Resilient Output Formation-Tracking of Heterogeneous Multiagent Systems Against General Byzantine Attacks: A Twin-Layer Approach.

This work solves the countermeasure design problems of distributed resilient output time-varying formation-tracking (TVFT) of heterogeneous multiagent systems (MASs) against general Byzantine attacks (GBAs). Inspired by the concept of Digital Twin, a hierarchical protocol equipped with a twin layer (TL) is proposed, which decouples the above problem into the defense against Byzantine edge attacks (BEAs) on the TL and the defense against Byzantine node attacks (BNAs) on the cyber-physical layer (CPL). First, a secure TL with respect to (w.r.t.) the high-order leader dynamics is designed, which achieves resilient estimation against BEAs. A trusted-node strategy against BEAs is proposed, which promotes network resilience by protecting almost the smallest fraction of crucial nodes on the TL. It is proven that strongly (2f+1) -robustness w.r.t. the above trusted nodes is sufficient for the resilient estimation performance of the TL. Second, a decentralized adaptive and chattering-free controller against potentially unbounded BNAs is designed on the CPL. This controller has the merit of uniformly ultimately bounded (UUB) convergence and an assignable exponential decay rate when converging into the above UUB bound. To the best of our knowledge, this article is the first to achieve resilient output TVFT against GBAs, rather than under GBAs. Finally, the practicability and validity of this new hierarchical protocol are illustrated via a simulation example.

Adaptive Decentralized Control for Constrained Strong Interconnected Nonlinear Systems and Its Application to Inverted Pendulum.

This work is dedicated to adaptive decentralized tracking control for a class of strong interconnected nonlinear systems with asymmetric constraints. Currently, there are few related studies on unknown strongly interconnected nonlinear systems with asymmetric time-varying constraints. To deal with the assumptions of the interconnection terms in the design process, which include upper functions and structural restrictions, the properties of Gaussian function in radial basis function (RBF) neural networks are applied to overcome this difficulty. By constructing the nonlinear state-dependent function (NSDF) and using a new coordinate transformation, the conservative step that the original state constraint converts into a new boundary of the tracking error is removed. Meanwhile, the virtual controller's feasibility condition is removed. It is proven that all the signals are bounded, especially the original tracking error and the new tracking error, which are both bounded. In the end, simulation studies are carried out to verify the effectiveness and benefits of the proposed control scheme.

Morphological Feature Visualization of Alzheimer's Disease via Multidirectional Perception GAN.

The diagnosis of early stages of Alzheimer's disease (AD) is essential for timely treatment to slow further deterioration. Visualizing the morphological features for early stages of AD is of great clinical value. In this work, a novel multidirectional perception generative adversarial network (MP-GAN) is proposed to visualize the morphological features indicating the severity of AD for patients of different stages. Specifically, by introducing a novel multidirectional mapping mechanism into the model, the proposed MP-GAN can capture the salient global features efficiently. Thus, using the class discriminative map from the generator, the proposed model can clearly delineate the subtle lesions via MR image transformations between the source domain and the predefined target domain. Besides, by integrating the adversarial loss, classification loss, cycle consistency loss, and L1 penalty, a single generator in MP-GAN can learn the class discriminative maps for multiple classes. Extensive experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that MP-GAN achieves superior performance compared with the existing methods. The lesions visualized by MP-GAN are also consistent with what clinicians observe.

An Adaptive Fuzzy Approach to Fault Estimation Observer Design With Actuator Fault and Digital Communication.

This article investigates the fault estimation (FE) problem for a class of nonlinear systems via an adaptive fuzzy approach. Considering the limited communication capacity of networks, the quantized measurement signals are used to construct adaptive laws instead of the real measurements in the designed fuzzy observer. By injecting the quantizer parameter into the observer inputs, the quantization effects on the convergence of estimation errors can be compensated. It is also shown that nondifferentiable actuator faults can be reconstructed by the developed FE approach. Finally, two simulation examples are provided to illustrate the validity of the presented scheme.

Stability Analysis for H∞ -Controlled Active Quarter-Vehicle Suspension Systems With a Resilient Event-Triggered Scheme Under Periodic DoS Attacks.

The stability analysis is studied for H∞ controlled networked active quarter-vehicle suspension systems with a resilient event-triggered scheme (RETS) under periodic denial-of-service (DoS) jamming attacks in this article. For the networked suspension system, the system-state signals are measured by sensors and transmitted to the cloud controller through a wireless network and then the control signal is transferred to the actuator to control it. An event-triggered scheme (ETS) is designed to reduce the workload of data transmission, which is effective to select some most useful information to transmit and discard some redundant data. DoS attacks can block the data transmission when it is active, so a resilient event-triggered H∞ control method is built based on the Lyapunov stability theory. The exponential stability of the controlled suspension system, as well as the H∞ performance, is analyzed in this article. Some simulation results show that the proposed control method is effective to improve driving comfort and driving safety and reduce the workload of data transmission under periodic DoS attacks.

Neuroadaptive Control for Active Suspension Systems With Time-Varying Motion Constraints: A Feasibility-Condition-Free Method.

This work is devoted to solving the control problem of vehicle active suspension systems (ASSs) subject to time-varying dynamic constraints. An adaptive control scheme based on nonlinear state-dependent function (NSDF) is proposed to stabilize the vertical displacement of the vehicle body. It provides a reliable guarantee of driving safety, ride comfort, and operational stability. It is commonly known that in the existing work, either the state constraints are ignored which may reduce the stability and safety of the system, or the virtual controller is subjected to some feasibility conditions affecting real system implementation. In this work, it is the first attempt to directly deal with the time-varying displacement and velocity of the vehicle constraints in ASSs without involving any specific feasibility conditions. A novel coordinate transformation based on the NSDF is introduced and integrated into each step of the backstepping design. Thus, the proposed control scheme not only adapts to the time-varying motion (time-varying vertical displacement and velocity) constraints, but also eliminates the feasibility conditions of the virtual controller without the difficulty of obtaining system parameters. Finally, the control scheme for ASSs used in this work is compared with existing control schemes in order to demonstrate its superiority and rationality.

PD Control-Based Reachable Set Synthesis for Singular Takagi-Sugeno Fuzzy Systems With Time-Varying Delay.

This article focuses on the reachable set synthesis problem for singular Takagi-Sugeno fuzzy systems with time-varying delay. The main contribution is that we design a proportional plus derivative state feedback controller to ensure that the singular fuzzy system is normal and the system states are bounded by a derived ellipsoid. In the light of the Lyapunov stability theory and the parallel distributed compensation method, the sufficient criteria are shown in the format of linear matrix inequalities. Furthermore, we investigate another case of reachable set synthesis, where the reachable set to be found is contained in a given ellipsoid. Finally, we use two examples to exhibit the usefulness of the proposed method.

Dissipative filtering for two-dimensional LPV systems: A hidden Markov model approach.

In this article, the dissipative filtering problem is explored for two-dimensional (2-D) Markov jump linear parameter varying (MJLPV) systems. The underlying system is described based on the frequently-used Fornasini-Marchesini (FM) model with the measurement missing phenomenon. The hidden Markov model (HMM) is adopted to depict the partial accessibility between system and the designed filter modes. Based on this, a HMM-based filter is proposed, and sufficient conditions in the form of parameterized linear matrix inequalities (PLMIs) are established to guarantee that the filtering error system (FES) is asymptotically stable (AS) and strictly dissipative. Then, the corresponding filter synthesis problem is converted into a convex optimization problem. Ultimately, the simulation example is presented to demonstrate the utility of the obtained results.

Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis.

Fusing multi-modality medical images, such as magnetic resonance (MR) imaging and positron emission tomography (PET), can provide various anatomical and functional information about the human body. However, PET data is not always available for several reasons, such as high cost, radiation hazard, and other limitations. This paper proposes a 3D end-to-end synthesis network called Bidirectional Mapping Generative Adversarial Networks (BMGAN). Image contexts and latent vectors are effectively used for brain MR-to-PET synthesis. Specifically, a bidirectional mapping mechanism is designed to embed the semantic information of PET images into the high-dimensional latent space. Moreover, the 3D Dense-UNet generator architecture and the hybrid loss functions are further constructed to improve the visual quality of cross-modality synthetic images. The most appealing part is that the proposed method can synthesize perceptually realistic PET images while preserving the diverse brain structures of different subjects. Experimental results demonstrate that the performance of the proposed method outperforms other competitive methods in terms of quantitative measures, qualitative displays, and evaluation metrics for classification.

Dissipativity-Based Consensus Tracking of Singular Multiagent Systems With Switching Topologies and Communication Delays.

This article, based on dissipativity theory, aims to tackle the consensus tracking issue for Lipschitz nonlinear singular multiagent systems (MASs) with switching topologies and communication delays. Rooted at the leader node, a directed spanning tree is assumed to be contained in the union of all possible interaction graphs. Within the framework of topology switching controlled by a Markov chain, communication delays encountered in the data transmission process are reasonably considered to be time-varying and dependent on Markovian jump modes. By using tools from the stochastic Lyapunov functional technique, algebraic graph theory, and strict (Q,S,R)-α -dissipativity analysis, the consensus controller collecting delayed in-neighboring agents' information is designed to ensure stochastic admissibility and strict dissipativity of the resulting consensus error system. The theoretical analysis is validated by numerical simulations.

Reachable Set Estimation for Markovian Jump Neutral-Type Neural Networks With Time-Varying Delays.

The reachable set estimation problem for a class of Markovian jump neutral-type neural networks (MJNTNNs) with bounded disturbances and time-varying delays is tackled in this article. With the aid of the delay partitioning method, a novel stochastic Lyapunov-Krasovskii functional containing triple integral terms is constructed in mode-dependent augmented form. To begin with, transition probabilities of the concerned Markovian jump neural networks (NNs) are considered to be completely known. By employing the integral inequality approach and reciprocally convex combination method, it is proved that all state trajectories which start from the origin by bounded inputs can be constrained by an ellipsoid-like set if a group of linear matrix inequalities (LMIs) is feasible. Then, the free-connection weighting matrix technique is utilized to handle the case of partially known transition probabilities. As byproducts, some sufficient conditions are also obtained to guarantee the stochastic stability of the concerned NNs. The validity of the theoretical analysis is confirmed by numerical simulations.

Necessary and Sufficient Conditions of Formation-Containment Control of High-Order Multiagent Systems With Observer-Type Protocols.

The analysis and design problems of formation-containment control for high-order linear time-invariant (LTI) multiagent systems (MASs) on directed graphs with observer-based output-feedback protocols are given in this work. To expand the feasibility of state formation configuration, two well-structured compensation signals are introduced for the leaders and followers in the protocols, respectively. Benefitting from the compensation signal of followers, the decoupling between formation control of leaders and containment control of followers is achieved. Thus, a necessary and sufficient condition is first established such that the formation-containment control for high-order LTI MASs can be achieved. Moreover, a heuristic iterative algorithm is developed to compute the controller gains, observer gains, as well as the compensation signals. Finally, two numerical examples are implemented to illustrate the time-varying formation-containment control of high-order MASs, which shows the validity and practicability of the theoretical results and algorithm.

[Research progress on chemical constituents from Chloranthus plants and their biological activities].

The genus Chloranthus has 13 species and 5 varieties in China, which can be found in the southwest and northeast regions. Phytochemical studies on Chloranthus plants have reported a large amount of terpenoids, such as diterpenoids, sesquiterpenoids, and sesquiterpenoid dimers. Their anti-inflammation, anti-tumor, antifungal, antivirus, and neuroprotection activities have been confirmed by previous pharmacological research. Herein, research on the chemical constituents from Chloranthus plants and their biological activities over the five years was summarized to provide scientific basis for the further development and utilization of Chloranthus plants.

Observer-based stabilization for switched positive system with mode-dependent average dwell time.

This paper concerns the exponential stabilization problem for a class of switched positive systems. The switching signal satisfies mode-dependent average dwell time (MDADT) and the state variables are partially unmeasurable. A further explanation of mode-dependent average dwell time is included. By employing a set of quasi-time variables, which is first proposed for switched systems with MDADT, new stability results are obtained for the switched nonlinear systems and the underlying linear systems. Observer-based stabilization controllers, both for single-input case and multi-input case, are designed such that the closed-loop system converges exponentially. The designed observers and controllers are both mode-dependent and quasi-time-dependent, which is proved to be less conservative than the ones only mode-dependent. A simplified design strategy is provided to reduce the computation burden. Illustrative examples are provided to demonstrate the effectiveness of the results.

Dissipativity analysis for discrete singular systems with time-varying delay.

In this paper, the issue of dissipativity analysis for discrete singular systems with time-varying delay is investigated. By using a recently developed inequality, which is less conservative than the Jensen inequality, and the improved reciprocally convex combination approach, sufficient criteria are established to guarantee the admissibility and dissipativity of the considered system. Moreover, H∞ performance characterization and passivity analysis are carried out. Numerical examples are presented to illustrate the effectiveness of the proposed method.

On Extended Dissipativity of Discrete-Time Neural Networks With Time Delay.

In this brief, the problem of extended dissipativity analysis for discrete-time neural networks with time-varying delay is investigated. The definition of extended dissipativity of discrete-time neural networks is proposed, which unifies several performance measures, such as the H∞ performance, passivity, l2 - l∞ performance, and dissipativity. By introducing a triple-summable term in Lyapunov function, the reciprocally convex approach is utilized to bound the forward difference of the triple-summable term and then the extended dissipativity criterion for discrete-time neural networks with time-varying delay is established. The derived condition guarantees not only the extended dissipativity but also the stability of the neural networks. Two numerical examples are given to demonstrate the reduced conservatism and effectiveness of the obtained results.

Stability and synchronization of discrete-time neural networks with switching parameters and time-varying delays.

This paper is concerned with the problems of exponential stability analysis and synchronization of discrete-time switched delayed neural networks. Using the average dwell time approach together with the piecewise Lyapunov function technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with time-delays. Benefitting from the delay partitioning method and the free-weighting matrix technique, the conservatism of the obtained results is reduced. In addition, the decay estimates are explicitly given and the synchronization problem is solved. The results reported in this paper not only depend upon the delay, but also depend upon the partitioning, which aims at reducing the conservatism. Numerical examples are presented to demonstrate the usefulness of the derived theoretical results.

Stability and dissipativity analysis of distributed delay cellular neural networks.

In this brief, the problems of delay-dependent stability analysis and strict (Q,S,ℜ)-α-dissipativity analysis are investigated for cellular neural networks (CNNs) with distributed delay. First, by introducing an integral partitioning technique, two new forms of Lyapunov-Krasovskii functionals are constructed, and improved distributed delay-dependent stability conditions are established in terms of linear matrix inequalities. Based on this criterion, a new sufficient delay and α-dependent condition is given to guarantee that the CNNs with distributed delay are strictly (Q,S,ℜ)-α-dissipative. The results developed in this brief can tolerate larger allowable delay than existing ones in the literature, which is demonstrated by several examples.

Exponential stability analysis for delayed neural networks with switching parameters: average dwell time approach.

This paper is concerned with the problem of exponential stability analysis of continuous-time switched delayed neural networks. By using the average dwell time approach together with the piecewise Lyapunov function technique and by combining a novel Lyapunov-Krasovskii functional, which benefits from the delay partitioning method, with the free-weighting matrix technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with constant and time-varying delays, respectively. Moreover, the decay estimates are explicitly given. The results reported in this paper not only depend upon the delay but also depend upon the partitioning, which aims at reducing the conservatism. Numerical examples are presented to demonstrate the usefulness of the derived theoretical results.