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.

This paper studies secure consensus of nonlinear multi-agent systems (MASs) affected by sequential scaling attacks and communication delays, employing an event-triggered delayed impulsive control strategy. Specifically, it considers sequential scaling attacks occurring within the communication channels between agents, while the communication delays arise in the controller-actuator pair. First, the attack properties include attack duration and attack frequency are defined. Then, a delayed impulsive control protocol that depends exclusively on neighboring agents’ state at event-triggered time instant is proposed to eliminate continuous control behavior. A sampled-data-based event-triggered mechanism (ETM) is introduced that uses the Lyapunov function at impulse time instant to determine communication intervals between agents, effectively reducing the need for continuous event detection. Furthermore, sufficient conditions for secure consensus of MASs are established, along with guidelines for designing event-triggering parameters. Finally, the effectiveness of the proposed approach is demonstrated via two numerical simulations.

This article investigates the problem of distributed Nash equilibrium (NE) seeking in noncooperative games within a directed communication network. For promoting the efficiency of communication among players, a gradient-based dynamic event-triggered mechanism is proposed, where Zeno behavior is excluded. Moreover, based on the Lyapunov stability theory, we derive sufficient conditions for exponential convergence and demonstrate that the seeking strategy proposed facilitates the convergence of players' actions toward the NE. To illustrate the effectiveness of the proposed strategies, simulation results are presented in a system consisting of five agents.

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.

Many industrial parks, which are connected to the main grid, have integrated renewable energy to reduce carbon emission for achieving the goal of Industry 5.0. However, the optimal scheduling is challenging due to fluctuations in renewable energy generation. Hydrogen, which plays an important role in the future development of the power grid in Industry 5.0, offers an attractive option to coordinate with the batteries. This work focuses on the day-ahead scheduling of a hydrogen-based microgrid for an industrial park. A day-ahead scheduling model is established by taking into consideration the detailed nonlinear energy conversion behavior of the electrolyzer and fuel cell, as well as the two-timescale property of a battery energy storage system (BESS) and the hydrogen system, including an electrolyzer, a hydrogen energy storage system (HESS), and a fuel cell. Note that the optimization problem is a mixed integer nonlinear programming, which is challenging to be solved. A novel multilearning rate reinforcement learning algorithm is proposed and its convergence is also proved based on two-timescale stochastic approximation theory. Simulation results, based on real-world traces in Belgium at a 15-min resolution, are presented, which shows that the proposed method has a higher reward, lower-operating costs and less computing time. It is also found that the shorter scheduling period for the BESS can lead to reduced operating costs by decreasing the required purchasing power and the renewable energy curtailment power.

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.

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.

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.

This article is concerned with the secure fully distributed event-triggered consensus problem of general linear multiagent systems subject to distributed sequential scaling (DSS) attacks. First, a generic DSS attack model is proposed, which enables different attack strategies in terms of scaling factors, attack frequency, and duration to be incorporated in various communication channels. Different from existing attack models, the DSS attack is a kind of scaling attacks in which attack sequences are characterized by the sequential attacks and with distributed attack strategies for different attack objectives. To resist the adverse effects of such DSS attacks and further reduce the unnecessary communication consumption of each communication channel, a channel-based dynamic event-triggered mechanism is next presented. Moreover, a fully distributed event-triggered consensus control protocol is developed such that the dependence of any global information of the network topology can be eliminated. Formal analysis criteria on the asymptotic convergence of the resultant consensus errors and Zeno-freeness are then derived, where the relationship of the triggering parameters, distributed scaling factors, and attack constraints is explicitly expressed. Furthermore, an offline algorithm without any global information is provided to determine both the adaptive consensus protocol gain matrices and the triggering parameters. Therefore, the parameters calculated by this algorithm are applicable to multiagent systems of different scales, which also confirms the flexibility and scalability of the proposed fully distributed event-triggered consensus control protocol. Finally, two simulation examples involving different scales of intelligent vehicles are given to validate the efficacy of the obtained theoretical results.

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.

Distributed optimization achieves a minimized objective function through collaboration among distributed agents. Considering limited communication capabilities and privacy concerns, this article proposes a dynamic event-triggered differentially private gradient-tracking algorithm for distributed optimization. The communication requirement is reduced by event triggering, while the$\epsilon$-differential privacy is guaranteed by perturbations on states and the tracking of the average gradient. The convergence point is uniquely determined by the noise injected to the tracking. Sufficient conditions for stepsizes are established theoretically to guarantee the convergence in mean and almost surely. Moreover, the theoretical privacy level is rigorously obtained and the positive effect of the event-triggered communication on the privacy is also discussed. Simulations are conducted for the classification of the dataset on the stability of a 4-node star power system to verify the theoretical findings.

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 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.

Inferential modeling has been of significance for modern manufacturing in estimating the quality-related process variables. As an effective inferential model, probabilistic slow feature analysis (PSFA) has gained attention in regression tasks to interpret dynamic properties with a slowness preference. However, PSFA is often challenged by the nonlinear sequential data due to its linear state-space structure. In this article, a new nonlinear extension of PSFA is proposed under the deep learning framework to enhance the dynamic feature extraction with limited labels, incorporating variational inference and Monte Carlo inference to derive the objective function. The proposed model considers the relevance of inputs with outputs as the input weights to upgrade prediction performance. The proposed model is verified through an industrial hydrocracking process to predict diesel yield with missing labels ranged from 0% to 50%, and the root mean squared error is reduced by at least 8.78% compared to PSFA.

Accompanied with the increasing popularity of linear regression approaches, most of the existing minimization problems are related with several convex measurements, e.g.,$\ell_1$/$\ell_2$/$\ell_{2,1}$-norm of a vector and$L_1$/$L_{2,1}$/Frobenius/nuclear norm of a matrix, where the regularized function and the loss function are usually studied for two objective terms case by case, respectively. To address this issue, this work combines these linear regression problems into a unified expression framework by employing an adaptive and flexible function, in which we need to choose different variable elements and adjust an inner parameter, properly. Besides this, they are equipped with some corresponding relationships and their interesting properties. Intuitively speaking, the proposed framework can generalize several traditional linear regression formulations and even more complex ones into an extended representation. For further optimizations, an iteratively re-weighted penalty solution (IRwPS) is devised without any inner loops, making the iteration programming easy to perform. Meanwhile, the theoretical results are provided for guaranteeing that the mathematical convergence analysis is solid and meaningful. Finally, by performing real-world applications in supervised, unsupervised, and semi-supervised tasks, numerical experiments are conducted to validate the theoretical properties and the superiority over some of the state-of-the-art.

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.

Due to the wide range of time scales involved in the ordinary differential equations (ODEs) describing chemical reaction kinetics, multidimensional numerical simulation of chemical reactive flows using detailed combustion mechanisms is computationally expensive. To confront this issue, this article presents an economic data-driven tabulation algorithm for fast combustion chemistry integration. It uses the recurrent neural networks (RNNs) to construct the tabulation from a series of current and past states to the next state, which takes full advantage of RNN in handling long-term dependencies of time series data. The training data are first generated from direct numerical integrations to form an initial state space, which is divided into several subregions by the K-means algorithm. The centroid of each cluster is also determined at the same time. Next, an Elman RNN is constructed in each of these subregions to approximate the expensive direct integration, in which the integration routine obtained from the centroid is regarded as the basis for a storing and retrieving solution to ODEs. Finally, the alpha-shape metrics with principal component analysis (PCA) are used to generate a set of reduced-order geometric constraints that characterize the applicable range of these RNN approximations. For online implementation, geometric constraints are frequently verified to determine which RNN network to be used to approximate the integration routine. The advantage of the proposed algorithm is to use a set of RNNs to replace the expensive direct integration, which allows to reduce both the memory consumption and computational cost. Numerical simulations of a H2/CO-air combustion process are performed to demonstrate the effectiveness of the proposed algorithm compared to the existing ODE solver.

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.

This paper studies the dynamic event-triggered tracking control of a nonholonomic mobile robot. Firstly, a non-holonomic dynamic model for a mobile robot is suggested. Then, a dynamic event-triggered control strategy with an auxiliary dynamic parameter is proposed for the mobile robot to track the reference trajectory. Moreover, it is proved that the triggering time sequence does not exhibit the Zeno behavior. Finally, the effectiveness of the proposed control mechanism is validated by a numerical example.

This paper explores the resilient design of petroleum refinery planning in response to disruption events. Based on an emerging quantitative measure of processing system resilience and three effective resilience enhancement strategies, a multi-objective two-stage adaptive robust mixed-integer fractional programming model is proposed to optimize the resilience economic objectives simultaneously under uncertainty of product price and the number of failed equipments after disruption events, which will guide the industrial production of the petroleum refining process.

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.

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the$\ell _{0}$-norm usually achieve better performance than the convex case (e.g.,$\ell _{1}$-norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Łojasiewica (KŁ) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones.

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.

Multiagent systems (MASs) provide an effective means for coordinating spatially distributed and networked agents (or nodes, subsystems) such that the desired cooperative tasks can be accomplished with promising reliability, manipulability, scalability, and efficiency. One key issue in the study of MASs is the design of distributed cooperative control protocol and algorithm that depend on only local and real-time information exchanges among interacting agents over networks. However, network-enabled information sharing and increasing connectivity in practical MASs present several attack factors for malicious adversaries, thereby rendering secure control of MASs fundamentally significant. This article provides a brief survey of systems and control technologies that have been available for addressing different secure control problems of MASs in the face of various malicious attacks. First, attacks on MASs are classified based on different configuration layers. Then, the existing attack models and strategies on communication layer and agent layer are systematically examined, respectively. Furthermore, some typical secure control techniques for MASs that have been employed to handle these attacks are surveyed. Finally, several challenging issues are envisioned for potential future research.

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.

Synchronization of a class of nonlinear singularly perturbed complex networks (SPCNs) with semi-Markov jump topologies and impulsive effects is studied in this article. A complex network with a kind of random switching topologies is considered, where the randomness is depicted by a semi-Markov chain. A method is put forward to obtain the upper bound of singularly perturbed parameter (SPP) with different coupling strengths, and the concept of average impulsive interval is introduced to regulate the frequency of impulses. By utilizing the SPP-dependent semi-Markovian Lyapunov function, some sufficient conditions are derived for achieving synchronization of an SPCN. The effectiveness and validity of the proposed synchronization strategy are verified by two numerical examples.

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.

This article presents a novel target detection method, which is named as circular target feature detection framework based on a deep convolutional neural network (DCNN). The central proposition of this method uses the optimized DCNN architecture to detect the target and locate the position of the circle accurately in the image field of view. In this article, a Hough transform based on threshold processing (HTP) is embedded into the optimized DCNN architecture, which calculates the center positions and radius of all circles by training the circular samples for each detected rectangular frame. It can efficiently identify small circular target materials in the industry and screen out unqualified particles. The experimental results show that the boundary information of the circles is obtained clearly from the complex noise background images, thereby accurately determining the location of the circle. It has some advantages over only using a specific circular recognition algorithm. We proposed the new study on HTP-DCNN, which has extremely high accuracy in the field of machine vision positioning with circles for industrial applications.

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.

This paper is concerned with event-triggered consensus of general linear multiagent systems (MASs) in leaderless and leader-following networks, respectively, in the framework of adaptive control. A distributed dynamic event-triggered strategy is first proposed, in which an auxiliary parameter is introduced for each agent to regulate its threshold dynamically. The time-varying threshold ensures less triggering instants, compared with the traditional static one. Then under the proposed event-triggered strategy, a distributed adaptive consensus protocol is formed including the updating law of the coupling strength for each agent. Some criteria are derived to guarantee leaderless or leader-following consensus for MASs with general linear dynamics, respectively. Moreover, it is proved that the triggering time sequences do not exhibit Zeno behavior. Finally, the effectiveness of the proposed dynamic event-triggered control mechanism combined with adaptive control is validated by two examples.

The papers in this special section examine smart process manufacturing that is driven by artificial intelligence (AI). As a fundamental industry, process industry mainly involves elementary raw material industries, such as petroleum, chemical, steel, nonferrous metal, and building. However, there are a series of problems existing in process industry such as inaccurate perception of industrial data, low production efficiency, high materials consumption and limitations in safety and environment protection. In order to solve these restriction problems, we must pursue the goal of efficient, green, and smart processes in manufacturing and marketing. On the other hand, artificial intelligence (AI) has powerful strengths in perception, knowledge representation, learning, reasoning and planning, so that it has been successfully utilized in diverse areas, such as autonomous vehicles and so on. It is promising to have deep and tight integration between artificial intelligence and process industry, to achieve “smart process industry”.

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.

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.

The deregulation of the electricity market enables residential customers to select suitable electricity retailing plans. This paper proposes a Bayesian hybrid collaborative filtering-based electricity plan recommender system (BHCF-EPRS), which is constructed in a two-stage model integrated with model-based and memory-based collaborative filtering methods. Bayesian inference is developed for missing feature estimation and user classification. Free from the requirements on total electricity use data and historical plan transaction data, the BHCF-EPRS can recommend suitable retailers and plans based on some easily obtainable features quantifying home appliance usage patterns. The BHCF-EPRS is verified to be a reliable recommender system with low error in full-ranking recommendation and high precision in top-N recommendation, which can improve the competitive operation of the electricity market.

Modern power grid is increasingly integrated with battery energy storage systems (BESSs). This paper deals with the problem of state-of-charge (SoC) balance control for multiple distributed BESSs in smart grid. The BESSs are expected to work cooperatively to not only fulfil the overall power requirement but also meet the constraints of the same relative SoC variation rate. To achieve this objective, a distributed SoC balance control approach is presented with event-triggered signal transmissions. It is designed with the dynamic average consensus (DAC) mechanism for parameter estimations. The DAC enables distributed control of each BESS through communicating with its neighboring BESSs. Different from traditional periodic signal transmission, the event-triggered signal transmission embedded in our approach allows each BESS to transmit signal to its neighboring BESSs only when needed, thus reducing the communication traffic. Theoretical lower bounds are established for consecutive interevent intervals such that the Zeno behavior is excluded. Case studies are conducted to demonstrate the effectiveness of the presented approach.

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.

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.

This paper is concerned with finite-time L2leader-follower consensus of networked Euler-Lagrange systems in the presence of external disturbances. A distributed finite-time L2control protocol is proposed by using backstepping design such that a group of follower agents modeled by Euler-Lagrange systems can follow a desired leader agent and achieve leader-follower consensus in finite time. Moreover, the finite-time L2gain is less than or equal to a prescribed value. A simulation example of a network composed of seven two-link manipulators is given to show the effectiveness of the theoretical results.

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 is concerned with sampled-data leader-following consensus of a group of agents with nonlinear characteristic. A distributed consensus protocol with probabilistic sampling in two sampling periods is proposed. First, a general consensus criterion is derived for multiagent systems under a directed graph. A number of results in several special cases without transmittal delays or with the deterministic sampling are obtained. Second, a dimension-reduced condition is obtained for multiagent systems under an undirected graph. It is shown that the leader-following consensus problem with stochastic sampling can be transferred into a master-slave synchronization problem with only one master system and two slave systems. The problem solving is independent of the number of agents, which greatly facilitates its application to large-scale networked agents. Third, the network design issue is further addressed, demonstrating the positive and active roles of the network structure in reaching consensus. Finally, two examples are given to verify the theoretical results.

In this brief, we propose a new data-driven model-free adaptive control (MFAC) method with dual radial basis function neural networks (RBFNNs) for a class of discrete-time nonlinear systems. The main novelty lies in that it provides a systematic design method for controller structure by the direct usage of I/O data, rather than using the first-principle model or offline identified plant model. The controller structure is determined by equivalent-dynamic-linearization representation of the ideal nonlinear controller, and the controller parameters are tuned by the pseudogradient information extracted from the I/O data of the plant, which can deal with the unknown nonlinear system. The stability of the closed-loop control system and the stability of the training process for RBFNNs are guaranteed by rigorous theoretical analysis. Meanwhile, the effectiveness and the applicability of the proposed method are further demonstrated by the numerical example and Aspen HYSYS simulation of distillation column in crude styrene produce process.

This paper is concerned with the synchronization of multiagent systems connected via different types of interactions, known as multilayer networks. Additive coupling and Markovian switching coupling are proposed to capture the layered connections with two kinds of mathematical models constructed. First, based on simultaneously diagonalization of multiple Laplacian matrices, a general criterion is derived, ensuring that the synchronization problem with additive coupling can be decoupled. Then, an alternative condition is presented, which is related to the number of layers, regardless of the number of agents. With the derived criteria, a concept of joint synchronization region is introduced and further discussed as a network design problem. Synchronization with Markovian switching layers is also analyzed in parallel, exemplified by some special cases of two-layer networks. Finally, a group of cellular neural networks coupled by two-layer connections are chosen to illustrate the effectiveness of the theoretical results.

Quasi-synchronization of heterogenous dynamic networks is studied by using impulsive control in this paper. The asymmetric network connections are considered. First, the weighted average state is introduced as the virtual leader. By defining the synchronization error between the virtual leader and the network node, impulsive quasi-synchronization is analyzed and a criterion is derived to ensure quasi-synchronization in the delayed heterogenous network. Then delayed networks with symmetric connections and delay-free networks are studied, respectively, with simpler conditions obtained. Numerical simulations demonstrate the effectiveness of the derived results.

This paper studies global synchronization between a heterogeneous dynamical network and a known target trajectory via distributed impulsive control. Synchronization with an error level, called quasi-synchronization, is analyzed by utilizing the time-varying Lyapunov function. Some sufficient quasi-synchronization conditions are presented and explicit expressions of error levels are derived. Furthermore, the effects of the pinning control matrix and the coupling strength are explored. Unlike the continuous pinning feedback control, it is shown that a large coupling strength will destroy synchronization in the present of impulsive controller, which is also verified by our simulations.

This paper proposes a novel scheme of nonuniform discretizetion-based control vector parameterization (ndCVP, for short) for dynamic optimization problems (DOPs) of industrial processes. In our ndCVP scheme, the time span is partitioned into a multitude of uneven intervals, and incremental time parameters are encoded, along with the control parameters, into the individual to be optimized. Our coding method can avoid handling complex ordinal constraints. It is proved that ndCVP is a natural generalization of uniform discretization-based control vector parameterization (udCVP). By integrating ndCVP into hybrid gradient particle swarm optimization (HGPSO), a new optimization method, named ndCVP-HGPSO for short, is formed. By application in four classic DOPs, simulation results show that ndCVP-HGPSO is able to achieve similar or even better performances with a small number of control intervals; while the computational overheads are acceptable. Furthermore, ndCVP and udCVP are compared in terms of two situations: given the same number of control intervals and given the same number of optimization variables. The results show that ndCVP can achieve better performance in most cases.

This study proposes an online monitoring technique for nonlinear multiple-mode problems in industrial processes. The contributions of the proposed technique are summarized as follows: 1) Lazy learning (LL), a new adaptive local modeling method, is introduced for multiple-mode process monitoring. In this method, multiple modes are separated and accurately modeled online, and the between-mode dynamic process is considered. 2) The modified receptor density algorithm (MRDA) exhibiting superior nonlinear ability is introduced to analyze the residuals between the actual system output and the model-predicted output. The simulation of the Tennessee Eastman process with multiple operation modes shows that compared with other techniques mentioned in this study, the proposed technique performs more accurately and is more suitable for nonlinear processes with multiple operation modes.

Model predictive control plays an important role in hierarchical control. It receives the set-point from real time optimization layer hourly and gives a dynamic control signal to basic control loops or the system in minutes. To solve the problem brought by the frequency difference in hierarchy design, the model predictive control layer is divided into two parts: steady-state target optimization and dynamic predictive control. The steady-state target optimization receives the set-points and recalculates the targets every moment before the dynamic predictive control executes. However, for the case that system property varies, the steady-state optimization will lose its feasibility in fixed model. In this paper, an steady-state model updating mechanism is proposed along with the adaptive predictive model updating mechanism. Simulation results on a two tank model show that the steady state target is re-optimized in real time, and good dynamic performance is achieved.

This paper investigates the problem of master-slave synchronization of two delayed Lur'e systems in the presence of parameter mismatches. First, by analyzing the corresponding synchronization error system, synchronization with an error level, which is referred to as quasi-synchronization, is established. Some delay-dependent quasi-synchronization criteria are derived. An estimation of the synchronization error bound is given, and an explicit expression of error levels is obtained. Second, sufficient conditions on the existence of feedback controllers under a predetermined error level are provided. The controller gains are obtained by solving a set of linear matrix inequalities. Finally, a delayed Chua's circuit is chosen to illustrate the effectiveness of the derived results.

The optimization of job-shop scheduling is very important because of its theoretical and practical significance. In this paper, a computationally effective algorithm of combining PSO with AIS for solving the minimum makespan problem of job-shop scheduling is proposed. In the particle swarm system, a novel concept for the distance and velocity of a particle is presented to pave the way for the job-shop scheduling problem. In the artificial immune system, the models of vaccination and receptor editing are designed to improve the immune performance. The proposed algorithm effectively exploits the capabilities of distributed and parallel computing of swarm intelligence approaches. The algorithm is examined by using a set of benchmark instances with various sizes and levels of hardness and is compared with other approaches reported in some existing literature works. The computational results validate the effectiveness of the proposed approach.

A new hybrid training method for Radial Basis Function (RBF) neural network is presented in this paper. The proposed methodology produces RBF neural network models based on specially designed Fuzzy C-means (FCM) and Fuzzy Immune Algorithm (FIA), which are used to auto-configure the structure of networks and obtain the model parameters. With the proposed method, the number of hidden layer neurons and cluster centers are automatically determined according to the given data; both the output weight values and cluster radii are calculated by fuzzy immune algorithm. Meanwhile, the wavelet de-noising technique is introduced to ensure the neural network performance. This learning approach is proved to be effective by applying the optimized RBF neural network in predicting of Mackey-Glass chaos time series and forecasting of Queensland electricity reference price from Australian National Electricity Market.