This work addresses the output reachable set-based leader-following consensus problem, focusing on a group of positive agents over directed dwell-time switching networks. Two types of non-negative disturbances, namely, 1) L1 -norm bounded disturbances and 2) L∞,1 -norm bounded disturbances are studied. Meanwhile, a class of directed dwell-time switching networks for modeling the communication protocol

In this article, the problem of integral sliding mode control (ISMC) for a class of nonlinear systems with stochastic characteristics under cyber-attack is investigated. The control system and the cyber-attack are modeled as an It ∧o -type stochastic differential equation. The stochastic nonlinear systems are approached by the Takagi-Sugeno fuzzy model. A dynamic ISMC scheme is applied and the states

This article examines the mechanisms by which aperiodic denial-of-service (DoS) attacks can exploit vulnerabilities in the TCP/IP transport protocol and its three-way handshake during communication data transmission to hack and cause data loss in networked control systems (NCSs). Such data loss caused by DoS attacks can eventually lead to system performance degradation and impose network resource constraints

We consider solving distributed constrained optimization in this article. To avoid projection operations due to constraints in the scenario with large-scale variable dimensions, we propose distributed projection-free dynamics by employing the Frank-Wolfe method, also known as the conditional gradient. Technically, we find a feasible descent direction by solving an alternative linear suboptimization

This article studies the stability problem of discrete-time switched positive linear systems (SPLSs) with marginally stable subsystems. Based on the weak common linear copositive Lyapunov function (weak CLCLF) approach, the switching property and the state component property are combined to ensure the asymptotic stability of SPLSs under three types of switching signals. First, considering the transfer-restricted

This article presents a novel self-healing fault accommodation framework for high-order fully actuated systems (HOFASs) with sensor faults. Starting from the HOFAS model with nonlinear measurements, a q -redundant observation proposition is derived from an observability normal form based on each individual measurement. On the heels of the ultimately uniformly bounded error dynamics, a definition of

This article is concerned with the security problems for networked Takagi-Sugeno (T-S) fuzzy systems with asynchronous premise constraints. The primary objective of this article is twofold. First, a novel important-data-based (IDB) denial-of-service (DoS) attack mechanism is proposed from the perspective of the adversary for the first time to reinforce the destructive effect of the DoS attacks. Different

This article proposes an optimal controller for a team of underactuated quadrotors with multiple active leaders in containment control tasks. The quadrotor dynamics are underactuated, nonlinear, uncertain, and subject to external disturbances. The active team leaders have control inputs to enhance the maneuverability of the containment system. The proposed controller consists of a position control

High-performance learning-based control for the typical safety-critical autonomous vehicles invariably requires that the full-state variables are constrained within the safety region even during the learning process. To solve this technically critical and challenging problem, this work proposes an adaptive safe reinforcement learning (RL) algorithm that invokes innovative safety-related RL methods

A slow time-delay assumption restricts the application of control approaches for numerous systems which are constantly affected by multiple uncertainties, including parameters, control coefficients, and the asymmetric dead-zone input. This work presents a new adaptive method for a class of high-order nonlinear delayed systems by removing the so-called slow time-delay assumption and multiple uncertainties

In this article, a fully distributed event-triggered protocol is proposed to solve the consensus problem of uncertain Euler-Lagrange (EL) multiagent systems (MASs) under jointly connected digraphs. First, distributed event-based reference generators are proposed to generate continuously differentiable reference signals via event-based communication under jointly connected digraphs. Unlike some existing

When applied to the consensus tracking of repetitive leader-follower multiagent systems (MASs), most of existing distributed iterative learning control (DILC) methods assume that the dynamics of agents are exactly known or up to the affine form. In this article, we study a more general case where the dynamics of agents are unknown, nonlinear, nonaffine, and heterogeneous, and the communication topologies

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)

This article investigates the optimal bipartite consensus control (OBCC) problem for unknown second-order discrete-time multiagent systems (MASs). First, the coopetition network is constructed to describe the cooperative and competitive relationships between agents, and the OBCC problem is proposed by the tracking error and related performance index function. Based on the distributed policy gradient

The present paper considers the model-based and data-driven control of unknown discrete-time linear systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based

With the development of the economy, distributed manufacturing has gradually become the mainstream production mode. This work aims to solve the energy-efficient distributed flexible job shop scheduling problem (EDFJSP) while simultaneously minimizing makespan and energy consumption. Some gaps are stated following: 1) the previous works usually adopt the memetic algorithm (MA) with variable neighborhood

Recent problems in robotics can sometimes only be tackled using machine learning technologies, particularly those that utilize deep learning (DL) with transfer learning. Transfer learning takes advantage of pretrained models, which are later fine-tuned using smaller task-specific datasets. The fine-tuned models must be robust against changes in environmental factors such as illumination since, often

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

This study focuses on building an intelligent decision-making attention mechanism in which the channel relationship and conduct feature maps among specific deep Dense ConvNet blocks are connected to each other. Thus, develop a novel freezing network with a pyramid spatial channel attention mechanism (FPSC-Net) in deep modeling. This model studies how specific design choices in the large-scale data-driven

This article proposes adaptive internal model controls for the collocated output regulation of a flexible wing, where distributed disturbances, boundary disturbances, and references are from an exactly unknown exosystem. Observer-based tracking error feedback controls are first designed to address the robust output regulation in case of a known exosystem matrix. If the exosystem has an unknown matrix

This article is considered on underactuated fractional-order stochastic systems (FOSSs) with actuator saturation and incrementally conic nonlinear terms, whose fractional-order α ∈ (0,1). First, to bring FO dynamic signals, solving the unmodeled dynamics, in the meantime, the saturated nonlinear term of the control input is taken into account. At the time, to cope with the stability issue of FOSS under

Continually capturing novel concepts without forgetting is one of the most critical functions sought for in artificial intelligence systems. However, even the most advanced deep learning networks are prone to quickly forgetting previously learned knowledge after training with new data. The proposed lifelong dual generative adversarial networks (LD-GANs) consist of two generative adversarial networks

Coverage path planning (CPP) is essential for robotic tasks, such as environmental monitoring and terrain surveying, which require covering all surface areas of interest. As the pioneering approach to CPP, inspired by the concept of predation risk in predator-prey relations, the predator-prey CPP (PPCPP) has the benefit of adaptively covering arbitrary bent 2-D manifolds and can handle unexpected changes

Multiobjective multitasking optimization (MTO) needs to solve a set of multiobjective optimization problems simultaneously, and tries to speed up their solution by transferring useful search experiences across tasks. However, the quality of transfer solutions will significantly impact the transfer effect, which may even deteriorate the optimization performance with an improper selection of transfer

Major challenges of controlling human–robot collaboration (HRC)-oriented modular robot manipulators (MRMs) include the estimation of human motion intention while cooperating with a robot and performance optimization. This article proposes a cooperative game-based approximate optimal control method of MRMs for HRC tasks. A harmonic drive compliance model-based human motion intention estimation method

Evolutionary multitask optimization is an emerging research topic that aims to solve multiple tasks simultaneously. A general challenge in solving multitask optimization problems (MTOPs) is how to effectively transfer common knowledge between/among tasks. However, knowledge transfer in existing algorithms generally has two limitations. First, knowledge is only transferred between the aligned dimensions

This article explores the model-free remote control problem in a wireless networked cyber–physical system (CPS) composed of spatially distributed sensors, controllers, and actuators. The sensors sample the states of the controlled system to generate control instructions at the remote controller, while the actuators maintain the system’s stability by executing control commands. To realize the control

Multitask image clustering approaches intend to improve the model accuracy on each task by exploring the relationships of multiple related image clustering tasks. However, most existing multitask clustering (MTC) approaches isolate the representation abstraction from the downstream clustering procedure, which makes the MTC models unable to perform unified optimization. In addition, the existing MTC

This article proposes an adaptive fault-tolerant control (AFTC) approach based on a fixed-time sliding mode for suppressing vibrations of an uncertain, stand-alone tall building-like structure (STABLS). The method incorporates adaptive improved radial basis function neural networks (RBFNNs) within the broad learning system (BLS) to estimate model uncertainty and uses an adaptive fixed-time sliding

It is technically challenging to maintain stable tracking for multiple-input-multiple-output (MIMO) nonlinear systems with modeling uncertainties and actuation faults. The underlying problem becomes even more difficult if zero tracking error with guaranteed performance is pursued. In this work, by integrating filtered variables into the design process, we develop a neuroadaptive proportional-integral

Active pantograph control is the most promising technique for reducing contact force (CF) fluctuation and improving the train's current collection quality. Existing solutions, however, suffer from two significant limitations: 1) they are incapable of dealing with the various pantograph types, catenary line operating conditions, changing operating speeds, and contingencies well and 2) it is challenging

This article presents a new perspective from control theory to interpret and solve the instability and mode collapse problems of generative adversarial networks (GANs). The dynamics of GANs are parameterized in the function space and control directed methods are applied to investigate GANs. First, the linear control theory is utilized to analyze and understand GANs. It is proved that the stability

This article addresses the practically predefined-time adaptive fuzzy tracking control problem of strict-feedback nonlinear stochastic systems, where the system under consideration includes stochastic disturbances and uncertain parameters. First, in this study, practically predefined-time stochastic stabilization (PPSS) in the p th moment sense is introduced, and a Lyapunov-type criterion for PPSS

This article proposes an observer-based event-driven fault-tolerant (OBEDFT) secondary control strategy for AC microgrids (MGs) to achieve load voltage regulation. First, the input-output feedback linearization method transforms the voltage regulation issue into an output feedback tracking problem for linear multiagent systems (MASs) with nonlinear dynamics. This transformation provides the necessary

Binary hashing is an effective approach for content-based image retrieval, and learning binary codes with neural networks has attracted increasing attention in recent years. However, the training of hashing neural networks is difficult due to the binary constraint on hash codes. In addition, neural networks are easily affected by input data with small perturbations. Therefore, a sensitive binary hashing

The convergence rate and applicability to directed graphs with interaction topologies are two important features for practical applications of distributed optimization algorithms. In this article, a new kind of fast distributed discrete-time algorithms is developed for solving convex optimization problems with closed convex set constraints over directed interaction networks. Under the gradient tracking

The multifactorial evolutionary algorithm (MFEA) is one of the most widely used evolutionary multitasking (EMT) algorithms. The MFEA implements knowledge transfer among optimization tasks via crossover and mutation operators and it obtains high-quality solutions more efficiently than single-task evolutionary algorithms. Despite the effectiveness of MFEA in solving difficult optimization problems, there

This article investigates the event-triggered impulsive control (ETIC) problem for a class of nonlinear time-delay systems subject to exogenous disturbances. An original event-triggered mechanism (ETM) which utilizes the information of system state and external input is constructed based on Lyapunov function approach. To achieve the input-to-state stability (ISS) of the considered system, some sufficient

This article investigates the distributed joint state and fault estimation issue for a class of nonlinear time-varying systems over sensor networks constrained by energy harvesting. It is assumed that data transmission between sensors requires energy consumption, and each sensor can harvest energy from the external environment. A Poisson process models the energy harvested by each sensor, and the sensor's

Spiking neural P (SNP) systems are a class of distributed and parallel neural-like computing models that are inspired by the mechanism of spiking neurons and are 3rd-generation neural networks. Chaotic time series forecasting is one of the most challenging problems for machine learning models. To address this challenge, we first propose a nonlinear version of SNP systems, called nonlinear SNP systems

The widespread application of learning-based methods in robotics has allowed significant simplifications to controller design and parameter adjustment. In this article, robot motion is controlled with learning-based methods. A control policy using a broad learning system (BLS) for robot point-reaching motion is developed. A sample application based on a magnetic small-scale robotic system is designed

We introduce a new class of fuzzy discrete event systems (FDESs) called stochastic FDESs (SFDESs), which is significantly different from the probabilistic FDESs (PFDESs) in the literature. It offers an effective modeling framework for applications that are unsuitable for the PFDES framework. An SFDES is comprised of multiple fuzzy automata that occur randomly one at time with different occurrence probabilities

Software-defined networking (SDN) allows flexible and centralized control in cloud data centers. An elastic set of distributed SDN controllers is often required to provide sufficient yet cost-effective processing capacity. However, this introduces a new challenge: Request Dispatching among the controllers by SDN switches. It is essential to design a dispatching policy for each switch to guide the request

In the research of multi-image encryption (MIE), the image type and size are important factors that limit the algorithm design. For this reason, the multi-image (MI) hybrid encryption algorithm that can flexibly encrypt color images and grayscale images of various sizes is proposed. Based on this, combining the back propagation (BP) neural network compression technology and the MI hybrid encryption

Classification is a fundamental task in the field of data mining. Unfortunately, high-dimensional data often degrade the performance of classification. To solve this problem, dimensionality reduction is usually adopted as an essential preprocessing technique, which can be divided into feature extraction and feature selection. Due to the ability to obtain category discrimination, linear discriminant

Aiming at simplifying the network structure of broad learning system (BLS), this article proposes a novel simplification method called compact BLS (CBLS). Groups of nodes play an important role in the modeling process of BLS, and it means that there may be a correlation between nodes. The proposed CBLS not only focuses on the compactness of network structure but also pays closer attention to the correlation

This article investigates the discontinuous adaptive impulsive control of uncertain linear and nonlinear systems with stochastic perturbations and actuator saturation. Existing literature on adaptive impulsive control schemes adopt continuous state information in designing the continuous adaptive law, which loses the advantages of impulsive control completely. In this article, the discontinuous adaptive

Engine calibration problems are black-box optimization problems which are evaluation costly and most of them are constrained in the objective space. In these problems, decision variables may have different impacts on objectives and constraints, which could be detected by sensitivity analysis. Most existing surrogate-assisted evolutionary algorithms do not analyze variable sensitivity, thus, useless

Neural architecture search (NAS) can automatically design architectures for deep neural networks (DNNs) and has become one of the hottest research topics in the current machine learning community. However, NAS is often computationally expensive because a large number of DNNs require to be trained for obtaining performance during the search process. Performance predictors can greatly alleviate the prohibitive

The relational triplet is a format to represent relational facts in the real world, which consists of two entities and a semantic relation between these two entities. Since the relational triplet is the essential component in a knowledge graph (KG), extracting relational triplets from unstructured texts is vital for KG construction and has attached increasing research interest in recent years. In this

This article aims to solve a hierarchical multi-UAV Dubins traveling salesman problem (HMDTSP). Optimal hierarchical coverage and multi-UAV collaboration are achieved by the proposed approaches in a 3-D complex obstacle environment. A multi-UAV multilayer projection clustering (MMPC) algorithm is presented to reduce the cumulative distance from multilayer targets to corresponding cluster centers. A

This research paper addresses the problem of achieving differentially private average consensus for multiagent systems (MASs) consisting of positive agents. A novel randomized mechanism is introduced that employs nondecaying positive multiplicative truncated Gaussian noises to maintain the positivity and randomness of the state information over time. A time-varying controller is developed to achieve

This article addresses the problem of containment control for continuous-time multiagent systems. A containment error is first given to show the coordination between the outputs of leaders and followers. Then, an observer is designed based on the neighbor observable convex hull state. Under the assumption that the designed reduced-order observer is subject to external disturbances, a reduced-order

In this article, the sliding mode control (SMC) problem is addressed for two-dimensional (2-D) systems depicted by the second Fornasini-Marchesini (FMII) model. The communication from the controller to actuators is scheduled via a stochastic protocol modeled as Markov chain, by which only one controller node is permitted to transmit its data at each instant. A compensator for other unavailable controller

Optimal control methods have gained significant attention due to their promising performance in nonlinear systems. In general, an optimal control method is regarded as an optimization process for solving the optimal control laws. However, for uncertain nonlinear systems with complex optimization objectives, the solving of optimal reference trajectories is difficult and significant that might be ignored

This article explores a guaranteed network connectivity problem during moving obstacle avoidance within a distributed formation tracking framework for uncertain nonlinear multiagent systems with range constraints. We investigate this problem based on a new adaptive distributed design using nonlinear errors and auxiliary signals. Within the detection range, each agent regards other agents and static

This technical paper utilizes the Lyapunov theory to characterize the event-triggered set stabilizability of Markovian jump logical control networks (MJLCNs). Whereas the existing result for checking the set stabilizability of MJLCNs is only sufficient, this technical paper further establishes its necessary and sufficient condition. First, the Lyapunov function is established to describe the set stabilizability

Articulating crane (AC) is used in various industrial activities. The articulated multisection arm exacerbates nonlinearities and uncertainties, making the precise tracking control challenging. This study proposes an adaptive prescribed performance tracking control (APPTC) for AC to robustly fulfill the task of precise tracking control, with adaptation to resist time-variant uncertainties, whose bounds

This article proposes a switching anti-windup strategy for linear, time-invariant (LTI) systems subject to asymmetric actuator saturation and L2 -disturbances, the core idea behind which is to make full use of the available range of control input space by switching among multiple anti-windup gains. The asymmetrically saturated LTI system is converted to a switched system with symmetrically saturated

This article addresses the resilient practical cooperative output regulation problem (RPCORP) for multiagent systems subjected to both denial-of-service (DoS) attacks and actuator faults. Fundamentally different from the existing solutions to RPCORPs, the system parameters considered in this article are unknown to each agent, and a novel data-driven control approach is introduced to handle such an