Neuroadaptive Output-Feedback Tracking Control for Stochastic Nonlower Triangular Nonlinear Systems With Dead-Zone Input.

For stochastic nonlower triangular nonlinear systems subject to dead-zone input, a neuroadaptive tracking control frame is constructed by applying the dynamic surface technique with a state observer in this work. Its primary contribution lies in extending the stability criteria to encompass stochastic nonlinear systems characterized by nonlower triangular structures and unmeasured states. The control strategy is delineated as follows.

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.

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.

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.

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.

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.

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.

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.

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.

[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.

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).

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.

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.

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.

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.

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.

Improved Stability Condition for Takagi-Sugeno Fuzzy Systems With Time-Varying Delay.

In this paper, the stability analysis problem of Takagi-Sugeno fuzzy systems with time-varying delay is investigated. By utilizing the Wirtinger-based integral inequality and the improved reciprocally convex combination technique, an improved stability condition is derived in terms of linear matrix inequalities. A numerical example is given to demonstrate the efficiency of the obtained result.

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.

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.

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.

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.