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In this article, the set stabilization problem of Boolean control networks (BCNs) using output-feedback controllers is investigated comprehensively. First, a novel method for stabilizing BCNs, namely, the state dynamics matrix-based method, is proposed. Then, based on the above method, the time-invariant output-feedback (TIOF) and the time-variant output-feedback (TVOF) laws are derived. The necessary and sufficient conditions of both TIOF and TVOF stabilization of BCNs are also given. In addition, corresponding algorithms are provided such that all possible TIOF stabilizers and TVOF stabilizers for given BCNs can be designed. Finally, some illustrative examples are demonstrated, where the results are discussed and the effectiveness of the presented method is validated. [ABSTRACT FROM AUTHOR]

This article focuses on the problem of adaptive finite-time tracking control for nonlinear stochastic systems under asymmetric constraints based on dynamic event-triggering control. Different from the existing works, a novel adaptive tracking control algorithm is proposed with asymmetric time-varying constraints and dynamic event-triggering mechanism. First, to constrain the state variable within given time-varying boundaries, a novel predefined-time performance function is constructed. Second, a novel barrier function related to state variable is constructed, by means of which the state variable is directly constrained within the asymmetric time-varying boundaries without the virtual controller. In addition, by establishing a novel dynamic function, we propose a dynamic event-triggering mechanism, and then design controller accordingly, which can reduce computation burdens and save the network resources. By the aid of the Lyapunov stability theory, it is proved that the system tracking error converges to an adjustable bounded set in probability in a finite time and all state variables are successfully constrained into the asymmetric time-varying boundaries. Finally, the effectiveness of the proposed control algorithm is verified by a simulation example. [ABSTRACT FROM AUTHOR]

This article investigates the problems of structural balance and distributed control for a high-order signed network with generic linear dynamics. A novel approach is proposed to analyze the structural balance of a general network based on the strongly connected components of the topology graph and the Frobenius normal form of the adjacency matrix. To address the distributed control of high-order signed networks, both state-feedback and observer-type algorithms are developed on the basis of low-gain strategies, where a unified framework is presented to construct full- or reduced-order observers. The dynamical behaviors of the signed network under the proposed algorithms are systematically explored, where the nontrivial final network state is computed by employing an eigenvector-based approach. The theoretical results are illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

Verification is a critical process in the development of engineered systems. Through verification, engineers gain confidence in the correct functionality of the system before it is deployed into operation. Traditionally, verification strategies are fixed at the beginning of the system’s development and verification activities (VAs) are executed as the development progresses. Such an approach appears to give inferior results as the selection of the VAs does not leverage information gained through the system’s development process. In contrast, a set-based design (SBD) approach to verification, where VAs are dynamically selected as the system’s development progresses, has been shown to provide superior results. However, its application under realistic engineering scenarios remains unproven due to the large size of the verification tradespace. In this work, we propose a parallel tempering approach (PTA) to efficiently explore the verification tradespace. First, we formulate an exploration of the verification tradespace as a tree search problem. Second, we design a parallel tempering (PT) algorithm by simulating several replicas of the verification process at different temperatures to obtain a near-optimal result. Third, We apply the PT algorithm to all possible verification states to dynamically identify near-optimal results. The effectiveness of the proposed PTA is evaluated on a partial model of a notional satellite optical instrument. [ABSTRACT FROM AUTHOR]

This article proposes a unified framework to investigate the leader-following and leaderless consensus problem of general linear multiagent systems under the directed communication topology only containing a spanning tree. To further reduce the use of resources for the control objective, an energy-saving control algorithm is introduced composed of double dynamic event-triggered mechanisms that work independently: one intends to control the communication of agents with their neighbors and the other to decide the update of controllers. It is shown that the control algorithm performs well compared to most existing control algorithms in terms of the communication cost between agents and the update cost of controllers. In addition, a new procedure to choose parameters is obtained by the developed Lyapunov stabilty method, with the potential of achieving less conservative parameters than related results. Finally, the effectiveness of the proposed control scheme is verified by numerical simulations. [ABSTRACT FROM AUTHOR]

This article proposes static/dynamic event-triggered and self-triggered discrete gain scheduled control with a designable parametric minimal interevent time (MIET) to achieve semiglobal stabilization of linear systems with input constraints. First, a novel static event-triggered discrete gain scheduled control, which can improve the control performance and simultaneously save the communication resources, is proposed by utilizing the properties of the parametric Lyapunov equation (PLE). Moreover, the static self-triggered mechanism, in which the next control law updates based on the previous triggered states, is also designed to avoid the monitoring of all states. In order to further increase the interevent times (IETs), the corresponding dynamic event-triggered and self-triggered discrete gain scheduled control are designed, respectively. All the proposed algorithms can not only avoid the Zeno phenomenon but also provide a designable parametric MIET. This allows to easily find a tradeoff between the IETs and the control performance by adjusting the only design parameter. In addition, by exploiting the properties of the PLE, the designed algorithms avoid the complicated relationship between the MIET and the system matrices. In some cases, the MIET can totally avoid the relationship with the system itself and be designed as an arbitrarily large bounded constant. Finally, applications to the spacecraft rendezvous system show the effectiveness of the established algorithms. [ABSTRACT FROM AUTHOR]

In time-series forecasting, it is an important task to make an accurate and interpretable long-term prediction. In this article, we present a novel approach developed from the perspective of granular computing (GrC) to realize the long-term prediction of time series. The proposed method first employs a sliding window strategy to smooth on the raw time series. Subsequently, the smoothed time series is transformed into the corresponding granular time series that is depicted by evolving shape with the aid of the clustering algorithm based on the dynamic time warping (DTW) distance. Finally, a Takagi–Sugeno (TS) architecture-like granular model (GrM) is formed by deriving the relations implying in the granular time series and offers the granular output in the numeric vector format. The GrM adopts the pattern-to-pattern inference mechanism to realize the long-term prediction of time series at the vector level. Experiments on several datasets demonstrate that the proposed method not only has the ability to circumvent the cumulative error but also makes the resulting GrM equip better interpretability. [ABSTRACT FROM AUTHOR]

Earth observation resources are becoming increasingly indispensable in disaster relief, damage assessment, and other related domains. Many unpredictable factors, such as changes in observation task requirements, bad weather, and resource malfunctions, may cause the scheduled observation scheme to become infeasible. In these cases, it is crucial to promptly reformulate high-quality observation schemes while exerting minimal negative effects on the previously scheduled tasks. Accordingly, in this study, a bottom-up distributed coordination framework together with an improved contract net is proposed, aiming to facilitate dynamic task replanning for heterogeneous Earth observation resources. This hierarchical framework consists of three levels: 1) neighboring resource coordination; 2) single planning center coordination; and 3) multiple planning center coordination. The observation tasks affected by unpredicted factors are managed along with a bottom-up route from resources to planning centers. This bottom-up distributed coordination framework transfers part of the computing load to various nodes of the observation systems to plan tasks more efficiently and robustly. To support the prompt replanning of multiple tasks to proper Earth observation resources in dynamic environments, we propose a multiround combinatorial allocation (MCA) method. Moreover, a new float interval-based local search algorithm is proposed to quickly obtain a promising replanning scheme. The simulation results demonstrate that the MCA method can achieve a better task completion rate for large-scale tasks with satisfactory time efficiency. In addition, this method can efficiently obtain replanning schemes based on original schemes in dynamic environments. [ABSTRACT FROM AUTHOR]

This article presents a model-based motion planning and control system for autonomous vehicles and its experimental validation. The system consists of four modules: 1) global routing; 2) behavior planner; 3) local trajectory generation; and 4) trajectory tracking. The algorithm and software of each module are detailed, including a behavior planner with unified models to handle typical scenarios in both highway and urban driving, a deterministic sampling algorithm for robust responsive trajectory generation, and a dynamics-and-delay-aware preview algorithm to achieve accurate trajectory tracking. The developed system is implemented and tested at the Mcity test facility with a full-size automated car and a dozen of challenging traffic scenarios. [ABSTRACT FROM AUTHOR]

This article deals with the fully distributed dual-terminal dynamic event-triggered bipartite output containment control of heterogeneous linear multiagent systems (HLMASs) subject to actuator faults. First, a class of fully distributed dynamic event-triggered observers is proposed over the directed signed communication network for each follower to estimate the leaders’ system matrices and (symmetric) combination states, which are merely available to a small fraction of followers. Then, based on the estimations of leaders’ system matrices, adaptive algorithms are employed to solve the regulator equations. Furthermore, the fully distributed observer-based dynamic event-triggered controllers are proposed by integrating the adaptive gains to compensate for actuator faults. Dual-terminal dynamic event-triggered mechanisms (ETMs) are addressed to exclude not only continuous control updates but also continuous communication among agents. Correspondingly, some mild criteria and the rigorous proofs are established to ensure the implementation of bipartite output containment through the Lyapunov stability theory. Moreover, the strictly positive lower bounds of intervals between two adjacent event-triggered time instants are derived, which indicates that Zeno behavior is ruled out in the dual-terminal dynamic ETMs. Finally, numerical simulations are performed to demonstrate the theoretical analysis. [ABSTRACT FROM AUTHOR]

This article investigates the multitarget tracking problem for multiple Lagrangian plants (MLPs) in the presence of sampled-data interactions, uncertain dynamic terms, and input-to-output redundancy. Two classes of impulsive estimator-based control (IEC) algorithms, including the first- and higher-order IEC algorithms, are newly designed to observe the dynamic uncertain terms, estimate the states of the multiple targets, and finally solve the above-mentioned problem. Based on the properties of the small-value norms, Lyapunov stability theory, Schur stability theory, and Hurwitz criterion, some sufficient conditions and the convergence radius are derived for guaranteeing the convergence of these IEC algorithms. Finally, numerical simulations are performed on networked heterogeneous manipulators to verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this article, we study the consensus issues of multiagent systems (MASs) without any information of the system model by using the reinforcement learning (RL) method and event-based control strategy. First, we design an adaptive event-based consensus control protocol using the local sampled state information so that the consensus errors of all agents are uniformly ultimately bounded. The validity of the above event-triggered adaptive control protocol is confirmed by excluding the Zeno behavior within finite time. Then, based on the RL approach, we present a model-free algorithm to get the feedback gain matrix, and accomplish constructing the adaptive event-triggered control strategy without the knowledge of model information. Distinct with the existing related works, this RL-based event-triggered adaptive control algorithm only relies on the local sampled state information, irrelevant to any model information or global network information. Finally, we provide some examples to demonstrate the validity of the above adaptive event-based consensus algorithm. [ABSTRACT FROM AUTHOR]

Mission aborting has recently attracted great attention, where mission abort rules (MARs) have been modeled and optimized for different types of technological systems aiming to effectively mitigate the risk of system losses. However, none of the existing works have considered systems with multiple work-sharing units. This article makes advancements in the state of the art by modeling condition-based MARs for nonrepairable work-sharing systems that must perform a specified amount of work during the primary mission (PM). The MAR considered presumes aborting the PM to prevent considerable damage when the number of available units reduces to a certain number ${k}$ while the amount of work accomplished in the PM is less than ${L}$ (${k}$). After the PM abortion, a rescue procedure (RP) is executed by the remaining units to survive the system. Dynamic operating conditions during PM and RP are considered. A probabilistic model-based numerical algorithm is proposed to evaluate several performance metrics, including mission success probability, RP success probability, damage avoidance probability, and expected cost of losses. The MAR optimization problem for minimizing the expected cost of losses is formulated and solved using the genetic algorithm. An example of a chemical reactor system is provided to demonstrate the proposed algorithm as well as the benefit of MARs optimized in comparison to the “no abort” policy and the most conservative abort policy (the PM aborts upon the failure of any unit). Effects of several model parameters on the system performance metrics and optimization solutions are also examined through examples. [ABSTRACT FROM AUTHOR]

This article deals with the problem of mobile robot path planning in an unknown environment that contains both static and dynamic obstacles, utilizing a reinforcement learning approach. We propose an improved Dyna- ${Q}$ algorithm, which incorporates heuristic search strategies, simulated annealing mechanism, and reactive navigation principle into ${Q}$ -learning based on the Dyna architecture. A novel action-selection strategy combining $\varepsilon $ -greedy policy with the cooling schedule control is presented, which, together with the heuristic reward function and heuristic actions, can tackle the exploration-exploitation dilemma and enhance the performance of global searching, convergence property, and learning efficiency for path planning. The proposed method is superior to the classical ${Q}$ -learning and Dyna- ${Q}$ algorithms in an unknown static environment, and it is successfully applied to an uncertain environment with multiple dynamic obstacles in simulations. Further, practical experiments are conducted by integrating MATLAB and robot operating system (ROS) on a physical robot platform, and the mobile robot manages to find a collision-free path, thus fulfilling autonomous navigation tasks in the real world. [ABSTRACT FROM AUTHOR]

Evolutionary games and population dynamics are finding increasing applications in design learning and control protocols for a variety of resource allocation problems. The implicit requirement for full communication has been the main limitation of the evolutionary game dynamic approach in engineering tasks with various information constraints. This article intends to build population games and dynamics with both static and dynamical graphical communication structures. To this end, we formulate a population game model with graphical strategy interactions and derive its corresponding population dynamics. In particular, we first introduce the concept of generalized Nash equilibria for population games with graphical strategy interactions, and establish the equivalence between the set of generalized Nash equilibria and the set of rest points of its distributed population dynamics. Furthermore, the conditions for convergence to generalized Nash equilibrium and particularly to Nash equilibrium are obtained for the distributed population dynamics with both static and dynamical graphical structures. These results provide a new approach to design distributed Nash equilibrium seeking algorithms for population games with both static and dynamical communication networks, and hence, expand the applicability of the population game dynamics in the design of learning and control protocols under distributed circumstances. [ABSTRACT FROM AUTHOR]

This article is focused on the design of an output tracking control scheme for a class of continuous-time periodic piecewise time-varying systems (PPTVSs) with actuator saturation and nonlinear perturbations. The energy-to-peak tracking performance is studied based on an equivalent condition on the definiteness property of matrix polynomials. Considering the actuator saturation and nonlinear perturbation, matrix polynomial-based sufficient conditions are derived through the Lyapunov method using periodic matrix functions. From a perspective of subinterval segmentation aimed at PPTVSs, the proposed conditions can achieve less conservatism for tracking the output of a periodic time-varying reference system, while the controller gains can be computed using convex optimization. Moreover, a heuristic algorithm is constructed to simultaneously guarantee the closed-loop state convergence and the output tracking performance. The reduction in conservatism and the effectiveness of algorithm are demonstrated by illustrative case studies. [ABSTRACT FROM AUTHOR]

This article is dedicated to solving the coordination problem using a reset algorithm that consists of linear impulsive dynamics, over which antagonistic reciprocity among interacting individuals is inevitable. It shows that reciprocal agents are convergent (including consensus and clusters) whenever the scaling parameters of an agent, corresponded to continuous/discrete dynamics, enjoy the same proportion value that matches to all agents. Otherwise, all participating agents share nothing eventually, that is, they achieve stability. This immediately gives rise to that the final aggregated values of agents are entirely determined by the underlying parameters, on condition that the connection property of communication topologies is preserved. Therefore, the proposed setup features a unified perspective on consensus, clusters (including bipartite consensus), and stability that are separately studied in most of the existing literature. The developed method is well supported via numerical examples. [ABSTRACT FROM AUTHOR]

In this article, a two-level planner is proposed to provide a solution to the autonomous luggage trolley collection problem at the airport. In the higher level planner, a decision-making problem is tackled where a sequence of luggage trolleys is determined with which the robot can collect them one by one. Based on the traditional traveling salesman problem (TSP), this decision-making problem is formulated as an open dynamic traveling salesman problem with fixed start (ODTSP-FS). Incorporating the modified transition rule, elitist global update rule, and additional local update rule, an efficient algorithm is proposed to handle this decision-making problem. The experimental results demonstrate that the proposed algorithm achieves fast convergence and smaller cost compared with the state-of-the-art algorithms. In the lower level planner, based on the pipeline of rapid-exploring random tree (RRT) scheme, a novel real-time path planning algorithm is introduced, which can adjust itself to moving obstacles and moving targets by retaining the whole tree and using two rewiring strategies. Finally, the proposed two-level planner is evaluated in a simulation environment similar to the airport to validate the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article designs the static and dynamic event-triggered control (ETC) and self-triggered control (STC) algorithms to achieve the semiglobal stabilization of discrete-time systems with input constraints. First, a novel static ETC algorithm based on the discrete-time parametric Lyapunov equation (DPLE) is designed. In order to further increase the interevent times (IETs), the corresponding dynamic ETC is designed. Next, both static and dynamic STC, where the next control law updates depend on the previous triggered states, are proposed to avoid monitoring the measurement errors. The proposed algorithms are not only capable of reducing the number of transmissions significantly but also build a very simple and clear relationship between the only design parameter and the nontrivial IET (NIET). This allows us to change regularly IETs by adjusting the design parameter so that the nontriviality of static and dynamic ETC and STC is guaranteed and a tradeoff between the IETs and the control performance can be easily found. Specifically, by exploring the properties of DPLE, the designed algorithms avoid the complex relationship between the nontrivial condition and the system matrices. Finally, the designed static and dynamic ETC and STC algorithms are applied to the design of the spacecraft rendezvous control system and their effectiveness is verified by simulation results. [ABSTRACT FROM AUTHOR]

We investigate a general solution based on reinforcement learning for the agile satellite scheduling problem. The core idea of this method is to determine a value function for evaluating the long-term benefit under a certain state by training from experiences, and then apply this value function to guide decisions in unknown situations. First, the process of agile satellite scheduling is modeled as a finite Markov decision process with continuous state space and discrete action space. Two subproblems of the agile Earth observation satellite scheduling problem, i.e., the sequencing problem and the timing problem are solved by the part of the agent and the environment in the model, respectively. A satisfactory solution of the timing problem can be quickly produced by a constructive heuristic algorithm. The objective function of this problem is to maximize the total reward of the entire scheduling process. Based on the above design, we demonstrate that Q-network has advantages in fitting the long-term benefit of such problems. After that, we train the Q-network by Q-learning. The experimental results show that the trained Q-network performs efficiently to cope with unknown data, and can generate high total profit in a short time. The method has good scalability and can be applied to different types of satellite scheduling problems by customizing only the constraints checking process and reward signals. [ABSTRACT FROM AUTHOR]

In this article, we propose two model-free algorithms using state or output feedback for saturated discrete-time multiagent systems (SDTMASs) to attain global containment control. In most previous works, the control input can avoid saturation by utilizing the low gain feedback (LGF) method whereas requiring the knowledge of agent dynamics, and SDTMASs just can attain semi-global containment control. Distinct with the previous works, first, based on the $Q$ -learning (QL) technique, this article defines a $Q$ -function and deduces the corresponding QL Bellman equation, which is the most important part of the QL algorithm. Then, in order to solve the QL Bellman equation, we propose two iterative model-free algorithms using state and output feedback, and the LGF matrix can be acquired from that solution directly. Furthermore, under the state and output feedback control protocols with the feedback matrices obtained from the proposed model-free algorithms, the SDTMASs can achieve global containment control instead of semi-global containment control. Finally, we present some simulations to confirm the validity of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In the study, a MIMO model-free-adaptive-iterative-learning-control-based (MFAILC-based) consensus tracking scheme for multiagent systems (MASs) has been proposed. The dynamics of agents are heterogeneous and unknown. And the compact form dynamic linearization (CFDL) technique is utilized to describe the unknown nonlinear dynamics of agents along iteration axis. Then, the MIMO MFAILC-based consensus tracking algorithm is proposed for MASs under fixed topology. From the proof, we can obtain that the consensus tracking error can converge to zero along iteration axis asymptotically. Next, the MFAILC-based consensus tracking algorithm is extended to controlling the MASs under iteration-switching topologies and the MASs with external disturbances, respectively. Compared with prior work, the main features of this article are the MFAILC-based consensus strategy can be utilized for MIMO MASs, and the framework of robust MFAILC is built for MIMO MASs with external disturbances. Finally, three simulations are given to verify the effectiveness of the consensus strategy for MASs under fixed and switching topology and MASs with external disturbances, respectively. [ABSTRACT FROM AUTHOR]

Packet routing is one of the fundamental problems in computer networks in which a router determines the next-hop of each packet in the queue to get it as quickly as possible to its destination. Reinforcement learning (RL) has been introduced to design autonomous packet routing policies with local information of stochastic packet arrival and service. However, the curse of dimensionality of RL prohibits the more comprehensive representation of dynamic network states, thus limiting its potential benefit. In this article, we propose a novel packet routing framework based on multiagent deep RL (DRL) in which each router possess an independent long short term memory (LSTM) recurrent neural network (RNN) for training and decision making in a fully distributed environment. The LSTM RNN extracts routing features from rich information regarding backlogged packets and past actions, and effectively approximates the value function of Q-learning. We further allow each route to communicate periodically with direct neighbors so that a broader view of network state can be incorporated. The experimental results manifest that our multiagent DRL policy can strike the delicate balance between congestion-aware and shortest routes, and significantly reduce the packet delivery time in general network topologies compared with its counterparts. [ABSTRACT FROM AUTHOR]

This article investigates a political party or an association social network where members share a common set of beliefs. In modeling it as a distributed iterative algorithm with network dynamics mimicking the interactions between people, the problem of interest becomes that of determining: 1) the conditions when convergence happens in finite time and 2) the corresponding steady-state opinion. For a traditional model, it is shown that finite-time convergence requires a complete topology and that by removing neighbors with duplicate opinions reduces in half the number of links. Finite-time convergence is proved for two novel models even when nodes contact two other nodes of close opinion. In a deterministic setting, the network connectivity influences the final consensus and changes the relative weight of each node on the final value. In the case of mobile robots, a similar communication constraint is present which makes the analysis of the social network so relevant in the domain of control systems as a guideline to save resources and obtain finite-time consensus. Through simulations, the main results regarding convergence are illustrated paying special attention to the rates at which consensus is achieved. [ABSTRACT FROM AUTHOR]

Recommendation systems are gaining popularity with the proliferation of the Internet of People (IoP). The popularity and use of online social networks facilitate integrating these social relationships with recommender systems under a single framework of IoP. This article proposes a new approach for item recommendation based on the diffusion method that combines user relationships in social networks with user–item relationships derived from the IoP. Especially, a resource redistribution process is explored in the user–object network that gives mass diffusion a higher recommendation accuracy and heat conduct a greater diversity by considering the social degree of users whilst calculating the user degree in the network. A tuning parameter is introduced to adjust the weight of resources that the objects finally receives from users based on their social relationships. Finally, extensive experiments conducted on the real-world datasets which contain friendship relationships, demonstrate the efficiencies of our proposed method in achieving notable performance improvements in terms of the recommendation accuracy, service diversity, and practical dependability. [ABSTRACT FROM AUTHOR]

The sequential fusion estimation for multirate multisensor dynamic systems with heavy-tailed noises and unreliable measurements is an important problem in dynamic system control. This work proposes a sequential fusion algorithm and a detection technique based on Student’s $t$ -distribution and the approximate $t$ -filter. The performance of the proposed algorithm is analyzed and compared with the Gaussian Kalman filter-based sequential fusion and the $t$ -filter-based sequential fusion without detection technique. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective and robust to unreliable measurements. The $t$ -filter-based sequential fusion algorithm is shown to be the generalization of the classical Gaussian Kalman filter-based optimal sequential fusion algorithm. [ABSTRACT FROM AUTHOR]

We investigate the problems of resilient cluster consensus in directed networks under three types of multiagent dynamics, namely, continuous-time multiagent systems, discrete-time multiagent systems, and switched multiagent systems composed of both continuous-time and discrete-time components. Resilient cluster censoring strategies are proposed to ensure cluster consensus against locally bounded Byzantine nodes in a purely distributed manner, where neither the number/identity of Byzantine nodes nor the division of clusters is assumed. We do not require complicated algebraic conditions or any balance conditions over intercluster structures, distinguishing the current work from previous results on cluster consensus problems besides a fortiori the attack-tolerant feature. Sufficient conditions are established in all the three scenarios based on the graph robustness. Furthermore, we solve the heterogenous cluster robustness problems and resilient scaled cluster consensus problems as extensions. The theoretical results are illustrated through numerical examples including the Santa Fe collaboration network. [ABSTRACT FROM AUTHOR]

The minimum dominating set (MDS) of the network is a node subset of smallest size that every node in the network is either in this subset or is adjacent to one or more nodes of this subset. MDS has found wide applications, ranging from network monitoring, routing, to epidemic control, and text processing. However, the majority of existing studies on MDS problem are confined to single networks. In real world, more and more complex systems consist of a set of elements linked up by different types of connections, which are best modeled as multiplex networks with interacting network layers. Though vastly important, the MDS of the multiplex networks has not yet been formally defined and its application and identification remain open issues. In this article, we present the definition of the MDS of the multiplex network and show some of its possible applications. For solving the MDS problem of the multiplex network, we built a spin-glass model and solve it through the belief-propagation (BP) equations under the replica symmetry mean-field theory. As a consequence, we can predict the relative size of the MDS of the multiplex network theoretically and we can propose a BP-guided decimation algorithm to construct an approximate optimal dominating set in practice. Then the algorithm is improved in both accuracy and efficiency by embedding a novel multiplex network-oriented leaf-removal strategy. The effectiveness of the proposed algorithms is finally verified by comparing with other methods on a number of the multiplex network examples. [ABSTRACT FROM AUTHOR]

This article discusses the control design and experiment validation of a flexible two-link manipulator (FTLM) system represented by ordinary differential equations (ODEs). A reinforcement learning (RL) control strategy is developed that is based on actor–critic structure to enable vibration suppression while retaining trajectory tracking. Subsequently, the closed-loop system with the proposed RL control algorithm is proved to be semi-global uniform ultimate bounded (SGUUB) by Lyapunov’s direct method. In the simulations, the control approach presented has been tested on the discretized ODE dynamic model and the analytical claims have been justified under the existence of uncertainty. Eventually, a series of experiments in a Quanser laboratory platform are investigated to demonstrate the effectiveness of the presented control and its application effect is compared with PD control. [ABSTRACT FROM AUTHOR]

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

This article concerns the distributed optimal observer design, which aims to estimate the discrete-time nonlinear leader state for all followers with optimum online. The communication constraint and no prior knowledge of the leader’s dynamics cause a challenging task about the observer design. To this end, an adaptive critic design-based distributed optimal observer is developed via the actor–critic framework. The critic network is employed to approximate the cost function. The action network produces the optimal correction policy of the observer. Through convergence analysis, the overall estimation error and the weight estimation errors of the critic network and the action network are demonstrated to be all uniformly ultimately bounded (UUB). The simulation results verify the effectiveness of the developed observer. [ABSTRACT FROM AUTHOR]

Autonomous vehicles’ dynamics stability control is one key issue to ensure safety of self-driving. However, vehicle uncertainties and time-varying parameters could weaken the performance of autonomous vehicle stability control. Therefore, this article proposes a novel robust linear matrix inequality (LMI)-based $H$ -infinite feedback algorithm for vehicle dynamics stability control, and this algorithm controls vehicle steering system and brake system via direct yaw moment control (DYC) and active front steering control (AFS). The presented controller is robust against vehicle parametric uncertainties, including the vehicle mass and vehicle longitudinal velocity. A linear parameter varying lateral model is constructed utilizing polytopic uncertainty method considering time-varying vehicle longitudinal velocity and mass, where a polytope that contains finite vertices is established to contain all of the possible selections for uncertainty parameters. Then, the $H$ -infinite feedback controller integrating DYC and AFS is derived via LMI technique. Finally, experimental results based on hardware-in-the-loop (HIL) platform illustrate that the presented controller has better performance of ensuring autonomous vehicle dynamics stability than other controllers. [ABSTRACT FROM AUTHOR]

Dynamic emotion is typically used to facilitate human–machine interactions. Conversational data from social media contain a considerable amount of useful information, and such data are the foundation for researching dynamic and artificial emotion. At present, most human–machine interaction systems focus on the complexity and accuracy of the dialog but neglect the emotional characteristics of the speaker. When generating a dialog considering the emotional personality of the interlocutor, controlling, and guiding the dialog to a specified direction are essential. This article presents a system for studying dynamic emotions in human-computer interaction from the perspective of emotional transfer and guidance. Based on the emotional state of the interlocutor and the distribution of emotional transfer, the process of emotional transfer is simulated and sampled, and the sequence of emotional guidance is generated. In this system, two algorithms are proposed. A generative Markov chain Monte Carlo (GEN-MCMC) algorithm is proposed to generate a variety of emotional transfer sequences that fit the talke’s personality dynamically based on the real-world dialog. Further, a guiding MCMC (GUI-MCMC) algorithm-based GEN-MCMC is proposed to generate the emotional guiding sequences. The generated emotional sequences by GEN-MCMC were evaluated in two aspects: 1) consistency and 2) diversity. The experimental results show that the GEN-MCMC algorithm performs better than the general sequence generation algorithm in terms of consistency and diversity in generating emotional states. The GUI-MCMC was able to generate a proper stimulus sequence when given the first and target emotions. An emotional stimulus sequence can simulate the emotional transfer of the interlocutor in the process of dialogue, and give the observer appropriate reference to guide and control the emotions of dialogue. The experimental results show that the proposed system can effectively model the dynamic emotion in emotional transfer and guidance, which can be further used to build chat robots, intelligent assistants, and human–machine interaction systems. The models can also be used for emotional induction and enhance the feel-good or feel-terrible factor in human–machine communication applications, such as medical treatment of mental diseases, interrogation, and psychological attack and defense. [ABSTRACT FROM AUTHOR]

In this article, a radial basis function neural network-based adaptive iterative learning fault-tolerant control (RBFNN-AILFTC) algorithm is developed for subway trains subject to the time-iteration-dependent actuator faults and speed constraint by using the multiple-point-mass dynamic model. First, the RBFNN is utilized to approximate the time-iteration-dependent unknown nonlinearity of the subway train system; then, the iterative learning mechanism is used to tackle the outstanding repetitive operational pattern of a subway train which runs from one station to the next strictly according to the operation timetable schedule every day within the finite time interval, and the adaptive mechanism is designed for dealing with the time and the iteration-varying factors of the subway train. Second, a barrier composite energy function technique is exploited to obtain the convergence property of the proposed RBFNN-AILFTC scheme for subway train system, which can guarantee that the tracking error is asymptotic convergence along the iteration axis, meanwhile keep the speed profile of the subway train system satisfies the constraint. Finally, a subway train simulation is shown to verify the effectiveness of the theoretical studies. [ABSTRACT FROM AUTHOR]