Information disclosure and cyber attacks pose significant challenges to practical implementation of distributed control in microgrids (MGs). This paper investigates the distributed privacy-preserving resilient secondary control problem for an isolated AC MG under hybrid attacks including false data injection (FDI) and denial-of-service (DoS) attacks at low communication costs. Specifically, using state decomposition strategy and event-triggered mechanism, a distributed privacy-preserving event-triggered resilient secondary controller is designed, which reduces communication while safeguarding system privacy. Additionally, a long short-term memory (LSTM)-based estimator is designed to estimate the aggregated communication signals from neighboring DGs. The proposed resilient control method can further mitigate the impact of cyber attacks by using the estimated aggregated communication signals as the reference value, and the stability of the MG system is proven by Lyapunov theory. Compared to existing data-driven methods for attack detection, the proposed LSTM-based estimator uses communication signals as the database, thus avoiding the leakage of complete system data during offline training. To validate the effectiveness of the proposed method, a hardware-in-the-loop experiment is conducted under various hybrid attacks in OPAL-RT.

This article concentrates on the attack detection and active attack defense strategies for discrete-time linear cyber-physical systems (CPSs) with unknown but bounded (UBB) disturbance and noise in the presence of both actuator and sensor attacks. First, a novel zonotopic observer is constructed to estimate the set-valued state and actuator attack by introducing augmentation techniques. To mitigate the effects of uncertainty and enhance estimation accuracy, the $H_{\infty }$ technique is introduced to construct the observer. Unlike most existing works, the constructed observer simultaneously estimates the system state and actuator attacks. Then, by combining the designed observer with reachability analysis, a set-valued abnormal detector and a residual-based abnormal detector are designed to detect actuator and sensor attacks, respectively. In addition, by incorporating the obtained state reachable sets and the $H_{\infty }$ technique, an active attack defense mechanism is designed to mitigate the impact of attacks on system performance. The proposed defense strategy does not introduce any performance loss in the absence of attacks. Finally, the superiority of the developed method is demonstrated by its application to a numerical simulation and an autonomous aircraft system.

Applying deep reinforcement learning (DRL) to solve quantum control problems has become a popular research direction. However, the exploration capability and reward design for the learning agent, which usually affect the DRL’s application performance, has not been sufficiently emphasized. In this article, we propose a particle-assisted DRL (PDRL) method to address the above concern by enhancing exploration capabilities and designing appropriate reward functions for efficient quantum state manipulation. In PDRL, each episode in the quantum learning process is characterized by three kinds of events, i.e., unidentifiable, identifiable, and successful events. To improve exploration, exploration particles and feedback particles are employed in the early learning phase when episodes end in identifiable and successful events, respectively. To assign rewards, three event-based reward functions are provided for the DRL’s agent, exploration particles and feedback particles, respectively. Numerical results on single-qubit, two-qubit, and many-qubit systems validate the effectiveness of PDRL. Comparative results with existing DRL methods demonstrate the superior performance of PDRL for quantum state manipulation.

Jamming attacks pose a significant threat to the security of air-ground communications, where the challenge becomes more severe when involving multiple unmanned aerial vehicles (UAVs) incurring complex interference. To address this issue, this paper proposes a graph attention-based reinforcement learning strategy for anti-jamming UAV communications. Specifically, we consider the multi-UAV transmission and deployment in the presence of jamming attacks. Then, we formulate a zero-sum game with the legitimate side and adversary to maximize and minimize the overall transmission rate, respectively. Given the complicated structure of the game, we decompose it into two layers, tackled in a hierarchical learning framework. Particularly, the inner layer addresses the legitimate beamforming, for which we establish the graph attention network (GAT) to track the complicated interference and jamming relationship based on the graph representation of the UAV network. The outer layer address the legitimate UAV deployment and adversarial jamming policy, which is reinterpreted in a multi-agent deep reinforcement learning framework to obtain the strategies of both sides. The inner GAT is then nested within the outer multi-agent learning framework in a hierarchical manner to approximate the equilibrium of the original game model. Simulation results demonstrate the convergence and the performance superiority of the proposed learning scheme in terms of anti-jamming transmission rate. Also, the results exhibit significant generalization capability to cover different network configurations and parameters with reliable communication performance.

Vehicle platoon control is one of the promising technologies to improve the performance of transportation systems. However, communication connections among vehicles in cyber physical vehicle systems (CPVS) are often intermittent due to environmental factors and limitations of physical equipment. Moreover, the system models of the vehicles are generally unknown in practice, making it challenging to implement platoon control for CPVS. To address these challenges, we investigate the challenge platoon control problem for CPVS with intermittent communication, where the system models of both the leader vehicle and the follower vehicles are unknown. A data-driven-based learning algorithm is first designed to obtain the unknown leader model. Then, based on the learned leader model, a finite-time distributed observer is proposed to estimate the leader vehicle state in finite time under intermittent communication. Furthermore, with the unknown system models, a data-driven-based controller gain learning algorithm is proposed to learn the controller gain. Based on the learned controller gain, adaptive decentralized tracking controllers are designed to perform platoon control for CPVS. Finally, the effectiveness of our result is examined by a simulation example.

In this paper, a distributed robust and adaptive error estimation framework is proposed to investigate the cooperative analysis of quasi-synchronization behaviors (QSBs) for heterogeneous cyber-physical systems with uncertain dynamic networks, where a dynamic pinning control strategy that is driven by time-varying switching and intermittent communication is developed. Specifically, the distributed robust error estimation for QSBs is first formulated as the followers with uncertain dynamics to approximately synchronize with the target within a robust non-zero error bound under a dynamic coupling law in a distributed manner. By developing a switching condition of dwell time, a novel distributed error estimation algorithm is proposed to obtain the robust cooperative analysis for QSBs of heterogeneous cyber-physical systems with uncertain dynamic networks by designing a time-varying pinning controller and a dynamic coupling law. Furthermore, by introducing an intermittent condition of the communication rate, the robust adaptive cooperative analysis is addressed for QSBs of heterogeneous cyber-physical systems with uncertain dynamic networks, where the system dynamics are composed of nonidentical nonlinear systems, dealing with an intermittent pinning controller and an adaptive and discontinuous coupling law. The proposed strategy can remove the traditional limitation for the quasi-synchronization error estimation that requires a static coupling law and the global knowledge of the fixed communication topology. The development of the developed methodologies is illustrated through two case studies of simulation.

In this article, the fixed-time resilient distributed Nash equilibrium (NE) control problem is addressed for nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks. Unlike existing results on NE seeking in noncooperative games, this article first investigates the layered fixed-time resilient distributed NE control problem for nonlinear MASs against DoS attacks, independent of initial conditions. To address these challenges, the novel layered NE control strategy is proposed, comprising a resilient fixed-time distributed NE seeking algorithm layer, an improved fixed-time performance enhancement layer, and an adaptive controller design layer. Specifically, a fixed-time resilient distributed NE seeking algorithm is first designed to guarantee players actions to converge toward the NE against DoS attacks. Then, novel high-order fixed-time filters are proposed to generate improved actions with smooth characteristics and converge to the actions in the aforementioned fixed-time algorithm. Based on the developed filters, a decentralized fuzzy adaptive controller is developed to achieve bounded tracking within the fixed time using the backstepping technique. Finally, a numerical simulation is conducted to validate the efficacy of the developed method.

This article investigates the resilient distributed Nash equilibrium (NE) control problem for nonlinear multiagent systems (MASs) that suffers from denial-of-service (DoS) attacks in the communication network. Different from the existing works on NE seeking in noncooperative games, it is the first trial to consider the resilient distributed NE control problem for nonlinear MASs under DoS attacks. To overcome the challenges caused by the considered problem, a new layered NE control method is developed, which consists of a resilient distributed NE seeking algorithm, two-stage cascade filters, and a resilient adaptive controller. Specifically, the resilient distributed NE seeking algorithm is proposed to ensure that the actions in this algorithm converge to the NE even under DoS attacks. Then, the improved actions with smooth characteristics are designed by introducing novel two-stage cascade filters. By using newly designed actions and their derivatives, a resilient adaptive controller is proposed to ensure that the output of MASs converges to the NE. Finally, simulation results are provided to verify the effectiveness of the proposed strategy.

In complex traffic environments, the driving performance of the leader vehicle in a platoon can be greatly impacted by sudden and unexpected changes in vehicle acceleration rates. This phenomenon is known as unknown jerk dynamics (JDs), and it can lead to more extreme car-following behaviors (CFBs) in platoon tracking control, which may raise safety and traffic capacity issues. To tackle these concerns, this work studies cooperative platoon tracking control and intermittent optimization problems for connected autonomous vehicles (CAVs) with unknown reaction time delays (RTDs) using a nonlinear car following model (NCFM). In a free-design but directed communication network, we assume that the leader CAV’s external inputs have unknown but bounded parameters both for the JDs and RTDs, while only a small number of nearby follower CAVs are aware of the leader CAV’s acceleration signals. To solve these issues, we consider that each follower CAV implements a distributed observer law, which provides a reference signal stated as an estimated JD of the leader CAV. Then, a distributed platoon tracking control protocol is proposed to construct cooperative tracking controllers with identical inter-vehicle constraints (ICs). This maintains the desired safety distance between the CAVs and allows each follower CAV to track its leader CAV only through local information exchange. In addition, we present a robust intermittent optimization design and a novel intermittent sampling condition that can guarantee optimally scheduled feedback gains for the cooperative platoon tracking controllers to minimize the control cost in the presence of unknown JDs and RTDs under non-identical ICs. Simulation case studies are conducted to demonstrate the effectiveness of the proposed approaches. We also demonstrate the efficient development of such a distributed cooperative car-following model for the platoon’s motion (or as an intelligent speed advising system for automated or human-driven vehicles), resulting in a trip that is safe, comfortable, and energy efficient.

Traditional chicken disease diagnosis methods rely on manual observation and experiential judgment, which are inefficient and susceptible to subjective factors. This paper aims to construct a chicken disease diagnostic model with deep learning algorithm for enhancing the accuracy and efficiency. Firstly, this paper proposes a chicken disease diagnostic model based on YOLOv8 algorithm. An SE-attention mechanism is designed for YOLOv8 structure to improve the detection accuracy. The SE-Attention based YOLOv8 detection model can identify and classify diseases by analyzing the feces images of chickens. Experiments on chicken disease diagnosis are performed to validate the proposed model's feasibility and effectiveness. Ablation study is constructed to validate the advantages of the SE-attention mechanism.

In this paper, the distributed secondary voltage and frequency restoration problem is addressed in AC microgrid systems subjected to hybrid false data injection (FDI) and denial-of-service (DoS) attacks. Firstly, a new distributed resilient iterative estimator based on the k-step estimation method is proposed that can accurately estimate the FDI attack signals as well as the voltage and frequency of each distributed generation (DG) even under the impact of DoS attacks. Then, based on the mean value of the attack estimation signal, a distributed resilient secondary controller is designed to compensate for the considered hybrid attacks. Compared with existing researches on resilient control of AC microgrids under hybrid attacks, the voltage and frequency regulation errors of the microgrid system converge to zero for the first time. Finally, the precise convergence of voltage and frequency in the AC microgrid system under the proposed method is verified through a real-time controller-hardware-in-the-loop experiment in OPAL-RT.

In this paper, the prescribed-time cooperative resilient fuzzy tracking control problem is addressed for thirdorder nonlinear cyber-physical vehicle systems (CPVS) under denial-of-service (DoS) attacks. Different from the existing results on cooperative resilient fuzzy tracking control, the developed method can achieve the cooperative resilient fuzzy tracking control objective within the prescribed time. To begin with, a data-driven-based online learning algorithm is introduced for learning the unknown and switching matrix of the reference signal. Based on the learned matrix, novel distributed resilient cooperative observers are designed to achieve prescribed-time observation by introducing an asynchronous observing method. To further improve the smoothness of the observation signal, a new improved smooth second-order prescribed-time observer is designed. Furthermore, leveraging the states of the smooth observer, a fuzzy controller is proposed to achieve precise tracking within the prescribed time, independent of initial conditions using the scaling error method and the backstepping technique. Finally, a simulation example is included to illustrate the effectiveness of the proposed methodology

While the Internet of Things (IoT) structure is capable to facilitate the distributed load frequency control (DLFC), the open-air sensors and the intrinsically open communication networks are inevitably vulnerable to uncertain environments. This work endeavors to present a general resiliency enhancement framework for DLFC considering the IoT faults. Multiple fault sources are incorporated, including the intermittent measurements caused by sensor aging, the communication network failures caused by cyberattacks, etc. The framework is equipped with two resilient layers. The first resilient layer focuses on the offline robust DLFC design, in which we consider the intermittent measurements from sensors in system modeling. The second resilient layer concerns the online cyberattack detection, which can further tolerant the incomplete modeling issues of the first resilient layer. Simulation results verify the efficacy of the presented resilient framework.

A robust model predictive control (RMPC) framework is proposed for nonlinear cyber-physical systems (CPSs) with input saturation and energy-bounded disturbance in this article. The coexistence of Lipschitz nonlinearity and actuator saturation remains a challenging problem in controller design. In our article, a major concern is to address the incorporation of bipartite nonlinear characteristics and exogenous disturbance under RMPC scheme. Via the saturation relaxation and Lipschitz condition, the conversion of a tractable linear matrix inequalitie-constrained problem is proposed in a less conservative way. For the purpose of enhancing the robustness and disturbance rejection, the proposed RMPC framework is associated with mixed$H_{2}$/$H_{\infty }$requirements. In particular, the algorithm is then cast into minimizing the upper bound of infinite-horizon cost function, and it is updated online for the linear control law. The computational feasibility is proved recursively, which is the key point in the practical implementation. Also, both the closed-loop stability and performance of CPS are derived. Eventually, the laboratory tank and reactor–separator process are presented to verify and illustrate the effectiveness of the proposed RMPC with mixed$H_{2}$/$H_{\infty }$performance.

This article develops a robust packetized predictive control framework to deal with the quantized-feedback control problem of networked systems subject to Markovian packet dropouts and input saturation. In the proposed framework, the Markov chain model of packet dropout is established from the link of the controller to the actuator. To deal with the quantized measurements, a robust packetized predictive control method is presented with a quantized-feedback law. The problem of unreliable transmission is addressed by proposing a packet dropout compensation strategy with a forgetting factor. An augmented Markovian jump system model is established to take the packet dropouts into account. The synthesis of packetized predictive control is then developed by minimizing a worst case cost function with respect to the model uncertainties. The recursive feasibility of the proposed controller design problem and the mean-square stability of the closed-loop systems are proved, respectively. The proposed packetized predictive control method is demonstrated by simulating a four-tank process system.

This article proposes an observer-based reinforcement learning (RL) control approach to address the optimal attitude-tracking problem and application for hypersonic vehicles in the reentry phase. Due to the unknown uncertainty and nonlinearity caused by parameter perturbation and external disturbance, accurate model information of hypersonic vehicles in the reentry phase is generally unavailable. For this reason, a novel synchronous estimation is proposed to construct a composite observer for hypersonic vehicles, which consists of a neural-network (NN)-based Luenberger-type observer and a synchronous disturbance observer. This solves the identification problem of nonlinear dynamics in the reference control and realizes the estimation of the system state when unknown nonlinear dynamics and unknown disturbance exist at the same time. By synthesizing the information from the composite observer, an RL tracking controller is developed to solve the optimal attitude-tracking control problem. To improve the convergence performance of critic network weights, concurrent learning is employed to replace the traditional persistent excitation condition with a historical experience replay manner. In addition, this article proves that the weight estimation error is bounded when the learning rate satisfies the given sufficient condition. Finally, the numerical simulation demonstrates the effectiveness and superiority of the proposed approaches to attitude-tracking control systems for hypersonic vehicles.

This article aims to investigate the data-driven attack detection and identification problem for cyber-physical systems under sparse actuator attacks, by developing tools from subspace identification and compressive sensing theories. First, two sparse actuator attack models (additive and multiplicative) are formulated and the definitions of I/O sequence and data models are presented. Then, the attack detector is designed by identifying the stable kernel representation of cyber-physical systems, followed by the security analysis of data-driven attack detection. Moreover, two sparse recovery-based attack identification policies are proposed, with respect to sparse additive and multiplicative actuator attack models. These attack identification policies are realized by the convex optimization methods. Furthermore, the identifiability conditions of the presented identification algorithms are analyzed to evaluate the vulnerability of cyber-physical systems. Finally, the proposed methods are verified by the simulations on a flight vehicle system.

There is no doubt that in the near future, machines will share roads with human drivers [1] [2]. Therefore, the prediction of human drivers' lane changing behavior is imperative. Lane-change prediction is one of the most important ones. Both human drivers and autonomous vehicles should make sure that no other vehicle switches lanes or moves into the same region of the target lane as the ego vehicle. The existing short-term prediction algorithms can only provide a prediction horizon of 3 ~ 5s, leaving only a limited reaction time for drivers and autonomous path planning modules. Additionally, the majority of previous research analysed less on investigate lane segmentation or merging, simply the inference of lane shift in an expressway context. Most of earlier research only focused on the inference of lane-change in an expressway context, as opposed to the more typical urban environment. There are relatively few of these studies that can handle multi-scenario and scenario switching. In this paper, a Scenario Encoding Model (SEM) is proposed to help solve the problem of long-term lane-change prediction and the scenario switching problem in the existing short-term lane-change prediction. Even in the absence of road history data, the SEM can model the road scenario and encode the real-time road scene by using Self-Organizing Map (SOM) In the mean time, the established initial model has the ability to be further evolved into a historical bias model in the background of a large amount of road historical data. The evaluation test of this SEM has been done through the NGSIM dataset.

This article studies the decentralized event-triggered control problem for a class of constrained nonlinear interconnected systems. By assigning a specific cost function for each constrained auxiliary subsystem, the original control problem is equivalently transformed into finding a series of optimal control policies updating in an aperiodic manner, and these optimal event-triggered control laws together constitute the desired decentralized controller. It is strictly proven that the system under consideration is stable in the sense of uniformly ultimate boundedness provided by the solutions of event-triggered Hamilton-Jacobi-Bellman equations. Different from the traditional adaptive critic design methods, we present an identifier-critic network architecture to relax the restrictions posed on the system dynamics, and the actor network commonly used to approximate the optimal control law is circumvented. The weights in the critic network are tuned on the basis of the gradient descent approach as well as the historical data, such that the persistence of excitation condition is no longer needed. The validity of our control scheme is demonstrated through a simulation example.

The distributed observer problem is motivated by the case where the output information of the system is decentralized in different subsystems. In this scene, all the subsystems form an observer network, and each of them has access to only a part of output information and the information exchanged via the given communication networks. Due to the limitation of communication conditions among subsystems, the communication network is often time varying and disconnected. However, the existing research about the aforementioned scene is still not enough to solve this problem. To this end, this article is concerned with the challenge of the distributed observer design for linear systems under time-variant disconnected communication networks. The design method is successfully established by fixing both completely decentralized output information and incompletely decentralized output information into account. Our work overcomes the limitation of the existing results that the distributed observer can only reconstruct the full states of the underlying systems by means of fast switching. In the case of completely decentralized output information, a group of sufficient conditions is put forward for the system matrix, and it is proved that the asymptotical omniscience of the distributed observer could be achieved as long as anyone of the developed conditions is satisfied. Furthermore, unlike similar problems in multiagent systems, the systems that can meet the proposed conditions are not only stable and marginally stable systems but also some unstable systems. As for the case where the output information is not completely decentralized, the results show with the observable decomposition and states reorganization technology that the distributed observer could achieve omniscience asymptotically without any constraints on the system matrix. The validity of the proposed design method is emphasized in two numerical simulations.

A vehicle platoon is a group of vehicles driving together with a harmonized speed and a short inter-vehicle gap by using vehicle automation and vehicle-to-vehicle communication. Platoons have to share road with human-driven vehicles (HDVs) and can only be applied in heterogeneous traffic flow for a long period. Driver cut-in behavior (DCB) towards a platoon can be frequently expected in such driving context. In this paper, to understand and simulate such behavior, we propose a platoon-oriented cut-in behavior (POCB) model by fusing a lateral and a longitudinal control model into the queuing network (QN) cognitive architecture. Platoon-oriented cut-in experiments are conducted to collect driver data under cut-in from back and front scenarios, which both include six sub-scenarios with different platoon gaps or initial velocities. We demonstrate the effectiveness of the proposed model in simulating the DCB towards platoons by comparing experimental and simulation results under various driving scenarios across different subjects.

The continuing increase of the on-road private cars is contributing to a deterioration of the urban traffic system. Public transportation is widely used to tackle this issue due to its large ridership. In this paper, we propose a multi-bus dispatching strategy combined with the boarding and holding control (MBDBH) to improve bus utilization and further decrease the passenger excess delay. Dispatching adjustments and operation control are taken into account in the system. At the dispatching level, on the one hand, either a bus platoon or a single bus can be dispatched for each trip to provide adaptive bus capacity to match the highly-fluctuated stop demands, on the other hand, we adjust the bus dispatching time based on the existing timetable to minimize passenger excess waiting time to a large extent. Meanwhile, the operation level incorporates both holding strategy and boarding limit strategy to bring more flexible adjustments in improving bus service. Besides the efficiency, we also minimize the headway variation in order to maintain a high system reliability. The problem is formulated as a Mixed Integer Nonlinear Programming (MINP) problem, which is solved by the commercial solver Gurobi. With the computational complexity as a concern, we propose a distributed algorithm to implement dual decomposition based on the partial Lagrangian relaxation. Finally, numerical examples are investigated to illustrate the significant time reduction of distributed algorithm and the efficiency of our proposed strategy: The proposed MBDBH model can reduce roughly 50% and 30% of remaining passenger volumes when compared with the timetable-based fixed schedule and the optimized single-bus dispatching schedule, respectively.

This article addresses the cooperative fault-tolerant control problem for networks of stochastic nonlinear systems with actuator faults and input saturation. The fuzzy neural networks (FNNs) are employed to estimate the unknown functions and stochastic disturbance terms. To analyze the nondifferential saturation nonlinearity, a smooth nonlinear function of the control input signal is constructed to estimate the saturation function. A novel adaptive fault-tolerant control protocol is proposed by using backstepping design technique. By using the stochastic Lyapunov functional strategy, it is proved that all the followers’ outputs eventually converge to a small neighborhood of the leader’s output, and all the signals in the closed-loop systems are bounded in probability. Finally, the performance of the proposed control strategy is illustrated through simulation.

This article is concerned with the leader-following output consensus problem in the framework of distributed nonlinear observers. Instead of certain hypotheses on the leader system, a group of geometric conditions is put forward to develop a novel distributed observers strategy, thereby definitely improving the applicability of the existing results. To be more specific, the improved distributed observers can precisely handle consensus problems for some nonlinear leader systems, which are invalid for the traditional strategies with a certain assumption, such as elastic shaft single linkage manipulator (ESSLM) systems and most of the first-order nonlinear systems. We prove the sufficient conditions for the exponential stability of our distributed observers’ error dynamic by proposing two pioneered lemmas to show the relationship between the maximum eigenvalues of two matrices appearing in Lyapunov type matrices. Then, a partial feedback linearization method with zero dynamic proposed in differential geometry is employed to design the purely decentralized control law for the affine nonlinear multiagent system. With this advancement, the existing results can be regarded as a specific case owing to that the followers can be chosen as an arbitrary minimum phase affine smooth nonlinear system. At last, the novel distributed observers and the improved purely decentralized control law are applied in the distributed control framework to construct a closed-loop system. We also prove the stability of the closed-loop system to achieve leader-following consensus. Our method is illustrated by the ESSLM system and Van der Pol system as leaders.

The rapid growth of data on the Internet has brought huge challenges to storage systems. Data deduplication technology is proposed to solve the problem of data redundancy. As one of the data deduplication technologies, the memory-assisted method uses an approximate membership data structure to greatly reduce the space consumption of membership determination. The approximate membership data structures represented by the cuckoo filter have been widely used. However, there is a lack of efficient ways to solve the problem that the insertion time increases exponentially with the load rate of the cuckoo filter. In this paper, an efficient cuckoo filter named TSCF is proposed with a two-stage insertion algorithm. The TSCF balances the load of the filter through active relocations in the first stage, laying the foundation for the second stage. Through the experiments, the cumulative relocation times of the TSCF are reduced to 37% and 46% respectively compared with the SCF and the CFBF, indicating that the TSCF greatly reduces the relocation times and insertion time of the entire insertion process, and improves the performance of the cuckoo filter.

In this paper, a model-free optimal synchronization controller is designed to achieve the aggressive attitude synchronization for multiple heterogeneous quadrotor systems with highly nonlinear and coupled dynamics by using a reinforcement learning (RL) approach. A distributed observer is first designed for each following quadrotor to estimate the states of a virtual leader. A performance function is then utilized for each quadrotor to penalize the observed synchronization error and the control effort. An RL approach is finally employed to learn the optimal control law without any knowledge of the dynamic model information of the followers. The control law depends on the quadrotor states and the observer states, and guarantees that the attitude synchronization error converges to zero for all quadrotors, under aggressive maneuvers. Simulation results are provided to verify the effectiveness of the proposed controller.

This article studies the bipartite containment control problem for nonlinear multiagent systems (MASs) with input quantization over a signed digraph. The design objective is to provide an appropriate distributed protocol such that the followers converge to a convex hull containing each leader's trajectory as well as its opposite trajectory different in sign. Based on a nonlinear decomposition approach of input quantization, an event-triggered control scheme is developed via backstepping technique. A fuzzy observer is constructed to estimate unmeasurable states. Moreover, the bipartite containment control scheme for nonlinear MASs is designed. It is demonstrated that all signals in the closed-loop system are semiglobally uniformly ultimately bounded and Zeno behavior is excluded. Finally, a simulation example is given to verify the validity of the designed method.

Efficient task assignments can significantly improve agent management and reduce communication load and energy consumption. This article investigates the cooperative control problem for multiagent systems with an active task assignment strategy, in which whether an agent exchanges the information with neighbors depends on a perceived mission. By defining a set of missions, a task assignment mechanism for cooperative control problem is first proposed, in which the tasks will be scheduled by intermittent communication signals associated with the actual optimization requirements. By allowing appropriate task assignment conditions, a class of tracking cooperative control protocol is designed and accordingly, the stability of the closed-loop systems under the intermittent communication will be guaranteed. We also consider a case that the communication links between the followers and the leader can be optimized. To maximize the quality of information interaction, a leadership competition mechanism is introduced to design the tracking cooperative control protocol. As an application, cooperative surveillance using a group of rotary-wing air vehicles is considered. Numerical simulation demonstrates the effectiveness of the proposed approaches.

This article proposes a novel dynamic fault-tolerant control model to address the optimal tracking cooperative control problem for cyber-physical systems, by considering that all systems can be endowed as a multiagent system and the admissible levels of the actuator fault can be resiliently management. Different from previous works, the feedback gain for the cooperative controller design is no longer fixed, and actuator outage behaviors can be solved by a resilient control way. By introducing a sampling manner, a robust optimal framework is first developed to determine the appropriate feedback gain under a cost constraint for the dynamic fault model. The dynamic fault-tolerant control protocol is, then, designed to achieve the cooperative behaviors. Moreover, a fault management mechanism is proposed, in which the fault parameter is reset as an initial value when the fault growth is greater than the admissible level. By this design, the tracking cooperative behaviors can be achieved in a resilient management process. Two examples are presented to illustrate the effectiveness of the proposed theories.

This paper investigates the cluster event-triggered tracking cooperative and formation control for multivehicle systems with nonlinear vehicles dynamics under switching directed communication topologies and safety missions. By employing the cluster event-triggered control strategy, the assumption of continuous feedback control gains and the condition of information exchange limited in a small domain can be relaxed. By constructing an appropriate tracking cooperative condition, a class of cluster event-triggered tracking cooperative control law with an extended magnification region condition is proposed based on bounded parameters and intermittent event samples. When the safety tasks are activated, it is displayed in terms of linear matrix inequalities that the tracking cooperative control for closed-loop multivehicle systems subject to nonlinear vehicles dynamics will be achieved, if the constructed sampling condition and the dwell time constraint of switching communication scenarios can be held. Furthermore, the proposed method is extended to formation control problem for the multivehicle systems. Simulations are presented to demonstrate the effectiveness of the proposed approaches.

This article investigates the fully cooperative consensus problem for heterogeneous nonlinear multiagent systems with a leader of bounded unknown input. In the proposed framework, a novel distributed cooperative consensus algorithm is developed to derive the perfect consensus behaviors, in which the general assumption in existing works that the leader agent must have a direct link to every follower can be removed. With the formulation of some mild assumptions, the sufficient conditions for controller design are proposed to develop a class of integrated consensus protocol, which can friendly synthesize the limited interaction, compensation dynamics of the equilibrant point of the systems, appropriate feedback gains, and distributed estimation of the unknown inputs of the leader. The effectiveness of the proposed approaches are verified by simulations with multiple heterogeneous single-link manipulators.

In this paper, the time-varying formation problem is studied under directed topologies. An adaptive approach is utilized to develop a fully distributed formation controller for general linear multiagent systems. To achieve distributed time-varying formations, a feasible formation set is proposed by proposing some conditions. By using adaptive techniques, the main contribution of this paper is that a fully distributed formation algorithm is designed for achieving time-varying formation under directed graphs, which relies only on the local measurements without requiring any global information. By analyzing the Lyapunov stability, the proposed fully distributed formation algorithm can ensure the multiagent systems achieving the goal of time-varying formations. To verify the theoretical results, some simulation examples are shown finally.

We demonstrate a novel intent defined optical network (IDON) platform that introduces self-adapted generation and optimization (SAGO) policy, utilizing closed-loop policy generation and closed-loop intent guarantee to achieve zero-touch operation of optical network.

With the rapid evolution of smart city all over the world, the appealing services of IoT and big data analytics have prompted the design of more reliable assurance mechanism for network quality. It has been a crucial issue of network operation that once multiple links fail simultaneously, the transmission of real-time services cannot be guaranteed. Therefore, rapid locating of faults is the premise for network to recover quickly. However, current faults location methods can't satisfy the requirement due to the expansion scale of wireless and optical networks and the growing demands of customers. In this paper, we propose an efficient multi-link faults location algorithm based on Hopfield Neural Network (HNN). We make full use of the information of network topology and the services transmitted to model the relationship between fault set and alarm set. HNN is used as an optimization method to analyze the uncertainty of faults and alarms and to find where the faults most likely occur by constructing a proper energy function. It has been proved by experiments that this method can achieve real-time faults location while ensuring positioning accuracy, which provides a good solution for smart city service assurance.

This paper proposes a heterogeneous coupling network framework to address the cooperative tracking control problem for multiagent systems with dynamic interaction topology and bounded intermittent communication. By considering the underlying dynamic interaction topology and introducing the adjustable heterogeneous coupling weighting parameters, a bounded consensus condition of cooperative tracking control is proposed. With considering a bounded intermittent communication condition, a class of intermittent cooperative tracking control protocol is designed based on the combination of the individual agent dynamic and the exchange of information among the agents under an appropriate consensus speed constraint. It is proved in the sense of Lyapunov that the cooperative tracking control for the closed-loop multiagent systems can be achieved under the dynamic interaction topology, an appropriate feedback gain matrix, and the intermittent communication information of all agents. The results are further extended to the information consensus protocol with intermittent coordinated constraint information. Finally, two examples are presented to verify the effectiveness.

This paper addresses the leader-follower consensus problem of multivehicle wirelessly networked uncertain systems with nonlinear dynamics and actuator fault and proposes a class of distributed discontinuous communication protocols based only on the relative states among neighboring vehicles. By introducing a novel fault model for multivehicle wirelessly networked uncertain systems, fault tolerant consensus can be achieved with different fault modes of the actuators. It is proved in the sense of Lyapunov that, if the conditions of dwell time and the intermittent communication rate are satisfied, the leader-follower consensus can be achieved for closed-loop multivehicle wirelessly networked uncertain systems with nonlinear dynamics and actuator fault under the topology that frequently but not always contains a spanning tree rooted at the leader. Furthermore, the results are extended to the collision avoidance and formulation control problems. Four examples are presented to demonstrate the effectiveness of the proposed approaches.

This paper proposes an accurate cooperative control strategy to address the distributed adaptive consensus problem for multiple subsystems of the process industrial plants by constructing a two-layer node-to-node communication framework. In the present framework, each subsystem is modeled by an agent, and all the subsystems and the information flow are regarded as a multiagent uncertain system. By introducing proper assumptions, a class of distributed adaptive consensus protocol for accurate cooperative control is designed by adaptive weighting factors, appropriate feedback gains, and limited state information. It shows that distributed adaptive consensus of accurate cooperative control can be achieved for closed-loop multiagent uncertain systems with the two-layer node-to-node communication framework, if each follower is affected by at least one leader for some uniformly bounded communication time intervals. The results are further extended to nonlinear situations. Two application examples are presented to verify the effectiveness of the proposed approaches.

This paper proposes an adaptive explicit synchronization framework to address the cooperative control for heterogeneous uncertain dynamical networks under switching communication topologies. The main contribution is to develop an adaptive explicit synchronization algorithm, in which the synchronization state can be completely tracked by each agent in real time rather than only be measured after the synchronization process of all agents is over. By introducing appropriate assumptions, a class of adaptive explicit synchronization protocols is designed by using a combination of the virtual leader's states, the neighboring agents' relative information, distributed feedback gain, and distributed average weighted parameters. It is proved in the sense of Lyapunov that, if the dwell time is larger than a positive threshold, the cooperative control problem for the closed-loop heterogeneous uncertain dynamical networks under switching of strongly-connected communication topologies can be solved by the proposed adaptive explicit synchronization algorithm. Furthermore, by assuming that the topology is frequently strongly-connected, it shows that intermittent adaptive explicit synchronization can be achieved with well-designed control parameters. Two examples are presented to demonstrate the effectiveness of the proposed theory.

This paper proposes a global cooperative control framework to address leader-follower consensus of constraints subsystems of industrial plants, in which each subsystem is modeled as an agent and all the subsystems and networks of information flow construct a multiagent system. The focus of this paper is to solve the global leader-follower consensus for multiagent systems with input saturation via low-high gain feedback approach and parametric algebraic Riccati equation approach, in which the feedback gain design is distributed and decoupled from network topologies. By introducing appropriate assumptions, a class of low-high gain feedback protocol is designed based on the states of local neighbors to reach the global stability. It is proved in the sense of Lyapunov that, if the dwell time is larger than a positive threshold, the global leader-follower consensus for the closed-loop linear multiagent systems with input saturation under the derived topology containing a directed spanning tree can be achieved. The results are further extended to leader-follower consensus for nonlinear multiagent systems with the design of nonlinear low-high gain feedback protocol. As industrial applications of the proposed low-high gain scheduling approaches, the controller design of vibration in mechanical systems and satellite formation systems are revisited. Numerical simulations with cooperative control of industries subsystems show the effectiveness of the proposed approach.