查看更多>>摘要：This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric games.In the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents'best response(BR),is technically the optimal strategy for the leader.However,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of states.To this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent's expected utility.The authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE strategy.Then the authors turn to multi-player models,where there exists one player adopting an equalizer ZD strategy.The authors give bounds of the weighted sum of opponents's utilities,and compare it with the SSE strategy.Finally,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.
查看更多>>摘要：This paper is concerned with the optimal threshold selection and resource allocation prob-lems of quantized identification,whose aims are improving identification efficiency under limited re-sources.Firstly,the first-order asymptotically optimal quantized identification theory is extended to the weak persistent excitation condition.Secondly,the characteristics of time and space complexities are established based on the Cramér-Rao lower bound of quantized systems.On these basis,the op-timal selection methods of fixed thresholds and adaptive thresholds are established under aperiodic signals,which answer how to achieve the best efficiency of quantized identification under the same time and space complexity.In addition,based on the principle of maximizing the identification efficiency under a given resource,the optimal resource allocation methods of quantized identification are given for the cases of fixed thresholds and adaptive thresholds,respectively,which show how to balance time and space complexity to realize the best identification efficiency of quantized identification.
查看更多>>摘要：This paper focuses on linear-quadratic(LQ)optimal control for a class of systems governed by first-order hyperbolic partial differential equations(PDEs).Different from most of the previous works,an approach of discretization-then-continuousization is proposed in this paper to cope with the infinite-dimensional nature of PDE systems.The contributions of this paper consist of the following aspects:1)The differential Riccati equations and the solvability condition of the LQ optimal control problems are obtained via the discretization-then-continuousization method.2)A numerical calculation way of the differential Riccati equations and a practical design way of the optimal controller are proposed.Meanwhile,the relationship between the optimal costate and the optimal state is established by solving a set of forward and backward partial difference equations(FBPDEs).3)The correctness of the method used in this paper is verified by a complementary continuous method and the comparative analysis with the existing operator results is presented.It is shown that the proposed results not only contain the classic results of the standard LQ control problem of systems governed by ordinary differential equations as a special case,but also support the existing operator results and give a more convenient form of computation.
查看更多>>摘要：This paper investigates the cooperative adaptive optimal output regulation problem of continuous-time linear multi-agent systems.As the multi-agent system dynamics are uncertain,solving regulator equations and the corresponding algebraic Riccati equations is challenging,especially for high-order systems.In this paper,a novel method is proposed to approximate the solution of regulator equations,i.e.,gradient descent method.It is worth noting that this method obtains gradients through online data rather than model information.A data-driven distributed adaptive suboptimal controller is developed by adaptive dynamic programming,so that each follower can achieve asymptotic tracking and disturbance rejection.Finally,the effectiveness of the proposed control method is validated by simulations.
查看更多>>摘要：Network localization serves as a fundamental component for enabling various position based operations in multi-agent systems,facilitating tasks like target searching and formation control by providing accurate position information for all nodes in the network.Network localization focuses on the challenge of determining the positions of nodes within a network,relying on the known positions of anchor nodes and internode relative measurements.Over the past few decades,distributed network localization has garnered significant attention from researchers.This paper aims to provide a review of main results and advancements in the field of distributed network localization,with a particular focus on the perspective of graph Laplacian.Owning to its favorable characteristics,graph Laplacian unifies various network localization,even when dealing with diverse types of internode relative measurements,into a unified protocol framework,which can be constructed by a linear method and ensure the global convergence.
查看更多>>摘要：This paper is devoted to event-triggered synchronization of delayed memristive neural net-works with H∞ and passivity performance.The aim is to guarantee the exponential synchronization and mixed H∞ and passivity control for memristive neural networks by using event-triggered control.Firstly,a switching system is constructed under the event-triggered control strategy.Then,by adopting a piece-wise Lyapunov functional,a sufficient condition is established for the exponential synchroniza-tion and mixed H∞ and passivity performance.Moreover,an event-triggered controller design scheme is proposed using matrix decoupling method.Finally,the effectiveness of the designed controller is exemplified by a numerical example.
查看更多>>摘要：This paper investigates the stability problem for sampled-data systems by adopting a re-fined semi-looped-functional,which is with the following two improvements.Firstly,the new functional term is with a new integral vector η0,which contains sampling information of the systems and asso-ciates two commonly used vectors.Secondly,the vector η0 is combined into various zero equations for processing the functional,especially where a new equation is derived from η0.Based on the refined functional,further stability results for sampled-data systems are obtained.And the effectiveness of the results is numerically verified through two examples at the end.
查看更多>>摘要：In this paper,the authors consider how to design defensive countermeasures against DoS attacks for remote state estimation of multiprocess systems.For each system,a sensor will measure its state and transmits the data packets through an unreliable channel which is vulnerable to be jammed by an attacker.Under limited communication bandwidth,only a subset of sensors are allowed for data transmission,and how to select the optimal one to maximize the accuracy of remote state estimation is the focus of the proposed work.The authors first formulate this problem as a Markov decision process and investigate the existence of optimal policy.Moreover,the authors demonstrate the piecewise monotonicity structure of optimal policy.Given the difficulty of obtaining an optimal policy of large-scale problems,the authors develop a suboptimal heuristic policy based on the aforementioned policy structure and Whittle's index.Moreover,a closed form of the indices is derived in order to reduce implementation complexity of proposed scheduling policy and numerical examples are provided to illustrate the proposed developed results.
查看更多>>摘要：Mobile Edge Computing(MEC)provides communication and computational capabilities for the industrial Internet,meeting the demands of latency-sensitive tasks.Nevertheless,traditional model-driven task offloading strategies face challenges in adapting to situations with unknown network communication status and computational capabilities.This limitation becomes notably significant in complex industrial networks of high-speed railway.Motivated by these considerations,a data and model-driven task offloading problem is proposed in this paper.A redundant communication network is designed to adapt to anomalous channel states when tasks are offloaded to edge servers.The link switching mechanism is executed by the train according to the attributes of the completed task.The task offloading optimization problem is formulated by introducing data-driven prediction of communi-cation states into the traditional model.Furthermore,the optimal strategy is achieved by employing the informer-based prediction algorithm and the quantum particle swarm optimization method,which effectively tackle real-time optimization problems due to their low time complexity.The simulations illustrate that the data and model-driven task offloading strategy can predict the communication state in advance,thus reducing the cost of the system and improving its robustness.
查看更多>>摘要：The multi-UAV adversary swarm defense(MUASD)problem is to defend a static base against an adversary UAV swarm by a defensive UAV swarm.Decomposing the problem into task assignment and low-level interception strategies is a widely used approach.Learning-based approaches for task assignment are a promising direction.Existing studies on learning-based methods generally assume decentralized decision-making architecture,which is not beneficial for conflict resolution.In contrast,centralized decision-making architecture is beneficial for conflict resolution while it is often detrimental to scalability.To achieve scalability and conflict resolution simultaneously,inspired by a self-attention-based task assignment method for sensor target coverage problem,a scalable central-ized assignment method based on self-attention mechanism together with a defender-attacker pairwise observation preprocessing(DAP-SelfAtt)is proposed.Then,an imperative-priori conflict resolution(IPCR)mechanism is proposed to achieve conflict-free assignment.Further,the IPCR mechanism is parallelized to enable efficient training.To validate the algorithm,a variant of proximal policy optimiza-tion algorithm(PPO)is employed for training in scenarios of various scales.The experimental results show that the proposed algorithm not only achieves conflict-free task assignment but also maintains scalability,and significantly improve the success rate of defense.
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