This paper investigates the adaptive group fault-tolerant time-varying formation tracking (GFTFT) problem for linear multiagent systems (MASs) subject to actuator faults and switching communication networks. To fulfill complex tasks, a novel multi-layer framework is proposed, organizing agents into leaders, informed followers, and uninformed followers across multiple subgroups. To this end, firstly, a fault-tolerant GFTFT protocol is proposed to relax the restrictions on the severity of actuator faults, thereby enhancing tolerance to unknown faults. Secondly, novel multiple asynchronous event-triggered mechanisms (MAETMs) are designed, which enhance coordination and transparency by implementing a layered triggering strategy with distinct functions for inter-layer and inter-group communication. Third, the connectivity condition is relaxed, requiring only joint connectivity of the graphs over time, which accommodates intermittently disconnected topologies. Finally, the simulation experiment is conducted to validate the theoretical results.

The close combination of information communication and power systems is an important feature of modern power systems, making them vulnerable to various cyber-attacks. In this paper, from the perspective of power system security, a method of false data injection (FDI) attack estimation and security defense is proposed to improve the security of multi-area load frequency control (LFC) systems. First, in the attack assumption, this paper removes the limitation of the bounded size of FDI attacks and requires only the first derivative to be bounded. Secondly, in order to estimate and defend against malicious FDI attacks, this paper proposes a novel attack estimation method based on an intermediate estimator and designs a defense control law to keep the system stable. Finally, a three-area IEEE 9-bus power system is used to verify the proposed algorithm.

In this paper, we consider the islanded microgrid restoration with communication link faults, where faults refer to certain classes of data manipulation attacks. To compensate for the impact of faults in communication links on microgrid stability, a distributed adaptive event-triggered compensator with adaptive parameters has been designed. This avoids the use of global information and reduces communication redundancy. To achieve voltage and frequency recovery, an distributed secondary control strategy is proposed. Using Lyapunov stability theorem, it is proved that the proposed method is effective in counteracting the primary control bias that exists in islanded microgrids in the presence of communication link faults and limited computational resources. The main advantage of our method is the scalability and resilience. Finally, the paper presents the results of a simulation test of an islanded microgrid consisting of four DGs with faults in communication links. These results show that the proposed secondary control strategy is effective.

Prefix caching is a key technique for reducing Large Language Model (LLM) inference costs. However, the prevalent least-recently-used (LRU) eviction algorithm has a large gap to the optimal algorithm. This paper introduces LPC, the first learned method to perform LLM prefix cache eviction. LPC leverages conversational content analysis to provide predictive guidance for eviction, determining which conversations are likely to continue. These insights, combined with last access timestamps, inform more effective cache management. Extensive evaluations across three real-world datasets demonstrate that LPC achieves 18-47% reductions in required cache sizes for equivalent hit ratios and has an 11% improvement in LLM prefilling throughput in an emulated environment.

Dynamic events, such as machine fault and rush order insertion, are fairly common in the job shop scheduling, which may lead to significant delay in order delivery and low production efficiency. Under such circumstance, it is urgent to consider more perspectives in the scheduling, such as delay time and equipment load rate. In this article, a dynamic many-objective flexible job shop scheduling problem (DMaFJSP) is founded to simultaneously optimize the completion time, delay time, total equipment load and energy consumption. Canonical many-objective optimization algorithms are seeing difficulties in maintaining population diversity and enduring poor adaptability in dynamic scheduling problems. The paper proposes a two-stage individual feedback non-dominated sorting genetic algorithm-III (TSIF-NSGA-III), where a new population diversity strategy and an individual feedback strategy are added to expand the global search faculty and stronger dynamic adaptability. Numerical study in many-objective problem and dynamic many-objective problem are conducted. The final results illustrate that the proposed algorithm can with effect dispose of the DMaFJSP.Note to Practitioners—This paper was motivated by the flexible job shop scheduling problem (FJSP) in practical dynamic situations. In the actual production procedure, however, FJSP is a more challenging issue. Not only operation sequencing and machine allocation matters, but also uncertain factors in the environment, such as machine fault, rush order insertion, etc., are important. In addition, the majority of current researchers formulate the FJSP simply focusing on maximum completion time. However, low carbon and high efficient manufacturing calls for more objectives. In this paper, two dynamic incidents, machine stoppage and rush order insertion, are considered. In addition, the model of DMaFJSP is established with many objectives such as total energy consumption, completion time, equipment load and delay time. To resolve foregoing problems, this article proposes a TSIF-NSGA-III algorithm, which adopts a diversity generation strategy and an individual feedback strategy to strengthen the search ability and dynamic adaptability of this algorithm. Preliminary simulation outcomes illuminate that this algorithm has certain advantages. In addition, the algorithm can also be applied to other multi-objective workshop scheduling problems, such as mixed flow workshop, distributed workshop, etc.

This manuscript presents an adaptive funnel controller applying to the active magnetic bearing for position constraint control with prescriptive tracking performance. To better depict the actual active magnetic bearing dynamics, a nonlinear active magnetic bearing model with switched parameters instead of the existing fixed parameters is constructed considering the inherent properties of uncertainties and nonstationarities of active magnetic bearings. Then, by designing the funnel control scheme and adaptive laws based on Lyapunov stability theory and backstepping technique, the rotor displacement is constrained to not exceed the prescribed funnel boundary in view of safety concerns. Moreover, the funnel boundary integrated into the proposed controller is in line with the rotor displacement characteristics of active magnetic bearing systems under different speeds during real operation process. The boundness of the position tracking error is confirmed via Lyapunov synthesis. Eventually, the effectiveness of the proposed controller is verified by simulated and experimental examples.Note to Practitioners—The motivation of this manuscript is to investigate a control strategy with potential application for the displacement control of active magnetic bearings. In most of the existing displacement control schemes for active magnetic bearings, the constraint and limitation of displacement is not taken into account. However, considering the high-speed rotation and active controllability of active magnetic bearings, it is necessary to preset the performance indicators of rotor displacement including convergence speed, overshoot, and constraint limit, which can not only avoid serious safety accidents, but also give full play to the active control advantages of active magnetic bearings in different actual situations. Therefore, this manuscript suggests an adaptive funnel control strategy for prescriptive performance. Moreover, this manuscript solves the modeling and control problems for active magnetic bearings subject to nonlinearity, uncertainty, and external disturbances, which are difficult to be tackled in practical applications. The stability and convergence of the proposed controller are analyzed mathematically, and the practical experimental results reveal that the proposed controller has the potential and possibility to be applied in practice.

This article presents a robust fault reconstruction scheme for a class of discrete-time nonlinear singular systems with disturbances. The Lipschitz condition is utilized to describe the nonlinearity under investigation. To effectively estimate multiple types of faults such as time-varying faults and constant ones, a brand-new proportional-difference (PD) observer design method is established. What is worth mentioning is that all the gains of PD observer can be calculated at the same time by our method instead of manually selecting some gains and then solving other gains as has been done in the pertinent literature, which serves as a pioneering attempt for the similar design. Besides, by using the Lyapunov theory, less conservative linear matrix inequalities (LMIs) criteria with slack scalars and matrices are obtained to ensure that error systems are robust stability with the prescribed H∞performance. Finally, two examples on RC circuits and tanks level control models are presented to prove the superiority and practicability of the proposed fault estimation procedure.

This paper addresses the challenge of fault analysis in the Vehicular Energy Network (VEN) caused by small fault samples due to transient faults and complex disturbances. The proposed generation and diagnosis networks (GDNs) are developed without necessitating prior knowledge or manual intervention. The approach starts with an encoding and diagnosis network that converts multi-dimensional signals into images through supervised learning. A sample enhancement network, improved with module transfer and a relaxation objective function, is then proposed to increase the reliability of convergence and diversity of features for small fault samples. Additionally, a joint iterative training strategy between these two networks improves diagnostic accuracy and generalization through feature feedback. Performance validation on a semi-physical simulation platform demonstrates that the proposed GDNs achieve a 20% improvement in diagnostic accuracy with small datasets (200 samples) and maintain superior performance as sample volume grows. Thus, the proposed approach offers a potent solution for fault diagnosis in VENs with scarce samples, enhancing the analysis of complex systems. Note to Practitioners—This paper delves into fault diagnosis in the vehicular energy network (VEN) using small samples, employing a data-driven and deep learning model. The proposed method is versatile, suitable for analyzing complex systems with multiple and heterogeneous signals. An end-to-end model, named generation and diagnosis networks (GDNs), is introduced for generating small samples and conducting fault diagnosis without requiring prior knowledge or manual input. This method encodes multiple signals into signal images, which are then processed by a specially designed sample enhancement model, improved through the relaxation objective function and module transfer method. The enhanced samples are utilized for accurate analysis within the encoding and diagnosis networks’ diagnostic unit. The paper also provides a comparison of diagnosis results for reference. This approach enables researchers and engineers to efficiently augment and analyze small samples in practical applications, offering a practical framework for junior and inexperienced analysts. Preliminary experiments conducted using the RT-Lab semi-physical simulation platform suggest the method’s feasibility and effectiveness. Future research will explore the optimization of the model’s topology and parameters for lightweight design.

With the continuous expansion of grid nodes, traditional centralized methods exhibit certain limitations in the amount of communication data and computational cost for state estimation. In the past few decades, distributed state estimation has been fully developed in multi-area smart grid (SG). According to different areas or electrical equipment, the SG is divided into several nodes, and each node can estimate the entire state of the SG through local sensor measurement information and neighbor state estimation information. Although malicious attackers can use the different information between local sensor data and neighbor state estimation communication data to attack grid nodes, it will inevitably cause the attack detectors of other nodes to trigger alarms due to the consistency of grid state estimation. In this paper, we propose a globally stealthy attack strategy that biases the state estimation of the attacked node without triggering alarms on all nodes’ detectors. We provide sufficient and necessary conditions to implement a globally stealthy attack on grid nodes and extend it to multi-node attacks in order to reduce the number of communication links and increase the degree of damage. We also give the corresponding attack algorithm to realize these attacks. Finally, our proposed globally stealthy attack methods are simulated and verified in IEEE 39-bus power system.Note to Practitioners—In this paper, we propose two attack strategies against distributed state estimation in smart grids and present the corresponding attack methods. Since the grid is a multi-area power system, attacks between different nodes are more likely than attacks within nodes. Most of the previous literature focused on the ability of an attack to remain stealthy within the attacked node. However, due to the presence of the consistency term of the distributed state estimation, the state of the attacked node will be propagated to other nodes and detected by other nodes. Therefore, in this paper, we consider an approach that can maintain stealthy at all nodes. The effectiveness of the proposed attack strategy against distributed state estimation in smart grid is demonstrated in IEEE 39-bus power system. It is shown that a globally stealthy attack leads to a bias in the state estimation of the attacked node and does not trigger an alarm at any node. In summary, the proposed attack strategy is useful for the research community to design targeted detection strategies and can be conveniently applied to real-world security management systems in smart grids.

This paper introduces a novel distributed Cauchy-kernel-based maximum correntropy filter designed to address the state estimation problem in multi-area power systems under non-Gaussian noise conditions. In the framework of load frequency control (LFC), the proposed filter uses local and neighboring sensor data, leveraging the coupling characteristics of multi-area power systems. A method for determining the coupling estimation gain is provided. Unlike traditional methods that use minimum mean square error, this filter employs the Cauchy-kernel-based maximum correntropy condition to evaluate estimation performance. A fixed-point iterative algorithm is also presented to enhance the estimation performance in non-Gaussian environments, and the convergence of the algorithm is proved by Banach’s fixed point theory. Furthermore, the filter’s performance is compared with that of the Kalman filter, with an analysis of estimation unbiasedness. Finally, we validate the proposed distributed filter by simulation using a ten-area power system.

In this paper, an inductive power transfer (IPT) system for distribution insulator based on reconfigurable autotransformer magnetic coupler (ATMC) is proposed. A three-stage IPT system architecture is proposed based on the form of a 35kV insulator structure. A novel multi-tap magnetic coupler in the form of autotransformer is proposed. The voltage conversion ratio and output characteristics of the IPT system can be reconstructed by changing the winding taps connected to the IPT system. An ATMC-based IPT system circuit model is established. The parameters of ATMC are optimized. The effect of system parameter reconstruction by changing the winding taps of ATMC is analyzed. An IPT system based on a reconfigurable ATMC with three winding taps on both the primary and secondary sides is designed, an experimental prototype is built.$3\times 3$sets of experiments are completed, and the solution to reconstruct the system state by changing the winding taps of ATMC is verified. The maximum efficiency of the system is close to 90%.

For the superheated steam temperature control system, an optimized disturbance observer and output-constrained control algorithm have been designed. Initially, the original system with output constraints is transformed into a system without any state constraints, suitable for backstepping design, through state transformation. Then, based on the concept of negative gradient optimization, a gain iterative disturbance observer is constructed, which dynamically improves the control accuracy of the system compared to a constant gain disturbance observer. Finally, an adaptive neural control scheme based on the gain iterative disturbance observer is proposed, proving that all output states are constrained within predefined bounds, and all closed-loop signals are semi-globally uniformly bounded. The effectiveness of the proposed scheme is demonstrated through a simulation example of the superheated steam temperature system.

This article addresses a new type of adaptive time-varying group formation tracking (TVGFT) problem for linear multiagent systems (MASs) with nonautonomous leaders. To fulfill complicated formation tasks, a TVGFT protocol is proposed, where the agents are decomposed into multiple subgroups and each subgroup can successfully track the corresponding leader. Additionally, novel multiple asynchronous dynamic edge-event-triggered mechanisms (DEETMs) are designed to further conserve communication resources and optimize network utilization by enabling the leader to send information intermittently and allowing each follower to transmit information asynchronously when the trigger mechanisms are satisfied. The DEETMs consider both interlayer and intergroup information interactions to improve communication efficiency. Different from the existing results, the proposed DEETMs are used for intergroup information exchange to enhance both the coordination of formation and information transparency. At last, the simulation experiment is offered to validate the designed protocol.

Electrocardiogram (ECG) analysis and diagnosis are important auxiliary means for preventing and detecting cardiovascular diseases. Traditional approaches often face challenges due to the sheer volume of data, difficulty in extracting meaningful features, limitations in model complexity, and the requirement for real-time analysis in clinical settings. This paper presents a pioneering approach for automatic ECG diagnosis through the application of a novel Multiscale Deep Neuro-fuzzy Network (MDNFN) structure. The MDNFN is designed to address the complexity of arrhythmia classification by incorporating deep learning and fuzzy logic processing across multiscale feature extraction. To optimize the performance of the MDNFN, an innovative model optimization technique based on the Particle Swarm Optimization (PSO) algorithm is introduced, offering an efficient exploration of the parameter space. Extensive experiments across diverse datasets validate the superior performance of the proposed model compared to existing methods. The MDNFN demonstrates heightened accuracy and robustness, supported by its adaptability to different frequency and time scales inherent in ECG signals. The study establishes the model's efficacy through comprehensive experimentation, providing compelling evidence for its potential application in real-world clinical scenarios.

In this article, a novel adaptive event-triggered (ET) control scheme with dynamic limits error constraints is presented. To solve the problem of difficult selection of error constraint limit, the new error transformation function is constructed, which relates the constraint limit to the error and directly constrains the error. The constraint limit of the error designed in this article will change with the change of the error, so that the constraint limit has the ability of self-adjustment to improve the flexibility of error constraints. In addition, a novel ET mechanism based on relative threshold is proposed in this article. The mechanism associates the threshold with the tracking error. When the tracking error of the system changes, the threshold of the ET mechanism will also change accordingly, so as to adjust the number of triggers. On the premise of ensuring the control performance of the system, it can save communication resources as much as possible. Finally, a simulation implementation of the control strategy is carried out through numerical simulation to prove its effectiveness.

This article investigates the mean-square stabilization problem of nonlinear semi-Markov jump systems (SMJSs) subject to time-varying transition rates (TRs). To accommodate TRs variations across different operating conditions, a piecewise homogeneous embedded Markov chain (EMC), varying across intervals but time-invariant within each interval, is introduced. Considering partial states unobservability and real-time requirements, a mode-dependent fuzzy dynamic output feedback controller is developed, operating across multiple homogeneous Markov chain levels for real-time performance control. By incorporating richer sojourn-time information and membership function (MF) characteristics, elapsed-time term and the homogeneous polynomially parameter-dependent (HPPD) method are integrated into Lyapunov functions (LFs), enabling relaxed stability conditions. Furthermore, different utilization efficiencies of available semi-Markov kernel (SMK) information are discussed, with extended exploitation of partial information covering existing conservative results. Finally, numerical simulations of a tunnel diode circuit model (TDCM) and hardware-in-the-loop (HIL) tests of a suspension system are presented to demonstrate the superior control performance of the proposed method compared to existing methods.

In this article, the large-scale networked heterogeneous multiagent systems (LSNH-MASs) under dual-channel aperiodic Denial-of-Service (DCA-DoS) attacks with unknown mathematical models and limited computing power are studied. In order to overcome the unknown mathematical model of the system and enhance the ability of consensus control of LSNH-MASs, a model-free adaptive hierarchical resilient cloud control (MFAHRCC) strategy, only using input and output data, is proposed. This strategy can reduce the negative impact of the DCA-DoS attacks by designing a compensation factor. The stability and consensus of LSNH-MASs in insecure network environment are strictly proved. Under the constructed cloud control framework, the computation burden of all agents is borne by the cloud controller, which not only improves the ability of real-time processing data of the system but also greatly reduces the hardware cost of agents. Finally, the effectiveness of the MFAHRCC algorithm proposed in this article is verified by simulation with comparisons.

The optimal tracking problem for continuous-time boiler turbine systems (BTSs) with asymmetric input constraints is considered in this paper. Considering that continuous updating of control input will reduce the service life of engineering oriented system, a novel dynamic event-based triggering mechanism is proposed to reduce the number of controller updates, where a positive internal dynamic variable is introduced to expand the threshold. The objective is to find the optimal event-triggered control strategy for a given performance index function so that the system can track the ideal signal while minimizing the cost function. Firstly, by introducing tracking error variable, the optimal tracking problem is transformed into the optimal stability problem with symmetric input constraints. Then, three neural networks are designed to approximate the system model, cost function and control strategy respectively, and the feasibility of the proposed optimization algorithm is strictly proved by Lyapunov method, and the system will not exhibit Zeno behavior. Finally, simulation results effectively indicate the feasibility of the developed method in the industrial oriented system.Note to Practitioners—With the development of the national economy, China’s demand for electricity is also increasing. The BTSs in thermal power generation unit should not only realize load tracking, but also consider minimizing fuel consumption, minimizing pollutant emissions, maximizing the service life of BTSs. In this paper, adaptive dynamic programming (ADP) method and dynamic event-based mechanism are used to solve the optimal tracking problem of BTSs. The dynamic event-based mechanism not only affects the real performance of the BTSs, but also reduces the number of controller updates and saves resources of the BTSs.

This article investigates the fully distributed resilient practical leader-follower bipartite output consensus (LFBOC) problem for heterogeneous linear multiagent systems (MASs) with denial-of-service (DoS) attacks and actuator faults. To estimate the leader matrix and state in the presence of DoS attacks, two novel adaptive event-triggered observers are proposed based on newly developed lemmas, and then the adaptive event-triggered fault-tolerant controller without chattering behavior is developed to solve the LFBOC problem. Different from most existing resilient practical LFBOC working with DoS attacks and actuator faults, our method does not rely on any global information, event-triggered communication between neighbors and discrete update controllers are implemented simultaneously. Finally, an example is presented to well illustrate the effectiveness of developed method.

This article investigates the privacy-preserving consensus control and containment control for strict-feedback multiagent systems (MASs). For the agents possessing sensitive state information that needs safeguarding, an output mask function is employed, which ensures that the true state value remains indiscernible to the other agents during the process of information interaction. However, the introduction of mask function increases the complexity of the cooperative control design for MASs, given the untrustworthiness of the received state information from other agents. To address this challenge, an adaptive backstepping-based control algorithm is proposed, relying on the masked states of neighboring agents. Simultaneously, a dynamic event-triggered control with the reset mechanism is introduced to save communication resources, in which the dynamic of the additional variable is determined by the preset conditions. Based on the proposed event-triggered privacy-preserving control method, it is ensured that the initial state value of each agent remains undisclosed, and the tracking errors can converge to a residual set around zero. Similar results are extendable to the privacy preservation containment control for MASs. Finally, the efficacy of the proposed control method is validated through two illustrative examples.

This article investigates the position and attitude trajectory tracking control problem of quadrotor unmanned aerial vehicle (UAV) formation. A distributed optimal control protocol is proposed for UAVs formation to cooperatively complete the tracking tasks under unknown external disturbances. First, the concrete UAV dynamic model is established, and the UAV position and attitude loop controllers are designed based on detailed analysis of the UAV system. Second, in order to overcome the different types of external disturbances in the UAV flight process, the disturbance observer and disturbance suppression strategy are introduced. Then, the linear quadratic regulator (LQR) performance index is introduced to design the optimal control protocol of the attitude loop system such that the UAVs can successfully follow the target trajectory in a specific formation. Compared with the traditional consistent UAVs formation tracking strategy, the designed controllers involved in this article are characterized by low structural and computational complexity, which facilitates faster decision-making according to changes in the actual flight situation of UAVs. Finally, the effectiveness of the distributed optimal tracking control scheme based on the LQR algorithm is verified by a simulation example.

Abstract The heat exchanger has been widely used in the energy and chemical industry and plays an irreplaceable role in the featured applications. The design of heat exchanger is a mixed integer complex optimization problem, where the efficient design significantly improves the efficiency and reduces the cost. Many intelligent methods have been developed for heat exchanger optimal design. In this paper, a novel variant of sine and cosine algorithm named EDOLSCA is proposed, enhanced by dynamic opposite learning algorithm and the elite strategy. The proposed method is tested in CEC2014 benchmark and proved to be of significant advantages over the original algorithm. The new algorithm is then validated in the plate-fin heat exchanger (PFHE) optimal design problem. The comparison results of the proposed algorithm and other algorithms prove that EDOLSCA also has demonstrated superiority in heat exchanger optimal design.

The heuristic dynamic programming (HDP) ( λ )-based optimal control strategy, which takes a long-term prediction parameter λ into account using an iterative manner, accelerates the learning rate obviously. The computation complexity caused by the state-associated extra variable in λ -return value computing of the traditional value-gradient learning method can be reduced. However, as the iteration number increases, calculation costs have grown dramatically that bring huge challenge for the optimal control process with limited bandwidth and computational units. In this article, we propose an event-triggered HDP (ETHDP) ( λ ) optimal control strategy for nonlinear discrete-time (NDT) systems with unknown dynamics. The iterative relation for λ -return of the final target value is derived first. The event-triggered condition ensuring system stability is designed to reduce the computation and communication requirements. Next, we build a model-actor-critic neural network (NN) structure, in which the model NN evaluates the system state for getting λ -return of the current time target value, which is used to obtain the critic NN real-time update errors. The event-triggered optimal control signal and one-step-return value are approximated by actor and critic NN, respectively. Then, the event trigger-based uniformly ultimately bounded (UUB) stability of the system state and NN weight errors are demonstrated by applying the Lyapunov technology. Finally, we illustrate the effectiveness of our proposed ETHDP ( λ ) strategy by two cases.

In this article, we consider complex networks without connection information. The absence of global structure induces a great obstacle in the structural controllability analysis of these networks. Thus, a topology predicting method based on the connection probability matrix is proposed to provide a global structure for structural controllability analysis in this article. Furthermore, the modified principles of predicting global network topologies are established to acquire a more accurate global connection relationship. Eventually, the drive node set of these networks is determined by predicting global topologies. The accuracy of the proposed topology predicting method is verified by numerical simulations in the context of artificial networks and real networks. The results reveal that the global topology and structural controllability of complex networks with large scale and high edge density could be accurately predicted by utilizing the proposed method.

Abstract With the development of high‐voltage transmission and artificial intelligence technology, unmanned line inspection has become the inevitable trend of current electric power inspection. A new recognition algorithm for high‐voltage lines is proposed based on colour (Red, Green, Blue) RGB image to support the unmanned line inspection. Firstly, in order to solve the problem of missing weak edges in image edge detection, an improved Canny algorithm is proposed. Fourier transform Gaussian filter is introduced to enhance the high‐frequency signal of the image, which makes the extracted edge information more complete. At the same time, an improved line segment detector (LSD) algorithm is developed to extract the high‐voltage line. The complementary edge information of the three channels of the colour RGB image is analyzed, and the calculation formula of the horizontal line angle is improved, which greatly reduces the possibility of false detection and missed detection in the high‐voltage line extraction. In addition, the convolution neural network (CNN) is used to accurately recognize the extracted high‐voltage lines, which reduces the interference of non–high‐voltage lines. Simulation results show that the proposed algorithm has high recognition accuracy and strong robustness in the complex environment.

Flexible job shop scheduling problem (FJSP) has long been a complex problem due to the resource flexibility and strong constraints, generating a mixed-integer non-linear optimization problem. The problem becomes more complex with the increasing demand of energy reduction and the corresponding environmental impacts. Proper production scheduling is of significant potential in saving energy in the manufacturing system. In this paper, a multi-objective FJSP model is formulated with the objectives of minimizing the makespan and energy consumption considering strong transportation constraints. Two popular multi-objective optimization solver including Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and A Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D) are employed and compared in a real-world instance of the FJSP, associated with novel coding schemes. The results show that the proposed model is well solved by the two solvers and NSGA-II get the better solutions.

This article proposed a novel event-triggered integral sliding-mode control (ISMC) strategy for nonlinear system with disturbances via robust adaptive dynamic programming (ADP) considering control constrains. A mixed event-triggered ISMC scheme with two different trigger parts is developed and the optimal performance of sliding-mode dynamics with input constrains is ensured. By guaranteeing that the system trajectory converges to the sliding-mode surface with removing the input disturbances, a discontinuous part triggering rule is presented together with the existence analysis of a lower trigger interval time bound. Then, the optimal event-triggered control of sliding-mode dynamics is converted into a discounted factor-based H∞constrained control problem under continuous part triggering condition. To solve the event-triggered HJI equation, a critic-only neural network (NN)-based ADP scheme is developed by applying a concurrent learning. The NN weight is updated by analyzing the uniformly ultimately bounded (UUB) stability of sliding-mode dynamics considering the event-triggered condition via the Lyapunov technique. Finally, the validity of our control strategy is verified by simulation.

The synchronization problem of a kind of complex dynamical networks with uncertain links and external disturbances is studied in this paper. The adaptive and impulsive controller is designed after a series of researches on nonlinearity of joints, time variability and uncertainty of system parameters as well as uncertainty of coupling relationship among joints. The stability criteria of the uncertain complex dynamical networks are obtained based on the robust control theory and Lyapunov function theory, thus the synchronization for this kind of complex networks can be realized. The simulation results verify the effectiveness of the method in this paper.

Fault feature extraction based on prior knowledge and raw data is increasingly becoming more challenging in energy Internet fault diagnosis due to complicated network topology and coupling disturbances introduced into the systems. Deep learning methods that have emerged in recent years, such as the convolutional neural networks (CNNs), have shown a number of advantages and great potentials in the field of feature extraction and image recognition. However, CNNs does not work well in fault diagnosis for industrial systems, due to the totally different data representations between images used in recognition and signals obtained from industrial processes. This paper tackles this problem by introducing a novel and generic fault diagnosis method for complicated system, namely, the Spearman rank correlation-based CNNs (SR-CNNs). By imposing the Spearman rank correlation image layer on the typical CNNs, the multiple time-series signals measured by the phasor measurement units (PMUs) is converted to appropriate data images, which are then fed to the CNNs. With the aid of this novel design, different fault features can be comprehensively extracted while the fault can be identified more quickly and precisely than other conventional approaches. To validate the efficacy of the proposed approach, an IEEE defined power gird with many new energy resources are used as the test platform. The experimental results confirm the effectiveness and superiority of the proposed method in energy Internet fault diagnosis over conventional methods.

A novel monitoring strategy is proposed for multimode process in which mode clustering and fault detection based on geodesic distance (GD) are integrated. To start with, the empowered adjacency matrix of normalized training dataset is obtained and improved Dijkstra algorithm (IDA) is utilized to calculate the geodesic distance between each sample data so as to characterize the shortest distance of the nonlinear data within local areas accurately. Next, GD matrix algorithm is presented as an optimal clustering solution for a multimode process dataset. Then, the GDS model is established in each operating mode. Monitoring statistics based on the power of geodesic distance are structured based on square sum of Euclidean distances. Once the test data is detected as fault data, mode location based on deviation coefficient is conducted to narrow the scope of the inspection fault. Finally, the validity and usefulness of the proposed GDMPM monitoring method are demonstrated through the Tennessee Eastman (TE) benchmark process.

Selection of electricity fee payment site (EFPS) is a complex multi-objective optimization problem. Proper site selection of EFPS can not only facilitate the users to pay the electricity payments, but also save costs in running processes of the power enterprises (PEs). Firstly, two-dimensional genetic code is improved and the multi-object optimization model of EFPS is established. Secondly, considering a model city as an example, the NSGA-II algorithm is applied to solve the optimization model. Finally, the Pareto optimal solution set and several locating schemes based on different preferences are obtained by simulation. In the follow-up of the site selection work, experts can choose the method of adaptive weights and other methods to determine which scheme to apply. Example analysis shows that the results of the study has strong applicability, and it has practical significance to the site selection of the EFPS or other business halls.

In this paper, a novel T-S fuzzy model based adaptive synchronization scheme for nonlinear large-scale systems with uncertainties and time-delay is proposed. Based on the universal approximation property of T-S fuzzy model, a nonlinear large-scale system is established and fuzzy adaptive controllers are designed under Parallel Distributed Compensation (PDC) for overcoming the unknown uncertainties in systems and the time-delay in communication. Furthermore, under some certain condition, this synchronization scheme can be transformed into pinning synchronization control, which will indeed save much resource. Finally, a numerical simulation example is taken to show the effectiveness of the proposed adaptive synchronization scheme.

This paper is concerned with the design of observer for discrete-time nonlinear systems in the Takagi-Sugeno (T-S) fuzzy form. Under the framework of multi-instant homogenous matrix polynomials, a novel fuzzy observer and a new Lyapunov function, which are homogenous polynomially parameter-dependent on both the current-time normalized fuzzy weighting functions and the m-steps past-time normalized fuzzy weighting functions, are proposed for conceiving less conservative observer design conditions. Since the algebraic properties of both the current-time normalized fuzzy weighting functions and the m-steps past-time normalized fuzzy weighting functions are fully considered, the relaxation quality of the fuzzy observer design of discrete-time T-S fuzzy systems is significantly improved. In particular, some existing fuzzy Lyapunov functions and fuzzy observers are special cases of the Lyapunov function and the fuzzy observer given in this paper, respectively. Finally, a numerical example is provided to illustrate the effectiveness of the proposed approach.

In this paper, we investigate multistability for n-dimensional high-order recurrent neural networks with multistep piecewise linear activation functions. By Intermediate Value Theorem and definition of stability, sufficient criteria are derived for checking the existence of (r+1)nlocally exponentially stable equilibria for high-order recurrent neural networks. And the attractive basins of locally exponentially stable equilibria are established. One numerical example is provided to demonstrate the effectiveness of the proposed stability criteria.

This paper is concerned with the networked synchronization control problem of coupled dynamic networks (CDNs) with time-varying delay. First, both the data packet dropouts and network-induced delays are taken into account in the synchronization controller design. A Markovian jump process is induced to describe the packet dropouts. The network-induced delays are interval time varying and depend on the Markovian jump modes. A new closed-loop coupled dynamic error system (CDES) with Markovian jump parameters and interval time-varying delays is constructed. Second, using the Kronecker product technique and the stochastic Lyapunov method, a delay-dependent sufficient criterion of stochastic stability is obtained for the closed-loop CDES, which also guarantees that the CDNs are stochastically synchronized. Finally, a simulation example is given to demonstrate the effectiveness of the proposed result.