immunohistochemistry and fluorescence imaging on AD brain sections and also Aß aggregation in vitro. In DAPI or Hoechst staining, the nuclear blue fluorescence could only be correctly assigned after subtracting the blue plaque autofluorescence. The flower-like structures wrapping dense-core blue fluorescence formed by cathepsin D staining could not be considered central-nucleated neurons with defective lysosomes since there was no nuclear staining in the plaque core when the blue autofluorescence was subtracted. Both Aß self-oligomers and Aß/hemoglobin heterocomplexes generated blue autofluorescence. The Aß amyloid blue autofluorescence not only labels senile plaques but also illustrates red cell aggregation, hemolysis, cerebral amyloid angiopathy, vascular plaques, vascular adhesions, and microaneurysms. In summary, we conclude that Aß-aggregation-generated blue autofluorescence is an excellent multi-amyloidosis marker in Alzheimer's disease.

Bearing-Based Robust Formation Tracking Control of Underactuated AUVs With Optimal Parameter Tuning.

This article investigates the control problem of bearing-based formation tracking for underactuated autonomous underwater vehicles (AUVs) considering actuator constraints and unknown disturbances. A leader-follower structure is adopted, where the leaders move with an unknown reference velocity. For the followers, an integrated strategy is proposed, which includes i) a bearing-based control method composed of a reference velocity estimator, a virtual velocity for achieving the desired formation, and an adaptive robust formation controller to track the virtual velocity under disturbances; and ii) a parameter tuning method based on control parameterization approaches and heuristic algorithms. By employing the cascade system theory, asymptotic convergence of errors in the overall system is proved in the presence of unknown disturbances. The tuning method optimizes controller gains to ensure, all while preserving the convergence properties of the closed-loop error system constraint feasibility and performance optimality. As a result, convergence, robustness, feasibility, and optimality are all achieved. Extension to the case where AUVs have sideslip motions in 3-D space is also discussed. Simulation results are presented to demonstrate the effectiveness of the proposed strategy.

Heterogeneously Networked Evolutionary Games With Intergroup Conflicts.

Network games primarily explore the intricacies of individual interactions and adaptive strategies within a network. Building upon this framework, the present study delves into the modeling, analysis, and control of heterogeneously networked evolutionary games with intergroup conflicts heterogeneously networked evolutionary games with intergroup conflict (HNEG-IC), where attacking players possess area-monitoring capabilities with limited attacking power. To begin with, a mathematical model is introduced to capture intragroup strategy dynamics and intergroup conflicts of HNEGs-IC via the algebraic state space representationalgebraic state space representation (ASSR). A necessary and sufficient condition for achieving global cooperation of HNEGs-IC is established. Then, a criterion for verifying the κ -cooperation below a certain mortality is presented. Considering the HNEGs-IC with strategy feedback control, it is proven that the feedback control, subject to global cooperation, is robust to conflicts when the intersection of the strategy threshold set and the reachable set of the preset initial strategy profiles is empty. Finally, for verification and demonstration, the obtained results are applied to a simplified virtual game model of the NATO and the Warsaw Pact.

Energy-Efficient Optimal Sensor Scheduling for State Estimation Over Multihop Sensor Networks.

In this article, we consider the power scheduling problem of the multihop transmission with limited power resources. For a discrete-time linear time-invariant process, we consider a more practical scenario where the forward-error-correcting (FEC) coding scheme is utilized. An approximate communication model is introduced to formulate the nonanalytical relationship between the consumption of power and the successful-decoding-probability. For the single-hop transmission, we propose an analytical method to figure out the optimal offline scheduling for the finite-time case and the optimal periodic schedule for the infinite-time case. We consider the process and terminal errors simultaneously, and explicitly discuss how different values of parameters affect the optimality. Moreover, we extend our conclusions to the multihop case. In order to deal with the difficulty and complexity brought by the multihop scenario, a novel method based on the equivalent-scheduling matrix (ESM) is proposed to describe the accumulated effects through the multihop transmission. Meanwhile, explicit solutions of the multihop case are provided for finite- and infinite-time cases, respectively. Numerical examples are provided to demonstrate the effectiveness of the proposed methods.

Bearing-Based Formation Tracking Control With Time-Varying Velocity Estimation.

This article studies the bearing-based formation tracking control problem of multiple double-integrator agents. A leader-following structure, where the leader moves with the reference dynamics, is adopted. Different from the existing methods, which require complete information of the time-varying reference velocity, in this article, only the time-varying reference orientation information is known by part of the followers and the amplitude of the reference velocity is unknown. To solve the problem, this article proposes a velocity-estimation-based control scheme, which consists of an estimator for estimating the varying rate of the reference orientation, an adaptation law for estimating the amplitude of the reference velocity, and bearing-based control inputs for tracking the leader and achieving the bearing-based formation based on the estimations. Moreover, the scaling formation maneuver can be achieved by using an auxiliary distance measurement. It shows that both the estimation errors and control errors converge to zero under the connectivity of the topology and properties of bearing rigidity. The closed-loop system is analyzed to be semiglobally uniformly asymptotically stable based on the cascaded system theory. Numerical simulations are presented to demonstrate the effectiveness of our method.

LFT: Neural Ordinary Differential Equations With Learnable Final-Time.

Since the last decade, deep neural networks have shown remarkable capability in learning representations. The recently proposed neural ordinary differential equations (NODEs) can be viewed as the continuous-time equivalence of residual neural networks. It has been shown that NODEs have a tremendous advantage over the conventional counterparts in terms of spatial complexity for modeling continuous-time processes. However, existing NODEs methods entail their final time to be specified in advance, precluding the models from choosing a desirable final time and limiting their expressive capabilities. In this article, we propose learnable final-time (LFT) NODEs to overcome this limitation. LFT rebuilds the NODEs learning process as a final-time-free optimal control problem and employs the calculus of variations to derive the learning algorithm of NODEs. In contrast to existing NODEs methods, the new approach empowers the NODEs models to choose their suitable final time, thus being more flexible in adjusting the model depth for given tasks. Additionally, we analyze the gradient estimation errors caused by numerical ordinary differential equations (ODEs) solvers and employ checkpoint-based methods to obtain accurate gradients. We demonstrate the effectiveness of the proposed method with experimental results on continuous normalizing flows (CNFs) and feedforward models.

Colon tissue image segmentation with MWSI-NET.

Developments in deep learning have resulted in computer-aided diagnosis for many types of cancer. Previously, pathologists manually performed the labeling work in the analysis of colon tissues, which is both time-consuming and labor-intensive. Results are easily affected by subjective conditions. Therefore, it is beneficial to identify the cancerous regions of colon cancer with the assistance of computer-aided technology. Pathological images are often difficult to process due to their irregularity, similarity between cancerous and non-cancerous tissues and large size. We propose a multi-scale perceptual field fusion structure based on a dilated convolutional network. Using this model, a structure of dilated convolution kernels with different aspect ratios is inserted, which can process cancerous regions of different sizes and generate larger receptive fields. Thus, the model can fuse detailed information at different scales for better semantic segmentation. Two different attention mechanisms are adopted to highlight the cancerous areas. A large, open-source dataset was used to verify improved efficacy when compared to previously disclosed methods.

Edge Sensing and Control Co-Design for Industrial Cyber-Physical Systems: Observability Guaranteed Method.

The new generation of the industrial cyber-physical system (ICPS) supported by the edge computing technology facilitates the deep integration of sensing and control. System observability is the key factor to characterize the internal relationship of them. In most existing works, the observability is regarded as the assumption for subsequent sensing and control. But, in fact, with the gradually expanded network scale, this assumption is more difficult to directly satisfy sensing design. For this problem, we propose the observability guaranteed method (OGM) for edge sensing and control co-design. Specifically, the nonconvex observability condition is transformed into the convex range of key parameters of the sensing strategy based on the graph signal processing (GSP) technology. Then, we establish the relationship between these parameters and control performance. In OGM, except the previous design from sensing to control, we reversely adjust the sensing design for control demands to satisfy observability. Finally, our algorithm is applied into the hot rolling laminar cooling process based on the semiphysical evaluation. The effectiveness is verified by the results.

Communication-Efficient and Collision-Free Motion Planning of Underwater Vehicles via Integral Reinforcement Learning.

Motion planning of underwater vehicles is regarded as a promising technique to make up the flexibility deficiency of underwater sensor networks (USNs). Nonetheless, the unique characteristics of underwater channel and environment make it challenging to achieve the above mission. This article is concerned with a communication-efficient and collision-free motion planning issue for underwater vehicles in fading channel and obstacle environment. We first develop a model-based integral reinforcement learning (IRL) estimator to predict the stochastic signal-to-noise ratio (SNR). With the estimated SNR, an integrated optimization problem for the codesign of communication efficiency and motion planning is constructed, in which the underwater vehicle dynamics, communication capacity, collision avoidance, and position control are all considered. In order to tackle this problem, a model-free IRL algorithm is designed to drive underwater vehicles to the desired position points while maximizing the communication capacity and avoiding the collision. It is worth mentioning that, the proposed motion planning solution in this article considers a realistic underwater communication channel, as well as a realistic dynamic model for underwater vehicles. Finally, simulation and experimental results are demonstrated to verify the effectiveness of the proposed approach.

Reset Observer-Based Zeno-Free Dynamic Event-Triggered Control Approach to Consensus of Multiagent Systems With Disturbances.

In this article, we investigate the observer-based event-triggered consensus problem of multiagent systems with disturbances. A reset observer consisting of a linear observer and reset element is proposed, the reset element endows the reset observer the ability to improve transient estimation performance compared with traditional linear observers. A hybrid dynamic event-triggering mechanism (ETM) is proposed, in which an internal timer variable is introduced to enforce a lower bound for the triggering intervals such that Zeno-free triggering can be guaranteed even in the presence of disturbances. Then, in order to describe the closed-loop system with both flow dynamics and jump dynamics, a hybrid model is constructed, based on which the Lyapunov-based consensus analysis and dynamic ETM design results are presented. In contrast with linear observer-based consensus protocols and the existing dynamic ETMs, the system performance can be improved and continuous communication between neighboring agents is not needed. Finally, a simulation example is provided to show the effectiveness of the proposed methods.

Fixed-Time Prescribed Tracking Control for Stochastic Nonlinear Systems With Unknown Measurement Sensitivity.

This article is concerned with the fixed-time prescribed tracking control problem for the uncertain stochastic nonlinear systems subject to input quantization and unknown measurement sensitivity. Different from existing results, the sensitivity on the sensor for measuring the system state is considered as an unknown parameter instead of the known one. Due to unknown measurement sensitivity on the sensor, the real system state cannot be obtained by measurement; hence, we put forward a new feedback control algorithm by the use of the unreal measured value of the system state. Moreover, the fixed-time prescribed performance on the output tracking error is investigated by developing a novel performance function. By means of the backstepping method, an adaptive quantized controller is designed for the system. Based on the Lyapunov stability theory, it is proved that the controller can render the output tracking error that satisfies the fixed-time prescribed performance and all signals of the resulting closed-loop system are bounded in probability. Finally, simulation results are provided to illustrate the effectiveness of the proposed control algorithm.

Stabilization and Data-Rate Condition for Stability of Networked Control Systems With Denial-of-Service Attacks.

This article investigates the stabilization control and stabilizing data-rate condition problems for networked control systems, which transmit signals from the sensor to the controller over the communication network with denial-of-service (DoS) attacks. Considering a class of DoS attacks that only constrain its frequency and duration, we aim to explore the constraint condition for stabilization and minimum stabilizing data rate of the networked control systems. The framework consists of two main parts. The first part considers the stabilizing control by the state-feedback approach under ideal bandwidth capacity. While the second part characterizes the average stabilizing data rate in terms of the eigenvalues of system matrix and DoS constraint functions to explicitly reveal the relationship between the attacks and the network bandwidth capacity. The stabilizing result is novel in the sense that the DoS-attack intensity, which is characterized by its frequency and duration, can vary for different time intervals. With this feature, the minimum average data-rate condition can vary for different time intervals according to the intensity of DoS attacks.

Distributed Output-Feedback Consensus Control for Nonlinear Multiagent Systems Subject to Unknown Input Delays.

This article considers the distributed output-feedback consensus control problem for nonlinear multiagent systems subject to input delays. Different from the existing related works, the input delay of each agent is described as an unknown time-varying function and is different from each other in this article. To deal with this problem, for each follower, we first construct a novel distributed observer based on the relative output information to asymptotically estimate the state information of the leader, then we introduce a classical observer to asymptotically estimate the state information of the follower based on its output information. By means of two observers, the leader-following consensus problem is transformed into the stability problem of the nonlinear system with unknown input delays. Subsequently, the distributed controller independent of delays is proposed for each follower by the use of the truncated prediction method under some conditions. Based on the Lyapunov stability theory, it is strictly proved that the distributed controller can render all agents achieving consensus. Finally, the effectiveness of the theoretical results is illustrated on the basis of numerical simulations on a group of single-link manipulators.

Accommodating Strategic Players in Distributed Algorithms for Power Dispatch Problems.

Distributed algorithms are gaining increasing research interests in the area of power system optimization and dispatch. Existing distributed power dispatch algorithms (DPDAs) usually assume that suppliers/consumers bid truthfully. However, this article shows the need for DPDAs to consider strategic players and to take account of their behavior deviation from what the DPDAs expect. To address this, we propose a distributed strategy update algorithm (DSUA) on top of a DPDA. The DSUA considers strategic suppliers who optimize their bids in a DPDA, using only the information accessible from a DPDA, that is, price. The DSUA also considers the cases when suppliers update bids alternately or simultaneously. Under both cases, we show the closeness of supplier bids to the Nash equilibrium via game-theoretic analysis as well as simulation.

Detecting slender objects with uncertainty based on keypoint-displacement representation.

Slender objects are long and thin objects. Existing object detection networks are not specially designed for detecting slender objects. We propose a method to detect slender objects. We represent slender objects with a keypoint-displacement pattern instead of using axis-aligned bounding boxes, avoiding problems like orientation confusion and wrong elimination. In our network, three parallel branches predict keypoint heatmaps, displacement vector field, and displacement uncertainty heatmap respectively. We add the uncertainty branch to enable our network to give uncertainty together with detection results. The predicted uncertainty provides a continuous criterion to evaluate whether detection results are reliable. In addition, the uncertainty branch can lower the weight of ambiguous training samples, leading to more accurate detection results. We employ our proposed method in two typical practical applications. Edges of electrode sheets and pins of electronic chips are correctly detected as slender objects. Manufacturing quality is evaluated through analyzing the detection results, including keypoint number, displacement property, and uncertainty value.

Finite-Time Observer-Based Leader-Following Consensus for Nonlinear Multiagent Systems With Input Delays.

This article studies the finite-time observer-based leader-following consensus problem for a class of nonlinear multiagent systems with nonuniform time-varying input delays. Existing works usually assume that input delays are the same constants and the input of the leader is available for each follower. In this article, we propose a new distributed consensus algorithm to relax the conservative condition. A novel finite-time distributed observer is designed for each follower, which can accurately estimate the state information of the leader in a setting time. By means of the observer, the distributed controller is proposed for each follower, and it depends only on the state information of the follower and the estimated-state information of its neighbor agents. Based on the Lyapunov stability theory, it is strictly proved that all agents can achieve a consensus. Finally, the effectiveness of the theoretical results is verified by numerical simulation on a group of single-link manipulators.

Distributed Containment Control for Nonlinear Stochastic Multiagent Systems.

This paper addresses the output feedback distributed containment control problem for a class of nonlinear stochastic multiagent systems under a fixed directed graph. Existing works usually design the containment control protocol using backstepping design method based on a conservative Lipschitz condition on nonlinear functions, which has a tedious control design procedure. In this paper, a new output feedback distributed containment control algorithm is proposed based on a novel dynamic compensator, which can not only simplify the control design procedure but also relax the condition on nonlinear terms. The proposed distributed containment protocol for each agent depends only on the agent output and the relative outputs of its neighbor agents, and can reduce the communication burden between the agents. Based on the Lyapunov stability theory, it is proved that the outputs of the followers are driven into the convex hull spanned by the outputs of the leaders with the proposed linear controller. Finally, the effectiveness of the theoretical results is illustrated by simulation examples.

Reset Control for Consensus of Multiagent Systems with Event-Triggered Communication.

In the existing literature, reset control has been shown to have great potential to improve transient performance of linear systems, but reset-induced jump dynamics bring difficulties for stability analysis, especially under the network environment. In this article, we apply reset control to the consensus of multiagent systems (MASs) with event-triggered communication, and a novel reset mechanism (RM) and a hybrid event-triggering mechanism (ETM) are proposed with guaranteed Zeno-freeness. The state space is decomposed into multiple flow sets and jump sets according to the RM and ETM, and a hybrid model of the MASs is constructed such that the reset induced and event-trigger induced jump dynamics can be handled in a unified framework. Based on the hybrid model, a novel hybrid systems framework is presented for consensus analysis and co-design of RM and ETM. Finally, the proposed design is verified with a simulation example.

Dissipativity Analysis for T-S Fuzzy System Under Memory Sampled-Data Control.

The dissipative stability problem for a class of Takagi-Sugeno (T-S) fuzzy systems with variable sampling control is the focus of this paper. The controller signals are assumed to transmit with a constant delay. Our aim is to design the sampled-data controller such that the T-S fuzzy system is globally asymptotically stable with a (Q,S,R) - γ -dissipative performance index. The stability is analyzed by using a novel piecewise Lyapunov-Krasovskii functional (LKF) together with a looped-functional and free-matrix-based (FMB) inequality method. First, several useful linear matrix inequality (LMI) conditions are derived to verify the dissipative stability of the T-S fuzzy system and then the controller gains matrices are expressed by resorting the LMI approach with the maximal-allowable upper bound (MAUB) of sampling periods. The proposed LMI conditions can be easily solved by using the MATLAB tool box. Finally, the numerical example of a truck-trailer system is considered and analyzed by the proposed scheme to illustrate the benefit and superiority.