In cooperative Multi-Agent Reinforcement Learning (MARL), efficient exploration is crucial for optimizing the performance of joint policy. However, existing methods often update joint policies via independent agent exploration, without coordination among agents, which inherently constrains the expressive capacity and exploration of joint policies. To address this issue, we propose a conductor-based joint policy framework that directly enhances the expressive capacity of joint policies and coordinates exploration. In addition, we develop a Hierarchical Conductor-based Policy Optimization (HCPO) algorithm that instructs policy updates for the conductor and agents in a direction aligned with performance improvement. A rigorous theoretical guarantee further establishes the monotonicity of the joint policy optimization process. By deploying local conductors, HCPO retains centralized training benefits while eliminating inter-agent communication during execution. Finally, we evaluate HCPO on three challenging benchmarks: StarCraft II Multi-agent Challenge, Multi-agent MuJoCo, and Multi-agent Particle Environment. The results indicate that HCPO outperforms competitive MARL baselines regarding cooperative efficiency and stability.

With increasing penetration of Electric Vehicles (EVs) into the transportation system and smart electricity grid, there is a growing need for integrating them into Home Energy Management Systems (HEMS). This integration within HEMS introduces dynamic user behaviors and time-varying charging demand, thus posing challenges for the HEMS. To mitigate these challenges, this paper proposes a charging model for heterogeneous EVs that covers the range of Plug-in Hybrid EVs (PHEVs), Range-Extender EVs (REEVs) and Battery EVs (BEVs) with/without heat pumps. The proposed heterogeneous EV charging model considers weather conditions, estimated mileage and driver’s experience to describe the dynamic charging demand and the anxiety level influencing their behavior. To optimize the HEMS operation, minimizing the energy cost and ensuring comfort, this paper introduces an offline Deep Reinforcement Learning (DRL) algorithm which learns directly from pre-collected datasets, avoiding the cost and safety issues associated with continuous real-world interactions. The algorithm incorporates the Huber loss and a Q-quantile estimator to mitigate performance degradation from dataset anomalies such as data noise, sensor failure and human error, resulting in more robust HEMS optimization strategies. Experimental results demonstrate the method’s effectiveness in reducing total costs and analyze the performance of household devices with two different electricity rates.

Recent approaches attempt to adapt powerful interactive segmentation models, such as SAM, to interactive matting and fine-tune the models based on synthetic matting datasets. However, models trained on synthetic data fail to generalize to complex and occlusion scenes. We address this challenge by proposing a new matting dataset based on the COCO dataset, namely COCO-Matting. It selects real-world complex images from COCO and converts semantic segmentation masks to matting labels. The built COCO-Matting comprises an extensive collection of 36,980 human instance-level alpha mattes in complex natural scenarios. Furthermore, existing SAM-based matting methods extract intermediate features and masks from a frozen SAM and only train a lightweight matting decoder by end-to-end matting losses, which do not fully exploit the potential of the pre-trained SAM. Thus, we propose SEMat which revamps the network architecture and training objectives. For network architecture, the proposed feature-aligned transformer learns to extract fine-grained edge and transparency features. The proposed matte-aligned decoder aims to segment matting-specific objects and convert coarse masks into high-precision mattes. For training objectives, the proposed regularization and trimap loss aim to retain the prior from the pre-trained model and push the matting logits extracted from the mask decoder to contain trimap-based semantic information. Extensive experiments across seven diverse datasets demonstrate the superior performance of our method, proving its efficacy in interactive natural image matting. Code is available at https://github.com/XiaRho/SEMat.

This article investigates reach-avoid games involving defenders equipped with capture radii, where both defenders and attackers have different speeds. The main challenge lies in using geometric methods to analyze different speed ratios, construct barriers, or defensive advantage angles, and divide the state space into defensive and offensive advantage regions. This article proposes optimal analytical strategies for players based on the corresponding payoff functions, depending on the attacker's position within different winning regions under various speed ratios. In addition, we demonstrate the existence of a unique optimal target point within the offensive advantage region. Unlike numerical methods, which are limited by computational complexity and real-time application capabilities, the proposed method allows for the precise calculation of barriers and real-time updates in nonpoint capture scenarios. Finally, simulation results validate the effectiveness of the constructed barriers in multiplayer reach-avoid games.

Time-series forecasting is considered a critical task with extensive applications across diverse domains. However, effectively capturing both cross-dimension and cross-time dependencies in nonstationary time series remains a significant challenge, particularly due to the confounding effects of environmental factors. These factors often introduce spurious correlations that obscure the learning of meaningful temporal features. In this article, the novel framework Caformer (Causal Transformer) is proposed for time-series forecasting grounded in causal reasoning, the science of identifying causality. The framework consists of four key modules: the dynamic learner, environment learner, temporal learner, and decompose learner. The dynamic learner uncovers dynamic interactions among features to model cross-dimension dependencies, while the temporal learner infers cross-time dependencies under causal constraints. The environment learner, together with the decompose learner, extracts environmental factors and applies a backdoor adjustment to mitigate the confounding effects on the time series. Extensive experiments demonstrate that Caformer achieves the state-of-the-art performance in both long-term and short-term forecasting. It achieves up to a 26.2% mean square error (mse) reduction on the traffic dataset and 21.8% on the electricity dataset compared to PatchTST, with consistent gains across eight long-term benchmarks. On the M4 dataset, Caformer ranks first across all 15 short-term forecasting categories. In addition to strong predictive accuracy, Caformer provides interpretable insights into the learned dependencies.

In this article, we propose a planning-operation coordinated mitigation scheme for load redistribution (LR) attacks to overcome the deficiencies of separately designed phase shifting transformer-based mitigation strategies. Specifically, the interactions amongst the defender, attacker, and system are formulated as a trilevel optimization, where the deployment of defense devices and phase shift angles can be optimized according to possible operation state. Based on the proposed load similarity metric, a clustering-based approximate solution is designed to reduce the computational complexity caused by the integration of planning and operation stages. Simulation results on the IEEE 14-bus and 30-bus test systems verify the performance of the proposed mitigation scheme and the clustering-based approximate solution method.

Opinion dynamics models that elucidate the evolution and formation of opinions conventionally focus on pairwise interactions within graphs, often overlooking the complex higher-order interactions that arise in real-world social networks, such as online meetings and group chats. In this article, a continuous-time dynamical system is developed to study opinion-forming processes over higher-order networks associated with undirected hypergraphs. The proposed model introduces a novel diffusion-like interaction function to characterize interactions of different orders over hypergraphs. The convergence and stability of the dynamical systems are further examined in both the presence and absence of stubborn individuals. Building on traditional opinion dynamics models and integrating weak-tie theory, we emphasize the critical role of higher-order interactions in shaping individual opinions, enhancing network communication efficiency, and mitigating opinion polarization. Finally, all theoretical results are extensively investigated and empirically validated through numerical experiments on both synthetic and real-world network datasets.

This article addresses the problem of cooperative path following for wheeled mobile robots (WMRs) under system constraints and external bounded disturbances within a switching communication network, by proposing a robust distributed model predictive control (DMPC) strategy. First, the cooperative path-following task is decoupled into two subtasks using a modified virtual structure: a cooperative task involving virtual reference robots and an individual path-following task between each actual robot and its corresponding virtual reference. A time-like path parameter is introduced to generate predefined path information for the virtual reference robot in advance, enabling dynamic formation tracking. Subsequently, discrete-time error dynamics subject to external bounded disturbances are derived for each robot, and a centralized predictive control problem is formulated as a baseline. A nominal DMPC strategy is then developed for the disturbance-free case, followed by an extension to a robust DMPC formulation that accounts for nonzero disturbances. In this context, a stability constraint is incorporated to ensure closed-loop stability without relying on neighboring agents’ real-time information. Theoretical analysis confirms the feasibility of the proposed scheme and guarantees the convergence of system trajectories to a disturbance invariant set. Finally, simulation and experimental results validate the effectiveness of the proposed strategy in cooperative path-following scenarios involving WMRs.

The vulnerabilities of power system state estimation have been widely analyzed recently. However, most of the existing attack model can only pass the bad data detector (BDD) in power system state estimation, where the state-of-the-art neural attack detectors (NADs) are not fully considered. To generate stealthy attack in power system with high penetrated renewable energy sources(RESs), the principle of constructing spatiotemporally stealthy attack is analyzed, where a spatio-temporality principle is proposed to ensure the stealthiness of the attack. A spatiotemporal correlation generation framework is proposed in the improved WGAN-GP framework, which can generate spatiotemporally stealthy false data injection (FDI) attack in power system state estimation deployed with the state-of-the-art NADs. Simulations in the IEEE 14-bus, the IEEE 57-bus and the IEEE 118-bus test systems verify the stealthiness and effectiveness of the proposed spatiotemporally stealthy FDI attack.

In the context of carbon peaking and carbon neutrality, green production scheduling that considers energy has attracted increasing attention. Reinforcement learning (RL) has emerged as a topic for developing efficient algorithms for solving complex combinatorial optimization problems, such as real-world shop scheduling problems. In this paper, we investigate the energy-conscious scheduling problem (ECSP) under time-of-use (TOU) electricity price with the goal of minimizing both the waiting time and extra electricity cost. A mathematical model is formulated, and an efficient deep RL (DRL)-based optimization method is proposed to solve the problem effectively. We design a novel ECSP network (ECSPNet) tailored to handle various ECSP scales based on the characteristics of the problem. Moreover, the Tchebycheff decomposition method is used to solve the multi-objective optimization problems, complemented by the application of the policy gradient method from reinforcement learning to train the ECSPNet without size limitations. Experiments verify that the proposed ECSPNet outperforms state-of-the-art methods and is computationally efficient, even on instances of larger scales unseen in training. Real-world case studies reveal that the proposed method can reduce annual total electricity cost by approximately 30% while effectively maximizing production efficiency. Note to Practitioners —Enhancing energy consciousness alongside improving production efficiency in manufacturing systems has increasingly become a focal point for both academia and industry, especially with the growing emphasis on environmental concerns and advancing industrialization. Time-of-use (TOU) electricity price policy is widely implemented to effectively balance electricity supply and demand. For business managers, appropriately responding to this policy by optimizing scheduling tasks can significantly reduce energy costs. This paper addresses a novel energy-conscious scheduling problem (ECSP) under TOU electricity price, specifically arising from the electrode graphitization production process in graphite material manufacturing. We propose a deep reinforcement learning-based energy-saving scheduling optimization method, which integrates considerations for both production efficiency and energy cost indicators. By combining the unique characteristics of ECSP with the decision-making capabilities of reinforcement learning and the perception capabilities of deep learning, our method excels in solution speed, efficiency, and adaptability. The effectiveness and practical applicability of our method have been demonstrated through experiments on actual enterprise cases of various scales. The rapidly generated optimization scheduling plans can assist enterprises in rationally arranging scheduling tasks while minimizing electricity cost. Looking ahead, our proposed method has the potential to address a wide range of energy-conscious scheduling problems under TOU electricity price policy.

In this paper, the problem of optimal deception attacks on remote state estimation is studied, where an interval χ2detector is set up to verify the validity of the data packets received by the remote state estimator. Malicious attackers are not only able to intercept the original measurements transmitted on the wireless network, but also obtain side information about the system states sensed by an extra sensor. First, By fusing these two types of information, an innovation-based deception attack model with combined information is proposed. Then, we present a novel stealthiness constraint and derive the covariance at the final instant of the attack interval to characterize the attack performance. Furthermore, the closed-form optimal deception attack schemes are obtained by utilizing the Lagrange method. Finally, numerical simulations and experiment results confirm the effectiveness of the proposed attack scheme.

This manuscript considers distributed optimization problems in systems with cooperative-collaborative relationships, involving two groups of nodes, each with its own optimization problem, but with a coupled communication topology. For the signed graph representing the cooperation and collaboration between agents, this manuscript introduces DIG-JOR, a discrete-time distributed algorithm that consists of three key modules: an inexact consensus and gradient descent module, a group gradient-tracking module, and a dynamic Jacobi over-relaxation (JOR) inverse-tracking module. To support the convergence analysis of the distributed optimization algorithm, this manuscript proposes the Multi-Loop Small Gain Theorem. Under the assumption of strong convexity and with appropriately chosen step sizes, it is proved that the DIG-JOR algorithm converges to the optimal solutions of both groups at an R-linear rate. The theoretical results are validated through a simulation example.

This article addresses the event-triggered attitude consensus problem for multiple rigid body systems with prescribed-time convergence. Achieving event-triggered prescribed-time attitude consensus almost globally is challenging in the attitude configuration space which is a non-Euclidean manifold. We design an adaptive event-triggered attitude consensus framework by using the exponential coordinates through the logarithm mapping covering$\mathbb {SO}(3)$almost globally. A novel triggering strategy governed by a dynamic variable is proposed to reduce the triggering numbers and guarantee the convergence performance simultaneously. With the proposed adaptive control framework, we achieve almost global attitude consensus with prescribed-time convergence in the event-triggered sampling setting with undirected graphs. Compared to existing results, the proposed adaptive control framework ensures event-triggered prescribed-time attitude consensus in a fully distributed manner, suggesting that the proposed framework does not rely on the global knowledge of the entire topology. Finally, we conduct numerical simulations to validate our results.

Feedback signal delays present a common challenge in the online decision-making processes of various real-world systems, such as real-time economic dispatch in power systems. These delays, primarily induced by limited computational capabilities or unknown parameter changes, can significantly hinder the performance or even effectiveness of online decision-making strategies. This study investigates distributed optimization over time-varying unbalanced networks with delayed feedback signals. We propose discrete-time distributed online algorithms for both constrained and unconstrained optimization problems. Each node in the network has access only to its individual time-varying local objective function with a delayed time sequence. At each decision-making step, each node selects the appropriate local online behavior, which depends on the information from its neighbor nodes, towards the minimization of global cumulative cost function value. We demonstrate that sublinear regrets can be ensured as long as the time-varying unbalanced communication networks maintain$\mathcal {B}$-strong connectivity. For a general convex or$\mu $-strongly convex local objective function, the network regret and individual regret grow as$\mathcal {O}((\ln (T))^{2})$and$\mathcal {O}(\sqrt {T})$, respectively, and are intrinsically related to the feedback delays and step sizes. To validate the proposed algorithms, numerical studies are carried out on constrained distributed time-varying economic dispatch problems.

Different from continuous-time or discrete-time (CoD-T) systems, time scale-type systems (TTSs) can operate in a continuous-discrete mode, bridging the gap between continuous and discrete states. As a result, conventional methods such as the average impulsive interval (AII) and the impulsive density function (IDF) are not suitable for characterizing the frequency of impulses (FoI) required for stability analysis of TTSs with impulses. This is due to the possibility that all impulses may fall into the complementary set of time scales. In light of this, the number of impulses satisfying AII or IDF may not be sufficient to ensure stability. In response to this challenge, we introduce a novel definition in this paper, termed time scale-type impulsive density (TTID). The key feature of TTID lies in the consideration of the graininess function, which is determined by time scales, allowing for a more accurate characterization of the number of impulses required for the stability of the TTSs. Employing the proposed TTID, the criteria of asymptotical stability are presented for TTSs with stochastic and Markov-type impulses, respectively. The results presented in this paper encompass and generalize the discoveries of some existing CoD-T impulsive systems, which can be regarded as special cases of our results. We shed new light on the advantages of the TTID and the effectiveness of the theoretical results by an application of voltage control for micro-grids.

The event-triggered almost global attitude consensus problem is considered in this article for multiple rigid body systems with prescribed performance. Two kinds of attitude consensus protocols using axis-angle vectors are proposed at the kinematic level with different prescribed performance constraints. The first protocol aims to achieve the event-triggered attitude consensus almost globally under jointly connected graphs. Based on a prescribed performance function with local states, the configuration space of parameterized attitude representations is shown to be positively invariant which almost globally covers . The second protocol is designed to reach attitude consensus with the prescribed transient behavior guaranteed in the event-triggered setting. By defining a prescribed performance function using the metric on axis-angle spaces, a dynamic event-triggered framework is designed to ensure both the attitude geometric topology constraint and prescribed convergence performance. Finally, numerical results are given to show the validness of the two control protocols.

This article tackles the sampled-data control issue for a class of time-scale-type systems (TSTSs) subject to denial-of-service (DoS) attacks. A novel sampled-data control protocol, that incorporate the backward-jump-like operator (BJLO), is proposed to ensure compatibility with the discontinuity of time scales. Furthermore, a generalized Halanay-like inequality (GHLI) is proposed to address the effects of time scale discontinuities and DoS attacks on sampling intervals. Compared with the common Halanay inequality (CHI) used in continuous-time sampled-data systems, the GHLI accommodates TSTSs and permits some sampling intervals that exceed the constraints of the CHI. By leveraging the GHLI and the proposed sampled-data control protocol, the exponential stability criterion is derived for TSTSs under DoS attacks. This article culminates with two simulation examples and the micro-grid case study conducted to validate the proposed results.

Large-scale overlapping problems are prevalent in practical engineering applications, and the optimization challenge is significantly amplified due to the existence of shared variables. Decomposition-based cooperative coevolution (CC) algorithms have demonstrated promising performance in addressing large-scale overlapping problems. However, current CC frameworks designed for overlapping problems rely on grouping methods for the identification of overlapping problem structures and the current grouping methods for large-scale overlapping problems fail to consider both accuracy and efficiency simultaneously. In this article, we propose a two-stage enhanced grouping method for large-scale overlapping problems, called OEDG, which achieves accurate grouping while significantly reducing computational resource consumption. In the first stage, OEDG employs a grouping method based on the finite differences principle to identify all subcomponents and shared variables. In the second stage, we propose two grouping refinement methods, called subcomponent union detection (SUD) and subcomponent detection (SD), to enhance and refine the grouping results. SUD examines the information of the subcomponents and shared variables obtained in the previous stage, and SD corrects inaccurate grouping results. To better verify the performance of the proposed OEDG, we propose a series of novel benchmarks that consider various properties of large-scale overlapping problems, including the topology structure, overlapping degree, and separability. Extensive experimental results demonstrate that OEDG is capable of accurately grouping different types of large-scale overlapping problems while consuming fewer computational resources. Finally, we empirically verify that the proposed OEDG can effectively improve the optimization performance of diverse large-scale overlapping problems.

Industrial time-series prediction is crucial for Industrial Internet of Things. Due to the complexity and variation of modern industry, knowledge transfer for varying data has been an attractive research area. However, conventional methods may overlook the intradomain distribution and the mutual information, leading to incorrect semantic alignment and loss of prediction-relevant information. To address these issues, a contrastive learning-based domain adaptation method, contrastive temporal prediction adaptation, for industrial time-series cross-domain prediction is proposed. It leverages a contrastive domain generalization and a contrastive self-supervised alignment method to obtain stable representations and capture the relationship between the data distribution and labels, to bring samples with similar labels closer in the feature space. Besides, an instancewise adversarial discrimination is developed to leverage the data distribution to mitigates interference from irrelevant information. The performance of our method is verified through experiments on CMAPSS dataset. The results demonstrate that our method outperforms existing methods.

The increasing penetration of distributed energy resources has prompted distribution system operators (DSOs) at the retail electricity market level to coordinate with the independent system operator (ISO) at the wholesale market level, for greater benefits. However, interaction mechanisms between the ISO and DSOs, and impacts of prices and power injections, have not been adequately investigated in literature. This article proposes a distributed coordination framework for the ISO and DSOs across wholesale-retail (bi-level) electricity markets, considering their interactions more fairly. Moreover, to mitigate the challenges arising from the interdependence between the ISO and heterogeneous DSOs, a coupled training mechanism based on the response model is devised. This mechanism iteratively trains the ISO and DSOs by solely exchanging prices and power injections, ensuring the demand–supply balance at both retail and wholesale levels. In addition, a deep reinforcement learning algorithm is introduced for the three-stage iterative training process of heterogeneous agents. Results demonstrate the effectiveness of the proposed method and its advantages in terms of lowering energy prices, clearing of cheaper clean resources and thus, improving overall market efficiency.

Existing domain adaptation semantic segmentation (DASS) methods under adverse conditions often depend on pseudo-labels for network training. However, these pseudo-labels are frequently plagued by noise and bias toward high-confidence predictions, thereby impeding the enhancement of segmentation performance. This article tackles the above challenge by proposing a novel boundary-based active domain adaptation (ADA) framework, which efficiently selects both informative low-confidence samples and high-confident but misclassified samples to be labeled while maximizing the segmentation performance under a limited annotation budget. For the evaluation of sample confidence and informativeness, we first propose ranking weighted feature space impurity (RWFSI) metric to quantify category distribution among a sample's nearest neighbors within the feature space and consider the samples with higher RWFSI values as low-confidence samples around the decision boundary, which can also alleviate the category imbalance of active labels. Subsequently, we apply Gaussian mixture models (GMMs) to model the distribution across source and target domains. Using the spatial arrangement of each GMM component, we define the intraclass domain shift score (ICDSS), which identifies samples with high ICDSS values as those more likely to be high-confidence but misclassified, aiding in refining sample selection. Extensive experiments demonstrate that our method is superior to the existing state-of-the-art domain adaptation and active learning (AL) methods and comparable with those of full supervision. The code will be released at https://github.com/1061018609/BADA.

With the remarkable success of generative models like ChatGPT, artificial intelligence generated content (AIGC) is undergoing explosive development. Not limited to text and images, generative models can generate industrial time series data, addressing challenges, such as the difficulty of data collection and data annotation. Due to their outstanding generation ability, they have been widely used in Internet of Things, metaverse, and CPSS to enhance the efficiency of industrial production. In this article, we present a comprehensive overview of generative models for industrial time series from deep-generative models (DGMs) to large-generative models (LGMs). First, a DGM-based AIGC framework is proposed for industrial time series generation. Within this framework, we survey advanced industrial DGMs and present a multiperspective categorization. Then, we systematically propose the roadmap to construct industrial LGMs from four aspects: large-scale industrial dataset, LGMs architecture for complex industrial characteristics, self-supervised training for industrial time series, and fine-tuning of industrial downstream tasks. Furthermore, we introduce an evaluation benchmark that systematically assesses fidelity, diversity, and utility. We include a case study on aircraft engine maintenance, demonstrating the application of DGMs in industrial predictive maintenance. Finally, we conclude the challenges and future directions to enable the development of generative models in industry.

This article investigates the almost sure consensus (ASC) problem for sampled-data multiagent systems (MASs) operating over semi-Markov switching networks (SMSNs) and facing different types of denial-of-service (DoS) attacks. During real-time information exchange among agents, communication failures between agents occur randomly, which may result in each possible network topology occurring with a certain probability, and its sojourn time is also stochastic. This necessitates the consideration of a more general switching signal to describe the stochastic switching phenomenon of networks. In pursuit of this goal, a semi-Markov chain is introduced to characterize the switching signal of stochastic interaction networks, whose sojourn time distribution allows for arbitrary continuous-time distribution and depends on the current and next state. Additionally, this article delves into the impact of two distinct types of DoS attacks on MASs. The first type involves random DoS attacks, which are also modeled by a semi-Markov chain to capture the stochastic nature of attack durations. The second type is deterministic DoS attacks, characterized by their frequency and duration. The proposed new stochastic analysis method, based on the law of large numbers, is used to analyze the ASC for MASs featuring SMSNs under the DoS attacks. The effectiveness of the proposed approach is demonstrated by evaluating the results obtained from two illustrative numerical examples.

Unpaired multi-domain video-to-video translation is an attractive solution for diverse video translation, which has to deal with not only unpaired data but also spatio-temporal inconsistency. Most current video-to-video translation models based on cycle consistency introduce optical flow as motion information to achieve spatio-temporal consistency. However, the warping of the optical flow generates a meaningless invisible regions outside the field of view, which is produced by stretching the edge area of the original image and negatively affects the model training. In this work, we propose the Contrastive learning for Multi-domain Video-to-video Translation to replace the cycle consistency, in order to avoid the affects of invisible regions. Specifically, we first introduce synthetic optical flow to maintain spatio-temporal consistency. Then, we use the attention mechanism as the selection principle of positive and negative, and eliminate the features of invisible regions by sorting the feature entropy. Frequency domain information is also used to maintain individual consistency. Experiments on the public datasets Viper and INIT show that our methods is universal across multiple datasets and achieves state-of-the-art performance in generating temporally consistent multi-domain videos.

Due to the challenge posed by multi-source and heterogeneous data collected from diverse environments, causal relationships among features can exhibit variations influenced by different time spans, regions, or strategies. This diversity makes a single causal model inadequate for accurately representing complex causal relationships in all observational data, a crucial consideration in causal learning. To address this challenge, we introduce the nonlinear Causal Kernel Clustering method designed for heterogeneous subgroup causal learning, illuminating variations in causal relationships across diverse subgroups. It comprises two primary components. First, the construction of a sample mapping function forms the basis of the subsequent nonlinear causal kernel. This function assesses the differences in potential nonlinear causal relationships in various samples, supported by our causal identifiability theory. Second, a nonlinear causal kernel is proposed for clustering heterogeneous subgroups. Experimental results showcase the exceptional performance of our method in accurately identifying heterogeneous subgroups and effectively enhancing causal learning, leading to a great reduction in prediction error.

This paper addresses the issue of cross-class domain adaptation (CCDA) in semantic segmentation, where the target domain contains both shared and novel classes that are either unlabeled or unseen in the source domain. This problem is challenging, as the absence of labels for novel classes hampers the effective solutions of both cross-domain and cross-class problems. Since Visual Language Models (VLMs) have exhibited impressive generalization across diverse data distributions and are capable of generating zero-shot predictions without requiring task-specific training examples, we propose a label alignment method by leveraging VLMs to relabel pseudo labels for novel classes. Considering that VLMs typically provide only image-level predictions, we embed a two-stage method to enable fine-grained semantic segmentation and design a threshold based on the uncertainty of pseudo labels to exclude noisy VLM predictions. To further augment the supervision of novel classes, we devise memory banks with an adaptive update scheme to effectively manage accurate VLM predictions, which are then resampled to increase the sampling probability of novel classes. Through comprehensive experiments, we demonstrate the effectiveness and versatility of our proposed method across various CCDA scenarios.

Despite their success, unsupervised domain adaptation methods for semantic segmentation primarily focus on adaptation between image domains and do not utilize other abundant visual modalities like depth, infrared and event. This limitation hinders their performance and restricts their application in real-world multimodal scenarios. To address this issue, we propose Modality Adaptation with text-to-image Diffusion Models (MADM) for semantic segmentation task which utilizes text-to-image diffusion models pre-trained on extensive image-text pairs to enhance the model's cross-modality capabilities. Specifically, MADM comprises two key complementary components to tackle major challenges. First, due to the large modality gap, using one modal data to generate pseudo labels for another modality suffers from a significant drop in accuracy. To address this, MADM designs diffusion-based pseudo-label generation which adds latent noise to stabilize pseudo-labels and enhance label accuracy. Second, to overcome the limitations of latent low-resolution features in diffusion models, MADM introduces the label palette and latent regression which converts one-hot encoded labels into the RGB form by palette and regresses them in the latent space, thus ensuring the pre-trained decoder for up-sampling to obtain fine-grained features. Extensive experimental results demonstrate that MADM achieves state-of-the-art adaptation performance across various modality tasks, including images to depth, infrared, and event modalities. We open-source our code and models at https://github.com/XiaRho/MADM.

The precise localization of false data injection attacks (FDIAs) is vital to ensure the stable operation of smart grids. However, the intermittency and uncertainty of renewable energy (RE) can lead to confusion with unknown FDIA. As a result, previous works encountered difficulties in extracting distinguishable spatiotemporal features to construct accurate behavior models, thereby affecting the effectiveness of the localization task. To address this challenge, we establish a more practical data set for FDIA localization that takes RE into account. Subsequently, we propose a spatiotemporal sequence analysis framework for the task. Specifically, we propose a factorized module to mitigate the impact of temporal fluctuations, which processes data sequence with down sampling and feature aggregation. Additionally, we introduce a fine-tuning matrix to take regional correlations of RE into consideration, where the weights of spatial information aggregation are adjusted. We evaluate the effectiveness of our approach through comprehensive case studies on IEEE 14-bus, IEEE 57-bus, and IEEE 118-bus standard test systems. The experimental results indicate that our method outperforms the compared methods by an average of 2.52% and 3% in terms of recall and F1-score, respectively.

The linear quadratic (LQ) optimal control problem with indefinite weighting matrices and the stabilization problem for discrete-time descriptor stochastic Markov jump systems (DSMJSs) involving state-dependent noises are studied. By using the Moore-Penrose generalized inverse of matrices and the equivalent transformation of restricted system, the indefinite LQ problem for DSMJSs is equivalently converted into the indefinite LQ problem for Markov jump systems (MJSs). Under some viable conditions and stabilization assumption, the generalized stochastic algebraic Riccati equation having a unique semi-positive definite solution is guaranteed. Then the necessary and sufficient conditions which ensure that DSMJSs are causal and mean-square stable are established. It is shown that the admissibility of the optimal closed-loop systems is equivalent to stabilizability in mean-square sense of the transformed MJSs. Besides, an efficient iterative algorithm is given to verify the mean-square stabilizability of DSMJSs by solving an optimization problem. Two examples including a practical RLC circuit system are presented as verifications of the theoretical results.

Enabled by the widely deployed distributed flexible alternating current transmission system (D-FACTS) devices in practical systems, moving target defense (MTD) has been considered as an effective way to detect stealthy false data injection (FDI) attacks by actively changing branch parameters. However, existing MTD methods heavily depend on the assumption that opponents can not timely obtain the newly changed branch parameters. In this paper, a parameter-estimate-first FDI (PEF-FDI) attack is proposed to reveal vulnerabilities of MTD methods in AC state estimation, which can bypass bad data detectors in the existence of MTD. Specifically, a PEF-FDI attack model is proposed to timely construct attack vector and stealthily misguide the results of alternating current (AC) state estimation in the presence of MTD. Requirements of constructing PEF-FDI attacks on eavesdropped measurements are deduced to reveal the limitation on capability of attackers. Simulations in the IEEE 118-bus system verify the performance of the proposed PEF-FDI attacks.

Circuits and systems, such as multidimensional and nonlinear ones, large-scale integration circuits, and power networks, play a significant role in the whole spectrum of science and technology, from basic scientific theories to various real-world applications. With the increasing demand from applications, it is vital to develop circuits and systems with high accuracy, stability, flexibility, and security through efficient learning, design optimization, and integrated implementation. The rapid advancement of artificial intelligence (AI) has fostered a symbiotic relationship between circuits and systems and AI in both theory and applications. On the one hand, research in circuits and systems on efficient learning, design optimization, and integrated implementation aided by AI has recently gained a promising development, where energy-efficient circuits and systems have a very broad range of applications. On the other hand, the utilization of AI in real-world applications has become indispensable for the optimization and implementation of circuits and systems with high efficiency and low-power computation. Overall, through advanced learning, optimization, and implementation driven by AI, efficient circuits and systems running in real-time with low power can be realized for wider applications.

Energy storage systems (ESS) are pivotal component in the energy market, serving as both energy suppliers and consumers. ESS operators can reap benefits from energy arbitrage by optimizing operations of storage equipment. To further enhance ESS flexibility within the energy market and improve renewable energy utilization, a heterogeneous photovoltaic-ESS (PV-ESS) is proposed, which leverages the unique characteristics of battery energy storage (BES) and hydrogen energy storage (HES). For scheduling tasks of the heterogeneous PV-ESS, a practical cost function plays a crucial role in guiding operator’s strategies to maximize benefits. We develop a comprehensive cost function that takes into account degradation, capital, and operation/maintenance costs to reflect real-world scenarios. Moreover, while numerous methods excel in optimizing ESS energy arbitrage, they often rely on black-box models with opaque decision-making processes, limiting practical applicability. To overcome this limitation and enable explainable scheduling strategies, a prototype-based policy network with inherent interpretability is introduced. This network employs human-designed prototypes to guide decision-making by comparing similarities between prototypical situations and encountered situations, which allows for naturally explained scheduling strategies. Comparative results across four distinct cases demonstrate the effectiveness and practicality of our proposed pre-hoc interpretable optimization method when contrasted with black-box models.

Low-rank matrix recovery is a major challenge in machine learning and computer vision, particularly for large-scale data matrices, as popular methods involving nuclear norm and singular value decomposition (SVD) are associated with high computational costs and biased estimators. To overcome this challenge, we propose a novel approach to learning low-rank matrices based on the matrix volume and a nonconvex logarithmic function. The matrix volume is the product of all the nonzero singular values of a matrix and has unique geometric properties and connections with other convex and nonconvex functions. We establish a generalized nonconvex regularization problem using the penalty function strategy and introduce an accelerated proximal alternating linearized minimization (AccPALM) algorithm with double acceleration, which combines Nesterov’s acceleration and power strategy. The algorithm reduces computational costs and has provable convergence results under the Kurdyka-Łojasiewicz (KŁ) inequality with mild conditions. Our approach shows superior accuracy, efficiency, and convergence behavior compared to other low-rank matrix learning methods on robust matrix completion (RMC) and low-rank representation (LRR) tasks. We analyze the impact of algorithm parameters on convergence and performance and present visually appealing results to further demonstrate the effectiveness of our approach. The proposed methodology represents a promising advance in the field of low-rank matrix recovery, and its effectiveness has been validated via extensive numerical experiments. The source code for the proposed algorithms is accessible at https://github.com/ZhangHengMin/AccPALMcodes.

In leader-follower multiagent systems (MASs), seeking an efficient scheme to select a set of agents as leaders is important for realizing the expected cooperative performance. In this article, the problem of minimal leader selection is investigated for impulsive general linear MASs with switching topologies. This study focuses on selecting a set of agents as leaders that receive information from a reference signal directly, while minimizing the number of leaders, subject to consensus tracking performance. First, adopting the average dwell time technique and a time-ratio constraint, an explicit criterion for consensus tracking is derived as prepreparation for leader selection. Second, applying the submodular optimization framework, leader selection metrics are established based on the derived criterion. Third, employing the greedy rule, an efficient leader selection scheme is presented according to the established metrics. The scheme comprises two polynomial-time algorithms that return selected leader sets within a logarithmic bound of the optimum. Finally, the effectiveness of the developed leader selection scheme is verified using an illustrative example.

This work studies the security of consensus-based distributed filtering under the replay attack, which can freely select a part of sensors and modify their measurements into previously recorded ones. We analyze the performance degradation of distributed estimation caused by the replay attack, and utilize the Kullback-Leibler (K-L) divergence to quantify the attack stealthiness. Specifically, for a stable system, we prove that under any replay attack, the estimation error is not only bounded, but also can re-enter the steady state. In that case, we prove that the replay attack is ϵ -stealthy, where ϵ can be calculated based on two Lyapunov equations. On the other hand, for an unstable system, we prove that the trace of estimation error covariance is lower bounded by an exponential function, which indicates that the estimation error may diverge due to the attack. In view of this, we provide a sufficient condition to ensure that any replay attack is detectable. Furthermore, we analyze the case that the adversary starts to attack only if the current measurement is close to a previously recorded one. Finally, we verify the theoretical results via several numerical simulations.

In this article, we consider the partial quantum consensus problem of a qubit network in a distributed view. The local quantum operation is designed based on the Hamiltonian by using the local information of each quantum system in a network of qubits. We construct the unitary transformation for each quantum system to achieve the partial quantum consensus, that is, the directions of the quantum states in the Bloch ball will reach an agreement. A simple case of two-qubit quantum systems is considered first, and a minimum completing time of reaching partial consensus is obtained based on the geometric configuration of each qubit. Furthermore, we extend the approaches to deal with the more general N -qubit networks. Two partial quantum consensus protocols, based on the Lyapunov method for chain graphs and the geometry method for connected graphs, are proposed. The geometry method can be utilized to deal with more general connected graphs, while for the Lyapunov method, the global consensus can be obtained. The numerical simulation over a qubit network is demonstrated to verify the validity and the effectiveness of the theoretical results.

Event-triggered (ET) consensus of linear multiagent systems with relative output sensing on undirected graphs is studied. Two output-feedback protocols with static and time-varying coupling strengths, respectively, are proposed, which, different from the existing results in relative output sensing, integrate effective ET strategies to reduce the communication burdens between agents. To ensure the closed-loop consensus, design conditions about the gain matrices, coupling strengths, and event-triggering functions are derived. Zeno behaviors are also shown to be excluded from the triggering process. In addition, recursive algorithms are devised for computing the continuous-time relative signals required by the event-triggering functions, so that continuous monitoring of neighbors is circumvented. Numerical examples finally demonstrate the effectiveness of the proposed design method.

The robust LQ optimal regulator problem for discrete-time uncertain singular Markov jump systems (SMJSs) is solved by introducing a new quadratic cost function established by the penalty function method, which combines the penalty function and the weighting matrices. First, the indefinite robust optimal regulator problem for uncertain SMJSs is transformed into the robust optimal regulator problem with positive definite weighting matrices for uncertain Markov jump systems (MJSs). The transformed robust LQ problem is settled by the robust least-squares method, and the condition of the existence and analytic form of the robust optimal regulator are proposed. On the infinite horizon, the optimal state feedback is obtained, which can guarantee the regularity, causality, and stochastic stability of the corresponding optimal closed-loop system and eliminate the uncertain parameters of the closed-loop system. A numerical example and a practical example of DC motor are used to verify the validity of the conclusions.

This article proposes model-free reinforcement learning methods for minimum-cost state-flipped control in Boolean control networks (BCNs). We tackle two questions: 1) finding the flipping kernel, namely, the flip set with the smallest cardinality ensuring reachability and 2) deriving optimal policies to minimize the number of flipping actions for reachability based on the obtained flipping kernel. For Question 1), Q-learning's capability in determining reachability is demonstrated. To expedite convergence, we incorporate two improvements: 1) demonstrating that previously reachable states remain reachable after adding elements to the flip set, followed by employing transfer learning and 2) initiating each episode with special initial states whose reachability to the target state set are currently unknown. For Question 2), it is challenging to encapsulate the objective of simultaneously reducing control costs and satisfying terminal constraints exclusively through the reward function employed in the Q-learning framework. To bridge the gap, we propose a BCN-characteristics-based reward scheme and prove its optimality. Questions 1) and 2) with large-scale BCNs are addressed by employing small memory Q-learning, which reduces memory usage by only recording visited action-values. An upper bound on memory usage is provided to assess the algorithm's feasibility. To expedite convergence for Question 2) in large-scale BCNs, we introduce adaptive variable rewards based on the known maximum steps needed to reach the target state set without cycles. Finally, the effectiveness of the proposed methods is validated on both small- and large-scale BCNs.

The industry is undergoing a transformation toward smart manufacturing, fostering intelligent operations, sustainability, and digitalization. However, the current state of the process industry falls short of this future vision. Key areas, such as hybrid modeling, autonomous control, dynamic scheduling, intelligent decision making, security and safety control, and predictive maintenance, still require significant development. Given that the industrial metaverse enables the virtualization and digitization of industrial processes using technologies, such as artificial intelligence, blockchain, cloud computing, and digital twins, it is promising to establish the industrial metaverse for manufacturing, encompassing the entire lifecycle based on the industrial Internet and other modern information technologies.

The process industry is an industrial field of interdisciplinary nature involving electrical engineering, energy, petroleum, chemical, and metallurgy, which play a key role in the sustainable development. As a main source of CO2 emissions, the process industry will undertake a large part of the emission reduction task. In order to incorporate the impact of social factors, such as environment, society, and human to support the future process industry, the cyber-physical-social system (CPSS) framework should be considered as a promising way to enhance the transformation of the process industry. The development of CPSS technologies will fundamentally change the infrastructure of conventional industrial systems, offering a great opportunity for the greenization, high-value, and digitalization in the process industry. This article first presents the current status of the process industry. Through a CPSS framework, the current developments of the process industry as well as the main challenges and opportunities are discussed. A vision for the future process industry based on CPSS is described by focusing on three aspects, namely, the greenization and low carbon, high-value and high-end, digitalization, and intellectualization in process manufacturing. Finally, the advanced technologies and approaches in CPSS driven by artificial intelligence and industrial digitalization, which are important in achieving the sustainable development of the process industry, are outlined. The development of the comprehensive digital technologies, such as virtual reality, digital twin, blockchain, and big data, will stimulate the implementation of a ground-breaking concept formed in the CPSS framework called industrial metaverse.

Enabled by the widely existed distributed flexible alternating current transmission system devices in power systems, moving target defense (MTD) has been considered as an effective way to detect stealthy false data injection (FDI) attacks. However, due to the limitation of power system topology, not all stealthy FDI attacks can be detected in power system with MTD. In this article, the authors propose a joint meter coding (MC) and moving target defense (MC-MTD) method to cost-effectively improve the detection of stealthy FDI attacks through integrating MC with MTD. Detection conditions and requirements on MC-MTD are theoretically analyzed, which reveal the close coupling between MC and MTD in collaboratively detecting stealthy FDI attacks. The design of the coding matrix and the selection of encoded measurements are theoretically analyzed to integrate MC with MTD in a special case that the coding matrix is diagonal. An optimization of MC-MTD is formulated and approximately solved to improve detection effectiveness with a small defending cost. Finally, simulations are carried out on both direct and alternating current state estimations to validate the performance of MC-MTD.

The digitization of process manufacturing involves converting information and knowledge into a digital format through technologies, such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, and digital twins. This transformation promotes extension and optimization within the industrial, supply, and value chains, aiming to enhance decision-making efficiency, enable agile operations, and ensure information security and privacy. However, the current learning and operational approaches in the process industry remain rooted in traditional informatization, falling short of the vision for digital transformation. To address this gap, it is crucial to implement fusion analysis, deepen understanding, adopt autonomous learning, and enable intelligent optimization based on life-cycle data. Therefore, it is of fundamental importance to realize the transformation of process manufacturing toward digitalization and intelligentization, i.e., the use of artificial intelligence with decision-making capability, via new learning theories, methods, and algorithms.

Monocular depth estimation is one of the fundamental tasks in environmental perception and has achieved tremendous progress by virtue of deep learning. However, the performance of trained models tends to degrade or deteriorate when employed on other new datasets due to the gap between different datasets. Though some methods utilize domain adaptation technologies to jointly train different domains and narrow the gap between them, the trained models cannot generalize to new domains that are not involved in training. To boost the transferability of self-supervised monocular depth estimation models and mitigate the issue of meta-overfitting, we train the model in the pipeline of meta-learning and propose an adversarial depth estimation task. We adopt model-agnostic meta-learning (MAML) to obtain universal initial parameters for further adaptation and train the network in an adversarial manner to extract domain-invariant representations for easing meta-overfitting. In addition, we propose a constraint to impose upon cross-task depth consistency to compel the depth estimation to be identical in different adversarial tasks, which improves the performance of our method and smoothens the training process. Experiments on four new datasets demonstrate that our method adapts quite fast to new domains. Our method trained after 0.5 epoch achieves comparable results with the state-of-the-art methods trained at least 20 epochs.

The task of 3D semantic scene graph (3D SSG) prediction in the point cloud is challenging since (1) the 3D point cloud only captures geometric structures with limited semantics compared to 2D images, and (2) long-tailed relation distribution inherently hinders the learning of unbiased prediction. Since 2D images provide rich semantics and scene graphs are in nature coped with languages, in this study, we propose Visual-Linguistic Semantics Assisted Training (VL-SAT) scheme that can significantly empower 3DSSG prediction models with discrimination about long-tailed and ambiguous semantic relations. The key idea is to train a powerful multi-modal oracle model to assist the 3D model. This oracle learns reliable structural representations based on semantics from vision, language, and 3D geometry, and its benefits can be heterogeneously passed to the 3D model during the training stage. By effectively utilizing visual-linguistic semantics in training, our VL-SAT can significantly boost common 3DSSG prediction models, such as SGFN and SGGpoint, only with 3D inputs in the inference stage, especially when dealing with tail relation triplets. Comprehensive evaluations and ablation studies on the 3DSSG dataset have validated the effectiveness of the proposed scheme. Code is available at https://github.com/wz7in/CVPR2023-VLSAT.

Most nighttime semantic segmentation studies are based on domain adaptation approaches and image input. However, limited by the low dynamic range of conventional cameras, images fail to capture structural details and boundary information in low-light conditions. Event cameras, as a new form of vision sensors, are complementary to conventional cameras with their high dynamic range. To this end, we propose a novel unsupervised Cross-Modality Domain Adaptation (CMDA) framework to leverage multi-modality (Images and Events) information for nighttime semantic segmentation, with only labels on daytime images. In CMDA, we design the Image Motion-Extractor to extract motion information and the Image Content-Extractor to extract content information from images, in order to bridge the gap between different modalities (Images ⇌ Events) and domains (Day ⇌ Night). Besides, we introduce the first image-event nighttime semantic segmentation dataset. Extensive experiments on both the public image dataset and the proposed image-event dataset demonstrate the effectiveness of our proposed approach. We open-source our code, models, and dataset at https://github.com/XiaRho/CMDA.

This paper tackles the challenges of self-supervised monocular depth estimation in indoor scenes caused by large rotation between frames and low texture. We ease the learning process by obtaining coarse camera poses from monocular sequences through multi-view geometry to deal with the former. However, we found that limited by the scale ambiguity across different scenes in the training dataset, a naïve introduction of geometric coarse poses cannot play a positive role in performance improvement, which is counter-intuitive. To address this problem, we propose to refine those poses during training through rotation and translation/scale optimization. To soften the effect of the low texture, we combine the global reasoning of vision transformers with an overfitting-aware, iterative self-distillation mechanism, providing more accurate depth guidance coming from the network itself. Experiments on NYUv2, ScanNet, 7scenes, and KITTI datasets support the effectiveness of each component in our framework, which sets a new state-of-the-art for indoor self-supervised monocular depth estimation, as well as outstanding generalization ability. Code and models are available at https://github.com/zxcqlf/GasMono

Manipulating deformable objects, such as ropes (1D), fabrics (2D), and bags (3D), poses a significant challenge in robotics research due to their high degree of freedom in physical state and nonlinear dynamics. Compared with single-dimensional deformable objects, multi-dimensional object manipulation suffers from the difficulty in recognizing the characteristics of the object correctly and making an accurate action decision on the deformable object of various dimensions. Some methods are proposed to use neural networks to rearrange deformable objects in all dimensions, but their approaches are not accurate in predicting the motion of the robot as they just consider the equivariance in the picking objects. To address this problem, we present a novel Transporter Network encoded and decoded with equivariance to generalize to different picking and placing positions. Additionally, we propose an equivariant goal-conditioned model to enable the robot to manipulate deformable objects into flexible configurations without relying on artificially marked visual anchors for the target position. Finally, experiments conducted in both Deformable-Ravens and the real world demonstrate that our equivariant models are more sample efficient than the traditional Transporter Network. The video is available at https://youtu.be/5_q5ff9c9FU.

Considering the popularity of electric vehicles and the flexibility of household appliances, it is feasible to dispatch energy in home energy systems under dynamic electricity prices to optimize electricity cost and comfort residents. In this paper, a novel home energy management (HEM) approach is proposed based on a data-driven deep reinforcement learning method. First, to reveal the multiple uncertain factors affecting the charging behavior of electric vehicles (EVs), an improved mathematical model integrating driver’s experience, unexpected events, and traffic conditions is introduced to describe the dynamic energy demand of EVs in home energy systems. Second, a decoupled advantage actor-critic (DA2C) algorithm is presented to enhance the energy optimization performance by alleviating the overfitting problem caused by the shared policy and value networks. Furthermore, separate networks for the policy and value functions ensure the generalization of the proposed method in unseen scenarios. Finally, comprehensive experiments are carried out to compare the proposed approach with existing methods, and the results show that the proposed method can optimize electricity cost and consider the residential comfort level in different scenarios.

In multi-agent systems with leader-follower dynamics, choosing a subset of agents as leaders is a critical step in achieving the desired coordination performance. In this study, by considering consensus tracking for general linear multi-agent systems under switching topologies, we address the problem of selecting a minimum-size set of leaders by leveraging the submodularity ratio. First, using the dwell time technique, a criterion is derived to ensure that the states of all agents can converge to a reference trajectory that is directly tracked by each leader. Second, exploiting the derived consensus tracking criterion, the metrics with a structure of the Euclidean distance between specific vectors and the space spanned by an iteratively updated matrix are established to identify a set of leaders, and then the corresponding bound of the submodularity ratio is proposed. Third, combining the derived criterion and the constructed metrics, a leader selection scheme is presented together with three polynomial-time algorithms, and the related provable optimality bound of each algorithm can be obtained by leveraging the proposed bound of the submodularity ratio. Finally, illustrative examples are provided to verify the effectiveness of the proposed leader selection scheme.

This study concentrates on the differential private distributed optimization problem with an event-triggered mechanism, whose goals include preserving the privacy of agents’ initial states and local cost functions and improving communication efficiency. A distributed event-triggered mechanism is integrated into the differentially private subgradient-push distributed optimization algorithm and then a new algorithm named as DP-ETSP is designed, where the real-time information propagation among agents is avoided. Additionally, under the proposed event-triggered mechanism, an analysis of mean-square consensus and optimality over time-varying directed networks is made when the added Laplace noises meet some specific decaying conditions. Convergence rate results are further established under a specific stepsize, which are equal to the rate of stochastic gradient-push algorithm without event-triggered communication. Moreover, the differential privacy preservation performance is analyzed and the rule for selecting privacy level is discussed. Finally, the feasibility and effectiveness of DP-ETSP are verified in two simulation cases.

In Home Energy Management System (HEMS), the scheduling of energy storage equipment and shiftable loads has been widely studied to reduce home energy costs. However, existing data-driven methods can hardly ensure the transferability amongst different tasks, such as customers with diverse preferences, appliances, and fluctuations of renewable energy in different seasons. This paper designs a transferable scheduling strategy for HEMS with different tasks utilizing a Meta-Reinforcement Learning (Meta-RL) framework, which can alleviate data dependence and massive training time for other data-driven methods. Specifically, a more practical and complete demand response scenario of HEMS is considered in the proposed Meta-RL framework, where customers with distinct electricity preferences, as well as fluctuating renewable energy in different seasons are taken into consideration. An inner level and an outer level are integrated in the proposed Meta-RL-based transferable scheduling strategy, where the inner and the outer level ensure the learning speed and appropriate initial model parameters, respectively. Moreover, Long Short-Term Memory (LSTM) is presented to extract the features from historical actions and rewards, which can overcome the challenges brought by the uncertainties of renewable energy and the customers’ loads, and enhance the robustness of scheduling strategies. A set of experiments conducted on practical data of Australia’s electricity network verify the performance of the transferable scheduling strategy.

In recent years, artificial intelligence has played an important role on accelerating the whole process of drug discovery. Various of molecular representation schemes of different modals (e.g., textual sequence or graph) are developed. By digitally encoding them, different chemical information can be learned through corresponding network structures. Molecular graphs and Simplified Molecular Input Line Entry System (SMILES) are popular means for molecular representation learning in current. Previous works have done attempts by combining both of them to solve the problem of specific information loss in single-modal representation on various tasks. To further fusing such multi-modal imformation, the correspondence between learned chemical feature from different representation should be considered. To realize this, we propose a novel framework of molecular joint representation learning via Multi-Modal information of SMILES and molecular Graphs, called MMSG. We improve the self-attention mechanism by introducing bond-level graph representation as attention bias in Transformer to reinforce feature correspondence between multi-modal information. We further propose a Bidirectional Message Communication Graph Neural Network (BMC GNN) to strengthen the information flow aggregated from graphs for further combination. Numerous experiments on public property prediction datasets have demonstrated the effectiveness of our model.

With the growing interest in convex and nonconvex low-rank matrix learning problems, the widely used singular value thresholding (SVT) operators associated with rank relaxation functions often face higher computational complexity, particularly for large-scale data matrices. To improve the efficacy of low-rank subspace clustering and overcome the issue of high computational complexity, this work proposes an efficient and effective method that avoids the need for singular value decomposition (SVD) computations in the iteration scheme. This can be achieved through the use of a computationally efficient and compact formulation, as well as automatic removal of the optimal mean, which reduces time consumption and enhances evaluation performance. A unified clustering framework based on Schatten-$p$norm regularized by$\ell _{2,q}$-norm can be formulated using this processing way, where inner element suppression can be achieved by choosing appropriate$p$,$q \in (0,1)$. Additionally, calculating the optimal mean enhances the robustness of the proposed method in the presence of outliers. Unlike the general iteration scheme of the alternating direction method of multiplier (ADMM) algorithms that introduce auxiliary splitting variables, the proposed alternating re-weighted least square (ARwLS) algorithm uses matrix inverse and multiplication computations to obtain analytic solutions, resulting in faster processing speeds for each sub-problem. To further investigate, we provide the computational complexity of each iteration and the theoretical analysis of the convergence property, where the derived solution is a stationary point. Experimental results on synthetic data and several benchmark datasets demonstrate the promising efficiency and efficacy of the proposed clustering method compared to classical and competing algorithms.

In this article, inspired by the Halanay inequality, we study stability of sampled-data systems with packet losses by proposing a nonuniform sampling interval approach. First, a sampled-data controller with an exponential gain is put forward to reduce conservatism. We obtain the sufficient condition for linear sampled-data systems to be exponentially stable by extending the famous Halanay inequality to sampled-data systems. The obtained sufficient conditions indicate that the maximal-allowable bound of sampling intervals is determined by the constant terms in the Halanay inequality, and the decay rate is presented in the form of a Lambert function. Compared with some existing results on the stability of sampled-data systems by using the Gronwall-Bellman Lemma, the conservatism induced by the exponential term via the Gronwall-Bellman Lemma can be reduced to some extent. Considering the phenomenon of packet losses, a new lemma is further proposed to generalize the proposed Halanay-like inequality. The results derived by the new lemma permit that there exist some sampling intervals with the upper bound violating the desired condition of the Halanay-like inequality. This permits us to establish exponential stability in significant cases that do not satisfy the Halanay-like inequality needed in the previous results. Finally, the sampled-data local exponential stability is investigated for nonlinear systems with strong nonlinearity.

Problem decomposition is crucial for coping with large-scale global optimization problems, which relies heavily on highly precise variable grouping methods. The state-of-the-art decomposition methods identify separability based on the finite differences principle, which is valid only for additively separable functions but not applicable to non-additively separable functions. Therefore, we need to investigate separability in more depth in order to propose a more general principle and design more universal decomposition methods. In this paper, we conduct a comprehensive theoretical investigation on separability, the core of which is proposing an innovative separability identification principle: the minimum points shift principle. By utilizing the new principle, we develop a general separability grouping (GSG) method that can handle both additively and non-additively separable functions with high accuracy. In addition, we design a new set of benchmark functions based on non-additive separability, which compensates for the lack of non-additively separable functions in the previous test suites. Extensive experiments demonstrate that the proposed GSG achieves high grouping accuracy on both new and CEC series benchmark problems, especially on non-additively separable problems Finally, we verify that the proposed GSG can effectively improve the optimization performance of non-additively separable problems through optimization experiments.

Stochastic Boolean networks (SBNs) take process noise into account, so it is better to fit the actual situation and has a wider application background than Boolean networks (BNs). However, the presence of noise influences us to estimate the real state of the system. To minimize the inaccuracies caused by the presence of noise, an optimal state estimation problem is studied in this article. The multi-sensor fusion Boolean Bayesian filtering is proposed and a recursive algorithm is provided to calculate the prior and posterior belief of system state by fusing multi-sensor measurements based on the algebraic form of the SBN and Bayesian law. Then, the optimal state estimator is obtained, which minimizes the mean-square estimation error. Finally, a simulation example is carried out to demonstrate the performance of the proposed methodology. It has been shown through the simulation experiment that it increases the confidence level of the state estimation and improves the estimation performance using multi-sensor fusion compared with using single sensor.

Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception, and navigation capabilities of autonomous systems have been efficiently addressed, and many new learning-based algorithms have surfaced with respect to autonomous visual perception and navigation. In this review, we focus on the applications of learning-based monocular approaches in ego-motion perception, environment perception, and navigation in autonomous systems, which is different from previous reviews that discussed traditional methods. First, we delineate the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions, which demonstrate the necessity to integrate deep learning techniques. Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks. Then, we focus on the visual navigation based on learning systems, mainly including reinforcement learning and deep reinforcement learning. Finally, we examine several challenges and promising directions discussed and concluded in related research of learning systems in the era of computer science and robotics.

Decomposing data matrix into low-rank plus additive matrices is a commonly used strategy in pattern recognition and machine learning. This article mainly studies the alternating direction method of multiplier (ADMM) with two dual variables, which is used to optimize the generalized nonconvex nonsmooth low-rank matrix recovery problems. Furthermore, the minimization framework with a feasible optimization procedure is designed along with the theoretical analysis, where the variable sequences generated by the proposed ADMM can be proved to be bounded. Most importantly, it can be concluded from the Bolzano-Weierstrass theorem that there must exist a subsequence converging to a critical point, which satisfies the Karush-Kuhn-Tucher (KKT) conditions. Meanwhile, we further ensure the local and global convergence properties of the generated sequence relying on constructing the potential objective function. Particularly, the detailed convergence analysis would be regarded as one of the core contributions besides the algorithm designs and the model generality. Finally, the numerical simulations and the real-world applications are both provided to verify the consistence of the theoretical results, and we also validate the superiority in performance over several mostly related solvers to the tasks of image inpainting and subspace clustering.

Self-supervised monocular depth estimation is an attractive solution that does not require hard-to-source depth la-bels for training. Convolutional neural networks (CNNs) have recently achieved great success in this task. However, their limited receptive field constrains existing network architectures to reason only locally, dampening the effectiveness of the self-supervised paradigm. In the light of the recent successes achieved by Vision Transformers (ViTs), we propose MonoViT, a brand-new framework combining the global reasoning enabled by ViT models with the flexibility of self-supervised monocular depth estimation. By combining plain convolutions with Transformer blocks, our model can reason locally and globally, yielding depth prediction at a higher level of detail and accuracy, allowing MonoViT to achieve state-of-the-art performance on the established KITTI dataset. Moreover, MonoViT proves its superior generalization capacities on other datasets such as Make3D and DrivingStereo. Source code available at https://github.com/zxcqlf/MonoViT

In cooperative multi-agent reinforcement learning, centralized training and decentralized execution (CTDE) has achieved remarkable success. Individual Global Max (IGM) decomposition, which is an important element of CTDE, measures the consistency between local and joint policies. The majority of IGM-based research focuses on how to establish this consistent relationship, but little attention has been paid to examining IGM's potential flaws. In this work, we reveal that the IGM condition is a lossy decomposition, and the error of lossy decomposition will accumulated in hypernetwork-based methods. To address the above issue, we propose to adopt an imitation learning strategy to separate the lossy decomposition from Bellman iterations, thereby avoiding error accumulation. The proposed strategy is theoretically proved and empirically verified on the StarCraft Multi-Agent Challenge benchmark problem with zero sight view. The results also confirm that the proposed method outperforms state-of-the-art IGM-based approaches.

Setting retail electricity prices is one of the significant strategies for energy management of multi-microgrid (MMG) systems integrated with renewable energy. Nevertheless, the need of privacy preservation, the uncertainties of renewable energy and loads, as well as the time-varying scenarios, bring challenges for pricing problems. In this paper, a two-level pricing framework is proposed based on interval predictions and model-free reinforcement learning to address these challenges. In particular, at the higher level, the distribution system operator (DSO) is viewed as an agent, which sets retail electricity prices without detailed user information for privacy protection to maximize the total revenue from selling energy with reinforcement learning. For time-varying scenarios with intermittent photovoltaic power generation and diverse loads, a differentiable trust region layer is considered in reinforcement learning to improve the robustness of the policy updating process. While at the lower level, operators in microgrids solve three-phase unbalanced optimal power flow (OPF) problems to minimize generation cost and network power loss. Additionally, to deal with the challenges from the uncertainties of renewable power generation and user loads, interval predictions are chosen to quantify prediction errors and improve the flexibility of pricing policies. Finally, a set of experiments are conducted to validate the effectiveness of the proposed method for pricing problems in MMG systems.

This paper designs an event-triggering based communication strategy for the global attitude synchronization of a network of rigid bodies. To overcome the topological constraint on the manifold$SO(3)$, the quaternion-based hybrid control strategy is designed using a binary logic variable, relying on the relative measurements of adjacent rigid bodies, to determine the torque orientation. The Zeno-free distributed event-triggering strategies (ETSs) are designed combining with the reset of the binary logic variable to generate discrete communication instants, where only the corresponding parts of the control inputs are updated at those discrete instants. By assuming perfect knowledge of the rigid bodies’ dynamics and considering uncertainties and/or exogenous disturbances simultaneously, nominal and robust cases are analyzed to ensure the global attitude synchronization, respectively. The effectiveness of the main results is demonstrated by considering the attitude synchronization of six miniature quadrotor prototypes.

The main purpose of this article is to investigate the consensus of linear multiagent networks with time-varying characteristics under sampled-data communications, where the time-varying characteristics include both time-varying topologies and the node's linear time-varying dynamics. By using the decoupling method, we prove that the sampled-data consensus problem of multiagent networks is equal to the stability problem of sampled-data systems. Then, the globally asymptotical consensus is investigated for multiagent networks with time-varying characteristics by virtue of the Lyapunov function method. It should be noted that when the Lyapunov function method is utilized to investigate the stability problem of control systems, it is always assumed that the derivative of the constructed Lyapunov function is not more than zero. This assumption is removed here and as a replacement, the average value of the derivative of the Lyapunov function in a period to be negative is needed.

In many real-world optimization applications, a goal solution (i.e., scenario) is often provided by a user according to his/her experience. Due to the presence of a wide range of uncertainties, one may be interested in identifying the robustness interval of the solution, i.e., the range of the decision variables in which the solution remains robust. This article investigates how to find the robustness intervals of the goal solution in evolutionary robust optimization and formulates this as a bilevel optimization problem. Then, a novel algorithm framework is proposed to solve the bilevel problem: an efficient heuristic-based approach is developed to optimize the upper level task, while a global optimizer is utilized to tackle the lower level task. The proposed heuristic-based approach contains four key components: 1) peak detection; 2) peak allocation; 3) calculation of the next perturbation value; and 4) robustness interval fine-tuning, aiming to enhance the efficiency of searching for the target intervals. Finally, three types of artificial test problems and a practical problem are provided to verify the effectiveness of the proposed algorithm framework. The results show that all the robustness intervals can be successfully found when the goal solution is given by means of the proposed algorithm framework.

Many problems in industrial smart manufacturing, such as process operational optimization and decision-making, can be regarded as distributed nonconvex optimization problems, whose goal is to utilize distributed nodes to cooperatively search for the minimal value of the global objective function. With the consideration of data transmission mode, transmission condition, and communication waste in industrial applications, it is meaningful to study the distributed nonconvex optimization problem with an event-triggered strategy over time-varying directed networks. To solve such a problem, a distributed nonconvex event-triggered algorithm is proposed in this article. Under some assumptions on local objective functions, gradients, and step sizes, the convergence of the proposed event-triggered algorithm to the local minimum is established theoretically. Moreover, it is obtained that the proposed distributed event-triggered algorithm has a convergence rate of$O(1/\ln (t))$. Finally, two examples of industrial systems are provided to validate the effectiveness of the proposed algorithm.

Traditional monocular direct visual odometry (DVO) is one of the most famous methods to estimate the ego-motion of robots and map environments from images simultaneously. However, DVO heavily relies on high-quality images and accurate initial pose estimation during tracking. With the outstanding performance of deep learning, previous works have shown that deep neural networks can effectively learn 6-DoF (Degree of Freedom) poses between frames from monocular image sequences in the unsupervised manner. However, these unsupervised deep learning-based frameworks cannot accurately generate the full trajectory of a long monocular video because of the scale-inconsistency between each pose. To address this problem, we use several geometric constraints to improve the scale-consistency of the pose network, including improving the previous loss function and proposing a novel scale-to-trajectory constraint for unsupervised training. We call the pose network trained by the proposed novel constraint as TrajNet. In addition, a new DVO architecture, called deep direct sparse odometry (DDSO), is proposed to overcome the drawbacks of the previous direct sparse odometry (DSO) framework by embedding TrajNet. Extensive experiments on the KITTI dataset show that the proposed constraints can effectively improve the scale-consistency of TrajNet when compared with previous unsupervised monocular methods, and integration with TrajNet makes the initialization and tracking of DSO more robust and accurate.

Deep learning-based methods mymargin have achieved remarkable performance in 3-D sensing since they perceive environments in a biologically inspired manner. Nevertheless, the existing approaches trained by monocular sequences are still prone to fail in dynamic environments. In this work, we mitigate the negative influence of dynamic environments on the joint estimation of depth and visual odometry (VO) through hybrid masks. Since both the VO estimation and view reconstruction process in the joint estimation framework is vulnerable to dynamic environments, we propose the cover mask and the filter mask to alleviate the adverse effects, respectively. As the depth and VO estimation are tightly coupled during training, the improved VO estimation promotes depth estimation as well. Besides, a depth-pose consistency loss is proposed to overcome the scale inconsistency between different training samples of monocular sequences. Experimental results show that both our depth prediction and globally consistent VO estimation are state of the art when evaluated on the KITTI benchmark. We evaluate our depth prediction model on the Make3D dataset to prove the transferability of our method as well.

This article studies the formation control problem of second-order multiagent networks, in which cooperative and antagonistic interactions of the agents spontaneously coexist in the communication process. Based on the convex analysis theory, several convex polytopes that do not require some kinds of system constraints are constructed in the presence of these interactions. Then, the matrix perturbation theory and some mathematical techniques are utilized to analyze these convex polytopes. The obtained results show that the agents with cooperative interactions monotonously converge to their own specified formation shape while maintaining the desired relative position of the other agents with antagonistic interactions. Subsequently, two numerical examples are presented to illustrate the obtained results.

This paper considers a one versus one two-phase capture-the-flag differential game with speed heterogeneity. The attacker tries to capture the flag in Phase-I (Flag-Capture) and return to the safe region in Phase-II (Flag-Return), while the defender aims to intercept the attacker. Firstly, we analyse the single-phase optimal strategies by employing the Apollonius circle. Moreover, we present two-phase jointly optimal strategies by modeling the game as constrained nonlinear optimization problems , which are solved by sequential quadratic programming (SQP) algorithm. Besides, we construct the winning regions by searching the border radius of the region when positions of defender and flag are given. It is worth noting that all results is analytical except for the SQP and search Algorithms. Finally, we present simulations to verify the proposed methods.

In this paper, the resilient control for linear discrete-time multi-agent networks subjected to unconfined cyber-attacks is investigated based on a data-driven method. Firstly, according to the evolution of the original network dynamics, a distributed data-driven estimation algorithm is presented. On this basis, a switching control law is proposed to solve the resilient consensus problem for the discrete-time multi-agent network under unconfined cyber-attacks. Further, some necessary and sufficient conditions for designing the resilient controllers are obtained by solving a nonlinear matrix inequation. Secondly, the proposed data-driven method is extended to study the resilient tracking control and formation control problems. Finally, some numerical simulations are provided to verify the effectiveness of the data-driven resilient control method.

Under practical environments, some certain noises may arise from the communication of agents due to electromagnetic interference, sensor error, and other disturbances, which are usually modeled by additive noise in the relative position state between an agent and its neighbors. In this article, a formation control strategy is considered for this kind of relative position state with the additive noise, where the unmeasured velocity information is estimated by an observer-based strategy. On the other hand, the considered problem is transformed into a convergence problem of infinite products of a sequence of general stochastic matrices. The general stochastic matrix means that a matrix has row sum one but its entries do not necessarily require non-negative. This article develops the convex analysis to cope with such infinite products. Then, a sufficient condition is obtained to ensure that the specified formation shape in a noisy environment can be achieved. Subsequently, an example is presented to show the effectiveness of the result.

In this paper, pinning controllability for a Boolean network (BN) under arbitrary disturbance inputs is considered. By selecting a fraction of nodes as the pinning nodes and injecting controllers that depend on the values of the disturbance inputs of the previous time instant, the controllability can be guaranteed for any BNs under arbitrary disturbance inputs. First, based on the necessary and sufficient conditions for the controllability of a BN with arbitrary disturbance inputs obtained in this paper, a constructive method for designing the transition matrix of the BN is presented, which further provides a method for selecting the pinning nodes. Second, a logical relationship between the control input nodes and system nodes is provided through solving logical matrix equations. Third, the control input sequence algorithm is also given. Finally, an example is presented to show the effectiveness of the proposed results.

This paper focuses on the time-varying formation tracking issue for nonlinear multiagent systems (MASs). Based on the explicit characterizations of frequency, duration, and magnitude properties for deception attacks, a hybrid framework is proposed for time-varying formation tracking of nonlinear MASs. To realize the desired formation tracking performance under deception attacks, the distributed edge-based event-triggered communication strategies are proposed with Zeno-freeness. The designed strategies are resilient to deception attacks under some appropriate assumptions, to realize a predefined formation and simultaneously track the convex combination of leaders' states. The designed control strategies render that we do not need to detect when the deception attack happens. Furthermore, the obtained results can be deduced to deal with consensus/synchronization problems, target enclosing problems for MASs with one/multiple leaders, where the communication is attacked by malicious attackers. An example of time-varying formation tracking of unmanned aerial vehicles is provided to show the effectiveness of the obtained results.

The economic dispatch problem (EDP) plays a fundamental and significant role in smart grids. Its purpose is to decide the output power of every generator in smart grids for achieving the minimal generation cost. With advantages in flexibility, robustness, and scalability, it is desirable to apply distributed optimization methods to solve EDPs. In most existing distributed optimization approaches, all generators explicitly exchange their states with neighbors to obtain the optimal solution, which may result in disclosing the privacy information of generators. This problem becomes worse if there are some adversaries aimed at inferring privacy information from the communication network for nefarious purposes. For privacy preservation, a privacy preserving distributed optimization algorithm over time-varying directed communication networks is proposed in this article by adding conditional noises to the exchanged states. It is proved that this proposed algorithm is able to solve the EDP. Moreover, the convergence rate and privacy analysis of the proposed algorithm are also shown in this article. An example is provided to confirm the effectiveness of this proposed algorithm.

In this article, a defense method with watermarking to detect linear deception attack under Kullback-Leibler (K-L) divergence detector in cyber-physical system (CPS) is proposed. It is known that linear deception attacks can reduce the performance of remote estimator without being detected by the K-L divergence detector. In order to detect this kind of attack, we use watermarking to encrypt and decrypt data transmitted through wireless networks. When the attack does not exist, the transmitted data can be restored to ensure the remote estimation performance. In the presence of linear deception attacks, these data are marked with a watermarking so that they can assist the K-L divergence detector to discover the attack. The watermarking encryption method is proved to be helpful for K-L divergence detector to discover attack, or weaken the impact of the attack in different situations. Finally, numerical simulations are provided to further illustrate the results.

Semantic segmentation and depth completion are two challenging tasks in scene understanding, and they are widely used in robotics and autonomous driving. Although several studies have been proposed to jointly train these two tasks using some small modifications, such as changing the last layer, the result of one task is not utilized to improve the performance of the other one despite that there are some similarities between these two tasks. In this article, we propose multitask generative adversarial networks (Multitask GANs), which are not only competent in semantic segmentation and depth completion but also improve the accuracy of depth completion through generated semantic images. In addition, we improve the details of generated semantic images based on CycleGAN by introducing multiscale spatial pooling blocks and the structural similarity reconstruction loss. Furthermore, considering the inner consistency between semantic and geometric structures, we develop a semantic-guided smoothness loss to improve depth completion results. Extensive experiments on the Cityscapes data set and the KITTI depth completion benchmark show that the Multitask GANs are capable of achieving competitive performance for both semantic segmentation and depth completion tasks.

Previous work has shown that adversarial learning can be used for unsupervised monocular depth and visual odometry (VO) estimation, in which the adversarial loss and the geometric image reconstruction loss are utilized as the mainly supervisory signals to train the whole unsupervised framework. However, the performance of the adversarial framework and image reconstruction is usually limited by occlusions and the visual field changes between the frames. This article proposes a masked generative adversarial network (GAN) for unsupervised monocular depth and ego-motion estimations. The MaskNet and Boolean mask scheme are designed in this framework to eliminate the effects of occlusions and impacts of visual field changes on the reconstruction loss and adversarial loss, respectively. Furthermore, we also consider the scale consistency of our pose network by utilizing a new scale-consistency loss, and therefore, our pose network is capable of providing the full camera trajectory over a long monocular sequence. Extensive experiments on the KITTI data set show that each component proposed in this article contributes to the performance, and both our depth and trajectory predictions achieve competitive performance on the KITTI and Make3D data sets.

The economic dispatch problem (EDP) is one of the fundamental and important problems in power systems. The objective of EDP is to determine the output generation of generators to minimize the total generation cost under various constraints. In this article, a finite-time consensus-based distributed optimization algorithm is proposed to solve EDP. It is only required that each device in the communication network has access to its own local generation cost function, designed virtual local demand and its neighbors' local optimization variables. The proposed finite-time algorithm can solve EDP, if the gain parameters in the algorithm satisfy some conditions under undirected and connected time-varying graphs. Moreover, the bounded or linear increasing assumption on the gradient and subgradient of objecive functions is relaxed in this algorithm. Examples under several cases are provided to verify the effectiveness of the proposed distributed optimization algorithm.

In this paper, the predefined-time consensus tracking problem of second-order multiagent systems (MASs) is investigated. A distributed observer is presented to estimate the tracking error for each follower within predefined time. A novel sliding surface is constructed to ensure predefined-time system convergence along the sliding surface and a terminal sliding mode consensus protocol is presented to overcome singularity problem and achieve leader-following consensus within predefined time. It is mathematically proved that the followers' states can track the leader's trajectory within predefined time. In particular, the settling time bound is directly related to tunable parameters, which facilitates the control protocol design to meet the desired convergence time requirement. Besides, the estimation bound for convergence time is less conservative than some existing fixed-time consensus protocols. The effectiveness of the proposed method is verified by the consensus tracking control for networked single-link robotic manipulators.

This paper considers fixed-time consensus tracking of second-order multiagent systems (MASs) under directed interaction topology. A novel distributed observer is presented to estimate the leader's states within a fixed time, which overcomes the difficulties caused by the asymmetry of the Laplacian matrix. A sliding surface is designed and a nonsingular terminal sliding mode consensus protocol is developed to achieve fixed-time convergence of the tracking error to the origin. It is shown that each follower can track the leader's trajectory within a fixed time. Particularly, the gain of the presented consensus protocol is directly related to the prescribed time, which makes it convenient to determine and tune the gain according to the requirement of convergence time. Moreover, the presented control protocol reduces the conservativeness of the convergence time estimation for sliding motion. The simulation results validate the effectiveness of the proposed consensus scheme.

In this article, the ultrafast tracking control for high-order discrete-time multiagent networks is investigated based on the predictive ability of agents. First, in view of the evolution of the original network dynamics, a distributed multistep prediction algorithm is established. Further, a control strategy containing predictive information is presented to achieve the ultrafast tracking control. Second, the problem of ultrafast tracking control with an$H_{\infty }$performance specification is dealt with. The obtained result indicates that the robustness of the whole multiagent network can be converted into the robustness of multiple separable subsystems. Finally, the optimal ultrafast static tracking control design is provided for the single-integrator network with sampled dynamics. Two simulation cases are carried out to verify the correctness of the obtained theoretical results.

This paper studies the design of adaptive event-triggered protocols for consensus of linear multi-agent systems (MASs) with external disturbances. Different from most of the existing results that deal with undirected graphs, this paper addresses directed graphs, specifically graphs that are assumed to be strongly connected. Inspired by a recent development [1], this paper devises novel adaptive event-triggered protocols for linear MASs with all agents subject to external disturbances. Two specific designs of composite triggering conditions are analysed and discussed. Compared with the disturbance-free case, additional constraints need to be introduced to deal with external disturbances and moreover the time-dependent terms are allowed to take any finite functions, rather than a class of L1 functions.

Trajectory planning in complex environments with different kinds of obstacles, like static obstacles, dynamic obstacles and noncooperative agents, is of great pratical importance for Unmanned Aircraft Vehicle (UAV). Although various algorithms are proposed to solve the obstacle avoidance problem, these methods only consider one or two kinds of obstacles. In this paper, a novel algorithm is proposed to plan a trajectory for UAV in such an environment which simultaneously includes static obstacles, dynamic obstacles and especially noncooperative agents. Besides, we consider different types of approach modes of noncooperative agents and propose corresponding strategies to avoid collisions with them. A set-valued filter-based method is proposed to predict the position of dynamic obstacles and noncooperative agents whose motion model is not available to UAV. Meanwhile, the measurement noise is considered in the set-valued filter to improve the safety of UAV. Some simulations are implemented to verify the effectiveness of the proposed method and they confirm that the proposed method can plan a safe trajectory for UAV in complex environments.

This paper investigates the effective computational resource allocation for large-scale fully-separable problems under the framework of a cooperative co-evolutionary algorithm called MLSoft. According to different subgroup sizes of the problems, we allocate different numbers of iterations to the subproblems in all the cycles. For high-dimensional subproblems, more iterations are needed during the optimization process; while for low-dimensional subproblems, fewer iterations will be assigned. The experimental results reveal that the proposed resource allocation scheme is simple but effective, which can enhance the performance of MLSoft in solving large-scale fully-separable problems. In addition, we conduct a group of experiments to evaluate the results if a higher weight is assigned to more recent performance in MLSoft. The results show that introducing weight to the latest reward affects very little on the performance of MLSoft.

Cyber-security is of the fundamental importance for cyber-physical systems (CPSs), since CPSs are vulnerable to cyber attack. In order to make the defensive measures better, one needs to understand the behavior from the view of an attacker. In this paper, the problem of false data injection attack on remote state estimation with resource constraints is studied in two cases, where the first case is that the attacker adds a Gaussian noise to the innovation, while the other is that the attacker employs a Gaussian noise to replace the innovation. In addition, the attacker is assumed to has a resource constraint, i.e., he/she cannot attack all the sensors, at the same time should decide which sensors to attack. By using the matrix theory, the optimal attack strategy problem, which aims to maximize the trace of the remote estimation error covariance, is converted into a convex optimization problem that can be solved. Thus, an optimal attack strategy is given to illustrate which sensors should be attacked. An example is given to show the effectiveness of the theoretical results.

In this paper, the quasi-consensus problem is investigated for a class of heterogeneous-switched nonlinear multiagent systems, in which both cooperation and competition interactions are considered simultaneously. By means of the Lyapunov function method, we show that quasi-consensus can be ensured for switched multiagent systems under the assumption that the activation time of cooperation interactions is sufficiently large. Moreover, a new Lyapunov function is considered to provide the lower and upper bounds of switching intervals explicitly. Thus, these bounds can be used to obtain less conservative stability results of switched systems. Furthermore, the established results are specialized to both the traditional consensus case and the stability of linear-switched systems. Finally, simulations are given to illustrate the theoretical results derived in this paper.

In this article, we consider the security problem for the consensus-based distributed state estimation. To resist the malicious attacker who can falsify the data transmitted through the wireless channel, each node equips with an attack defender, which is based on the measurement of its built-in sensor. Under the stealthy attack, which can deceive the defender, we investigate the resilience and convergence of the distributed estimation in two different attack scenarios. For the attack with enough communication resources, we provide a sufficient condition of the optimal attack to quantify the maximum estimation performance degradation. We also analyze the resilience of the worst case distributed estimation caused by the attacker. For the attack with limited resources, the optimal Kalman gain for each node is derived to maximize its estimation performance under the attack. We also give a sufficient condition to guarantee the convergence of the distributed estimation in this case. Finally, numerical simulations are provided to illustrate the effect of the defender on guaranteeing the resilience of sensor networks against attacks.

This article presents a secure communication scheme based on the quantized synchronization of master-slave neural networks under an event-triggered strategy. First, a dynamic event-triggered strategy is proposed based on a quantized output feedback, for which a quantized output feedback controller is formed. Second, theoretical criteria are derived to ensure the bounded synchronization of master-slave neural networks. With these criteria, an explicit upper bound is given for the synchronization error. Sufficient conditions are also provided on the existence of quantized output feedback controllers. A Chua's circuit is chosen to illustrate the effectiveness of our theoretical results. Third, a secure communication scheme is presented based on the synchronization of master-slave neural networks by combining the basic principle of cryptology. Then, a secure image communication is studied to verify the feasibility and security performance of the proposed secure communication scheme. The impact of the quantization level and the event-triggered control (ETC) on image decryption is investigated through experiments.

In the presence of malicious denial-of-service (DoS) attacks, this paper investigates the tracking control of mobile robots. Some explicit characterizations are presented for frequency and duration properties of malicious DoS attacks. A hybrid model is established by considering malicious DoS attacks and event-triggering control. The significance of this paper is to develop a set of event-triggering conditions to ensure the tracking convergence. As well, these conditions can guarantee the existence of uniformly positively minimum interval between any two successive transmissions. Finally, a practical experiment is presented by considering the tracking control of an Amigobot mobile robot over a wireless network with DoS attacks, which verifies the effectiveness of the derived results.

Due to properties of flexibility, adaptiveness, error tolerance, and time-determinism performance, the FlexRay communication protocol has been widely used to investigate robot systems and new generation of automobiles. In this paper, with the FlexRay communication protocol, the tracking problem of a class of cyber-physical systems are investigated by developing a general hybrid model, in which an emulation controller is utilized. Based on the proposed hybrid model, some sufficient conditions are established to guarantee the convergence of tracking errors. Then, the maximum allowable transmission interval (MATI) of the static/dynamic segment is obtained with a more general formula than the ones in some the previous works. The obtained MATI over the FlexRay communication network can be adjusted via the appropriate length of the static/dynamic segment, which reflects the flexibility of FlexRay. Finally, the results are verified by considering the tracking problem of a single-link robot arm system as well as the stabilization of a batch reactor system.

This paper is concerned with the stabilization problem of type-2 fuzzy systems with network-induced packet losses. By regarding the packet lost process as an unstable mode of a switched system, the stability of the system is then guaranteed with the aid of the mode-dependent average dwell time approach in the sense of the slow and fast switching. The discrete-time multiple discontinuous Lyapunov function is also utilized for the analysis. Two sufficient conditions regarding the stability and the stabilization of the system are proposed. The state-feedback matrices can be then calculated from the conditions to ensure the criterion that the packet-loss rate is no larger than a specific constant. Two practical examples are given to illustrate the feasibility and effectiveness of the proposed method.

This paper tackles the high-dimensional robust order scheduling problem. A multi-objective evolutionary algorithm called constrained nondominated sorting differential evolution based on decision variable classification is developed to search for robust order schedules. The decision variables are classified into highly and weakly robustness-related variables according to their contributions to the robustness of candidate solutions. The experimental results reveal that the performance of robust evolutionary optimization can be greatly improved via analyzing the properties of decision variables and then decomposing the high-dimensional robust optimization problem. It is also unveiled that the order scheduling is greatly affected by the uncertain daily production quantities. The robust order schedules are able to provide more information on earliness/tardiness of the orders, which enhances the flexibility of the production.

This paper aims to investigate the semiglobal consensus problem of a class of linear discrete-time multi-agent systems with distributed impulsive control. First, a novel distributed impulsive strategy is presented for achieving semiglobal consensus by considering low-gain feedback control, in which the magnitude of the impulsive protocol converges to zero when the low-gain parameter tends to zero. By utilizing the Lyapunov function and low-gain theory, a parametric discrete-time Riccati equation is considered for calculating impulsive control gain matrix. Furthermore, based on the low-and-high-gain approach, another distributed impulsive strategy is proposed. Moreover, two algorithms are then presented to derive the control gain matrices of distributed impulsive protocols for meeting different performance requirements. Subsequently, the applicability of proposed strategies is validated through two examples.

In this technical note, the event-based leader-following control is investigated for a multiple non-identical Euler-Lagrange system with a directed graph. An event-triggered observer is provided to estimate the state of a dynamic leader. To avoid continuous interaction among agents, a broadcasting communication mechanism based on models is utilized. Combining with the event-triggered observer, a distributed adaptive tracking control is presented for multiple lagrangian systems with uncertainties such that all nodes asymptotically follow a virtual leader governed by a linear model. Finally, a simulation with 2-degree-of-freedom manipulators is presented to verify the usefulness of our proposed methods.

This study focuses on the classification and pathological status monitoring of hyper/hypo-calcemia in the calcium regulatory system. By utilizing the Independent Component Analysis (ICA) mixture model, samples from healthy patients are collected, diagnosed, and subsequently classified according to their underlying behaviors, characteristics, and mechanisms. Then, a Just-in-Time Learning (JITL) has been employed in order to estimate the diseased status dynamically. In terms of JITL, for the purpose of the construction of an appropriate similarity index to identify relevant datasets, a novel similarity index based on the ICA mixture model is proposed in this paper to improve online model quality. The validity and effectiveness of the proposed approach have been demonstrated by applying it to the calcium regulatory system under various hypocalcemic and hypercalcemic diseased conditions.

Order scheduling is of vital importance in discrete manufacturing industries. This paper takes fashion industry as an example and discusses the robust order scheduling problem in the fashion industry. In the fashion industry, order scheduling focuses on the assignment of production orders to appropriate production lines. In reality, before a new order can be put into production, a series of activities known as preproduction events need to be completed. In addition, in real production process, owing to various uncertainties, the daily production quantity of each order is not always as expected. In this paper, by considering the preproduction events and the uncertainties in the daily production quantity, robust order scheduling problems in the fashion industry are investigated with the aid of a multiobjective evolutionary algorithm called nondominated sorting adaptive differential evolution (NSJADE). The experimental results illustrate that it is of paramount importance to consider preproduction events in order scheduling problems in the fashion industry. We also unveil that the existence of the uncertainties in the daily production quantity heavily affects the order scheduling.

This paper is concerned with the consensus of multiple Euler-Lagrange systems with time-varying sampled-data control. Different from traditional sampled-data strategies, a time-varying sampled-data strategy is developed to realize the consensus of multiple Euler-lagrange systems, in which a function that can be distinct at different sampling instants is proposed to modulate the sampling interval. In addition, a new definition of average sampling interval, which is parallel to the average dwell time in switching control or average impulsive interval in impulsive control, is proposed to characterize the number of the updating of the sampling controller during some certain interval. The proposed average sampling interval makes our sampled-data strategy more suitable for a wide range of sampling signals. By utilizing the comparison principle, a sufficient criterion is obtained to guarantee the consensus of multiple Euler-Lagrange systems. The sufficient criterion is heavily dependent on the actual control duration time and the communication graph. Finally, a simulation example is presented to verify the applicability of the proposed results.

The model approximation problem is studied in this paper for switched genetic regulatory networks (GRNs) with time-varying delays. We focus on constructing a reduced-order model to approximate the high-order GRNs considered under the switching signal subject to certain constraints, such that the approximation error system between the original and reduced-order systems is exponentially stable with a disturbance attenuation performance. The stability conditions and the disturbance attenuation performance are established by utilizing two integral inequality bounding techniques and the average dwell-time method for the approximation error system. Then, the solvability conditions for the reduced-order models for the GRNs are also established using the projection method. Furthermore, the model approximation problem can be transferred into a sequential minimization problem that is subject to linear matrix inequality constraints by using the cone complementarity algorithm. Finally, several examples are provided to illustrate the effectiveness and the advantages of the proposed methods.

An online monitoring method, which aims to deal with the high order non-Gaussian characteristics in chemical process, is proposed in this paper. In the framework of the proposed method, kernel based independent component analysis is utilized to identify the operation status of the chemical process and statistics pattern analysis is employed to combine with independent component analysis to extract high order information from the process so as to improve the monitoring performance. The modified statistics pattern analysis introduces the Mahalanobis distance into statistics pattern to analyze the inner structure relationship between the samples. Then, the validity and effectiveness of our proposed method is illustrated by applying to a representative non-Gaussian process, Continuous Stirred Tank Reactor (CSTR). The results show that the proposed method has its advantages when compared to other conventional Eigen-decomposition monitoring algorithms.

In this work, the control problem for type-2 T-S fuzzy system with packet dropouts is investigated by modeling the system as a switched system with an unstable subsystem. The mode-dependent average dwell time approach in both slow and fast switching sense is utilized for the analysis and synthesis. A sufficient condition is given by ensuring the packet loss rate no bigger than the specific fast switching mode-dependent average dwell time (MDADT) and the corresponding feedback matrices are obtained. Several simulation results illustrate the feasibility and effectiveness of the proposed method and the priority of the type-2 fuzzy system on describing some nonlinear systems.

This paper examines the tracking property of nonlinear delayed multi-agent systems with impulsive effects. The strengths and locations of the stabilizing impulses, as well as the number of the controlled nodes are all assumed to be time-varying. Some sufficient criteria are established such that the considered system can exponentially track the dynamical reference state based on the given impulsive control algorithm. Finally, the dynamic system of robotic arms modeled by nodes is presented to verify that the established results are of not only theoretical validity but also practical effectiveness.

H∞consensus is investigated for multi-agent systems with linear dynamics and impulsive effects in this paper. First of all, by considering the effects of distributed impulses, the model of linear multi-agent systems with impulsive effects and external disturbances has been obtained. Then, in view of the average impulsive interval and the Lyapunov stability theory, an algorithm has been given to solve the H∞impulsive consensus for the linear multi-agent system under consideration. The theoretical results are verified by an example in the final.

In this paper, we consider the privacy preserving problem in an agreement network under interception attacks. First, we introduce a consensus protocol with privacy preserving, where each node hides their initial states into a set of random sequences, and then injects the sequences into the process of consensus. Second, we assume that an attacker with limited power can intercept the data transmitted on the edges. Aiming at the case when the privacy preserving protocol fails, we propose an index to measure the degree of network privacy leakage. In the ring and small-world network, we find an optimal attacking strategy for the attacker to maximize the probability of the privacy leakage from the perspective of the attacker. Finally, we verify all the derived theoretical results by simulations.

Boolean networks serve a powerful tool in analysis of genetic regulatory networks since it emphasizes the fundamental principles and establishes a nature framework for capturing the dynamics of regulation of cellular states. In this paper, the robust reachability of Boolean control networks is investigated by means of semi-tensor product. Necessary and sufficient conditions for the robust reachability of Boolean control networks are provided, in which control inputs relying on disturbances or not are considered, respectively. Besides, the corresponding control algorithms are developed for these two cases. A reduced model of the lac operon in the Escherichia coli is presented to show the effectiveness of the presented results.

Differential evolution (DE) is a simple but powerful evolutionary algorithm, which has been widely and successfully used in various areas. In this paper, an event-triggered impulsive (ETI) control scheme is introduced to improve the performance of DE. Impulsive control (IPC), the concept of which derives from control theory, aims at regulating the states of a network by instantly adjusting the states of a fraction of nodes at certain instants, and these instants are determined by event-triggered mechanism (ETM). By introducing IPC and ETM into DE, we hope to change the search performance of the population in a positive way after revising the positions of some individuals at certain moments. At the end of each generation, the IPC operation is triggered when the update rate of the population declines or equals to zero. In detail, inspired by the concepts of IPC, two types of impulses are presented within the framework of DE in this paper: 1) stabilizing impulses and 2) destabilizing impulses. Stabilizing impulses help the individuals with lower rankings instantly move to a desired state determined by the individuals with better fitness values. Destabilizing impulses randomly alter the positions of inferior individuals within the range of the current population. By means of intelligently modifying the positions of a part of individuals with these two kinds of impulses, both exploitation and exploration abilities of the whole population can be meliorated. In addition, the proposed ETI is flexible to be incorporated into several state-of-the-art DE variants. Experimental results over the IEEE Congress on Evolutionary Computation (CEC) 2014 benchmark functions exhibit that the developed scheme is simple yet effective, which significantly improves the performance of the considered DE algorithms.

In this paper, the consensus problem is studied for a class of multi-agent systems with sampled data and packet losses, where random and deterministic packet losses are considered, respectively. For random packet losses, a Bernoulli-distributed white sequence is used to describe packet dropouts among agents in a stochastic way. For deterministic packet losses, a switched system with stable and unstable subsystems is employed to model packet dropouts in a deterministic way. The purpose of this paper is to derive consensus criteria, such that linear multi-agent systems with sampled-data and packet losses can reach consensus. By means of the Lyapunov function approach and the decomposition method, the design problem of a distributed controller is solved in terms of convex optimization. The interplay among the allowable bound of the sampling interval, the probability of random packet losses, and the rate of deterministic packet losses are explicitly derived to characterize consensus conditions. The obtained criteria are closely related to the maximum eigenvalue of the Laplacian matrix versus the second minimum eigenvalue of the Laplacian matrix, which reveals the intrinsic effect of communication topologies on consensus performance. Finally, simulations are given to show the effectiveness of the proposed results.In this paper, the consensus problem is studied for a class of multi-agent systems with sampled data and packet losses, where random and deterministic packet losses are considered, respectively. For random packet losses, a Bernoulli-distributed white sequence is used to describe packet dropouts among agents in a stochastic way. For deterministic packet losses, a switched system with stable and unstable subsystems is employed to model packet dropouts in a deterministic way. The purpose of this paper is to derive consensus criteria, such that linear multi-agent systems with sampled-data and packet losses can reach consensus. By means of the Lyapunov function approach and the decomposition method, the design problem of a distributed controller is solved in terms of convex optimization. The interplay among the allowable bound of the sampling interval, the probability of random packet losses, and the rate of deterministic packet losses are explicitly derived to characterize consensus conditions. The obtained criteria are closely related to the maximum eigenvalue of the Laplacian matrix versus the second minimum eigenvalue of the Laplacian matrix, which reveals the intrinsic effect of communication topologies on consensus performance. Finally, simulations are given to show the effectiveness of the proposed results.

In this brief, a novel impulsive control has been proposed for consensus problems of discrete-time multi-agent systems. Utilizing the Lyapunov technique, a parametric discrete-time Riccati equation has been obtained in order to design impulsive controller. The derived criteria show that such impulsive control relies on the proper value of designed parameters and the upper bound of impulsive intervals. Finally, the applicability of proposed strategy is given through a numerical example.

In this paper, consensus of multi-agent systems containing linear self dynamics is investigated. By considering packet losses, a sampled data protocol with packet losses is considered. A switched systems including stable modes and unstable modes is considered here to describe packet losses. By using the contradiction method and the Lyapunov function method, a sufficient condition on consensus of the multi-agent system under consideration is obtained, where the controller can be solved in the form of convex optimization approaches. In addition, the maximum sampling interval is also given.

This paper is concerned with pth moment exponential stability of stochastic delayed neural networks. By using the Lyapunov function method, some stability criteria of impulsive stochastic delayed systems are obtained, and these results are applied to the study on the stability criteria of stochastic delayed neural networks. It is shown that if the continuous stochastic delayed neural network is stable and the impulsive effects are destabilizing, then the stochastic delayed neural network is exponentially stable with respect to a lower bound of the impulsive interval. Moreover, if the continuous stochastic delayed neural network is not stable, the impulsive effects can successfully stabilize the delayed neural network for a given upper bound of the impulsive interval. One example is presented to demonstrate the usefulness of the proposed results.

This paper addresses robust multiobjective identification of driver nodes in the neuronal network of a cat's brain, in which uncertainties in determination of driver nodes and control gains are considered. A framework for robust multiobjective controllability is proposed by introducing interval uncertainties and optimization algorithms. By appropriate definitions of robust multiobjective controllability, a robust nondominated sorting adaptive differential evolution (NSJaDE) is presented by means of the nondominated sorting mechanism and the adaptive differential evolution (JaDE). The simulation experimental results illustrate the satisfactory performance of NSJaDE for robust multiobjective controllability, in comparison with six statistical methods and two multiobjective evolutionary algorithms (MOEAs): nondominated sorting genetic algorithms II (NSGA-II) and nondominated sorting composite differential evolution. It is revealed that the existence of uncertainties in choosing driver nodes and designing control gains heavily affects the controllability of neuronal networks. We also unveil that driver nodes play a more drastic role than control gains in robust controllability. The developed NSJaDE and obtained results will shed light on the understanding of robustness in controlling realistic complex networks such as transportation networks, power grid networks, biological networks, etc.

self-triggered control of networked systems. The system considered here includes parameter uncertainties, packet dropouts, and time delays. The time delay is described in a stochastic way, which takes a value from a given finite set. In order to compensate for the existence of deterministic packet dropouts, a new self-triggered control scheme is proposed. The main feature of the proposed self-triggered control strategy is that the next control task is predicted based on the self-triggered technique, in which the predicted event interval is divided equally for the sake of packet dropouts. The triggered condition is developed to ensure the stability of the uncertain sampled system by utilizing an uncertain algebraic Riccati equation and the comparison principle. Finally, an example of the inverted pendulum of a cart is provided to illustrate the effectiveness of the proposed results.

In this paper, the distributed exponential consensus of stochastic delayed multi-agent systems with nonlinear dynamics is investigated under asynchronous switching. The asynchronous switching considered here is to account for the time of identifying the active modes of multi-agent systems. After receipt of confirmation of mode's switching, the matched controller can be applied, which means that the switching time of the matched controller in each node usually lags behind that of system switching. In order to handle the coexistence of switched signals and stochastic disturbances, a comparison principle of stochastic switched delayed systems is first proved. By means of this extended comparison principle, several easy to verified conditions for the existence of an asynchronously switched distributed controller are derived such that stochastic delayed multi-agent systems with asynchronous switching and nonlinear dynamics can achieve global exponential consensus. Two examples are given to illustrate the effectiveness of the proposed method.

Differential evolution (DE) is a simple but powerful evolutionary algorithm, which has been widely and successfully used in many areas. In this paper, an impulsive control method is introduced to the DE framework, and the impulsive DE (IpDE) is proposed for improving the performance of DE. The impulsive control operation instantly moves the individuals which do not update for continuous pre-defined generations to a desired state based on the individuals with better fitness values in the current population. This way, IpDE controls individuals' positions in the space domain according to the stagnation status of the population. In order to validate the effectiveness of IpDE, the presented framework is applied to the original DE algorithms, as well as several state-of-the-art DE variants. Experimental results exhibit that IpDE is a simple but effective framework to improve the performance of the studied DE algorithms.

This paper aims to discuss the H∞control problem of nonlinear stochastic switched systems in case where both global asymptotically stable in the mean (GASiM) subsystems and unstable subsystems coexist. An average dwell time (ADT) scheme is established to show us that the system is GASiM, if the activation time of GASiM subsystems is comparatively longer than that of unstable ones. Further, some conditions upon the H∞performance of the stochastic switched system are provided. The effectiveness of the proposed result is illustrated by a simulation example.

In this paper, the stability problem is studied for a class of stochastic neural networks (NNs) with local impulsive effects. The impulsive effects considered can be not only nonidentical in different dimensions of the system state but also various at distinct impulsive instants. Hence, the impulses here can encompass several typical impulses in NNs. The aim of this paper is to derive stability criteria such that stochastic NNs with local impulsive effects are exponentially stable in mean square. By means of the mathematical induction method, several easy-to-check conditions are obtained to ensure the mean square stability of NNs. Three examples are given to show the effectiveness of the proposed stability criterion.

This paper investigates the problem of robust model predictive control (RMPC) with saturations and packet dropouts. In this model, polytopic uncertainties are adopted to describe the inconsistency arising from the discretization process of sampling, while the occurrence probabilities of packet dropouts are time-varying and saturations are taken into account to describe input and output signals. The problem of exponential RMPC with saturations and packet dropouts is solved and characterized by a convex optimization problem. The developed results of RMPC are then applied to networked flotation processes, which are made up of three layers: direct control layer, set-point control layer, and optimization layer. The RMPC is used for compensating the output information from the optimization layer to the direct control layer such that the desired economic objective can be achieved. Simulations are presented to show the effectiveness of the proposed method.

Control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with the constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that as the constraints become more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide useful insights into developing new techniques to control a realistic complex network efficiently.

This paper is concerned with the problem of pinning synchronization of nonlinear dynamical networks with multiple stochastic disturbances. Two kinds of pinning schemes are considered: 1) pinned nodes are fixed along the time evolution and 2) pinned nodes are switched from time to time according to a set of Bernoulli stochastic variables. Using Lyapunov function methods and stochastic analysis techniques, several easily verifiable criteria are derived for the problem of pinning distributed synchronization. For the case of fixed pinned nodes, a novel mixed optimization method is developed to select the pinned nodes and find feasible solutions, which is composed of a traditional convex optimization method and a constraint optimization evolutionary algorithm. For the case of switching pinning scheme, upper bounds of the convergence rate and the mean control gain are obtained theoretically. Simulation examples are provided to show the advantages of our proposed optimization method over previous ones and verify the effectiveness of the obtained results.

In this paper, the stochastic synchronization problem is studied for a class of delayed dynamical networks under delayed impulsive control. Different from the existing results on the synchronization of dynamical networks under impulsive control, impulsive input delays are considered in our model. By assuming that the impulsive intervals belong to a certain interval and using the mathematical induction method, several conditions are derived to guarantee that complex networks are exponentially synchronized in mean square. The derived conditions reveal that the frequency of impulsive occurrence, impulsive input delays, and stochastic perturbations can heavily affect the synchronization performance. A control algorithm is then presented for synchronizing stochastic dynamical networks with delayed synchronizing impulses. Finally, two examples are given to demonstrate the effectiveness of the proposed approach.

In this paper, we investigate the multiobjective identification of controlling areas in the neuronal network of a cat's brain by considering two measures of controllability simultaneously. By utilizing nondominated sorting mechanisms and composite differential evolution (CoDE), a reference-point-based nondominated sorting composite differential evolution (RP-NSCDE) is developed to tackle the multiobjective identification of controlling areas in the neuronal network. The proposed RP-NSCDE shows its promising performance in terms of accuracy and convergence speed, in comparison to nondominated sorting genetic algorithms II. The proposed method is also compared with other representative statistical methods in the complex network theory, single objective, and constraint optimization methods to illustrate its effectiveness and reliability. It is shown that there exists a tradeoff between minimizing two objectives, and therefore pareto fronts (PFs) can be plotted. The developed approaches and findings can also be applied to coordination control of various kinds of real-world complex networks including biological networks and social networks, and so on.

In this paper, the distributed synchronization problem of networks of agent systems with controllers and nonlinearities subject to Bernoulli switchings is investigated. Controllers and adaptive updating laws injected in each vertex of networks depend on the state information of its neighborhood. Three sets of Bernoulli stochastic variables are introduced to describe the occurrence probabilities of distributed adaptive controllers, updating laws and nonlinearities, respectively. By the Lyapunov functions method, we show that the distributed synchronization of networks composed of agent systems with multiple randomly occurring nonlinearities, multiple randomly occurring controllers, and multiple randomly occurring updating laws can be achieved in mean square under certain criteria. The conditions derived in this paper can be solved by semi-definite programming. Moreover, by mathematical analysis, we find that the coupling strength, the probabilities of the Bernoulli stochastic variables, and the form of nonlinearities have great impacts on the convergence speed and the terminal control strength. The synchronization criteria and the observed phenomena are demonstrated by several numerical simulation examples. In addition, the advantage of distributed adaptive controllers over conventional adaptive controllers is illustrated.

In this paper, we study the distributed synchronization and pinning distributed synchronization of stochastic coupled neural networks via randomly occurring control. Two Bernoulli stochastic variables are used to describe the occurrences of distributed adaptive control and updating law according to certain probabilities. Both distributed adaptive control and updating law for each vertex in a network depend on state information on each vertex's neighborhood. By constructing appropriate Lyapunov functions and employing stochastic analysis techniques, we prove that the distributed synchronization and the distributed pinning synchronization of stochastic complex networks can be achieved in mean square. Additionally, randomly occurring distributed control is compared with periodically intermittent control. It is revealed that, although randomly occurring control is an intermediate method among the three types of control in terms of control costs and convergence rates, it has fewer restrictions to implement and can be more easily applied in practice than periodically intermittent control.

This paper investigates the synchronization problem of coupled switched neural networks (SNNs) with mode-dependent impulsive effects and time delays. The main feature of mode-dependent impulsive effects is that impulsive effects can exist not only at the instants coinciding with mode switching but also at the instants when there is no system switching. The impulses considered here include those that suppress synchronization or enhance synchronization. Based on switching analysis techniques and the comparison principle, the exponential synchronization criteria are derived for coupled delayed SNNs with mode-dependent impulsive effects. Finally, simulations are provided to illustrate the effectiveness of the results.

Controlling regions in cortical networks, which serve as key nodes to control the dynamics of networks to a desired state, can be detected by minimizing the eigenratio R and the maximum imaginary part \sigma of an extended connection matrix. Until now, optimal selection of the set of controlling regions is still an open problem and this paper represents the first attempt to include two measures of controllability into one unified framework. The detection problem of controlling regions in cortical networks is converted into a constrained optimization problem (COP), where the objective function R is minimized and \sigma is regarded as a constraint. Then, the detection of controlling regions of a weighted and directed complex network (e.g., a cortical network of a cat), is thoroughly investigated. The controlling regions of cortical networks are successfully detected by means of an improved dynamic hybrid framework (IDyHF). Our experiments verify that the proposed IDyHF outperforms two recently developed evolutionary computation methods in constrained optimization field and some traditional methods in control theory as well as graph theory. Based on the IDyHF, the controlling regions are detected in a microscopic and macroscopic way. Our results unveil the dependence of controlling regions on the number of driver nodes l and the constraint r. The controlling regions are largely selected from the regions with a large in-degree and a small out-degree. When r=+ \infty, there exists a concave shape of the mean degrees of the driver nodes, i.e., the regions with a large degree are of great importance to the control of the networks when l is small and the regions with a small degree are helpful to control the networks when l increases. When r=0, the mean degrees of the driver nodes increase as a function of l. We find that controlling \sigma is becoming more important in controlling a cortical network with increasing l. The methods and results of detecting controlling regions in this paper would promote the coordination and information consensus of various kinds of real-world complex networks including transportation networks, genetic regulatory networks, and social networks, etc.

Maximizing the controllability of complex networks by selecting appropriate nodes and designing suitable control gains is an effective way to control distributed complex networks. In this paper, some novel particle swarm optimization (PSO) approaches are developed to enhance the controllability of distributed networks. The proposed PSO algorithm is combined with a global search scheme and a modified simulated binary crossover (MSBX). In addition, the node importance-based method is introduced to study the controllability of distributed complex networks. A set of experiments show that the PSO with the global search and the MSBX (PSO-GSBX) can outperform some well-known evolutionary algorithms and pinning schemes. Following the PSO-GSBX approach, some interesting findings about pinned nodes, coupling strengths and the eigenvalues for enhancing the controllability of distributed networks are revealed. The obtained results and methods are applied in unmanned aerial vehicle (UAV) coordination to show their effectiveness. These findings will help to understand controllability of complex networks and can be applied in control science and industrial system.

In this paper, the problem of adaptive synchronization for a class of stochastic neural networks (SNNs) which involve both mixed delays and Markovian jumping parameters is investigated. The mixed delays comprise the time-varying delays and distributed delays, both of which are mode-dependent. The stochastic perturbations are described in terms of Browian motion. By the adaptive feedback technique, several sufficient criteria have been proposed to ensure the synchronization of SNNs in mean square. Moreover, the proposed adaptive feedback scheme is applied to the secure communication. Finally, the corresponding simulation results are given to demonstrate the usefulness of the main results obtained.