中国科学院数学与系统科学研究院期刊网

2026年, 第39卷, 第2期 刊出日期:2026-04-08
  

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  • HONG Yiguang, FENG Jun-e, ZHANG Lijun, QI Hongsheng
    系统科学与复杂性(英文). 2026, 39(2): 481-482. https://doi.org/10.1007/s11424-026-6002-1
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  • LIN Zhonghao, ZENG Xianlin, HOU Jie, SUN Jian, CHEN Jie
    系统科学与复杂性(英文). 2026, 39(2): 483-510. https://doi.org/10.1007/s11424-026-5499-7
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    This paper presents a primal-dual prediction-correction (PD-PC) method for solving linearly constrained time-varying convex optimization problems, which frequently arise in control, signal processing, and online learning applications. The proposed method establishes a novel integration of primal-dual gradient dynamics with a discrete-time prediction-correction structure, specifically designed for problems with time-dependent linear constraints. A tunable memory parameter is introduced in the prediction phase to perform linear extrapolation using past iterates, enabling a flexible trade-off between the amount of historical information stored and the computational cost of correction. In the correction phase, primal and dual variables are updated via gradient descent-ascent iterations, thus maintaining the computational efficiency of a first-order method without requiring Hessian or high-order derivative computations. Theoretical analysis shows that the method achieves $\mathcal{O}(h^2)$ asymptotic tracking accuracy for both primal and dual variables, matching the state-of-the-art performance among first-order methods even in unconstrained settings. Numerical experiments on problems with both time-invariant and time-varying constraints validate the theoretical findings and demonstrate the method's effectiveness.
  • LIN Liquan, HUANG Jie
    系统科学与复杂性(英文). 2026, 39(2): 511-524. https://doi.org/10.1007/s11424-026-5466-3
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    The cooperative output regulation problem for unknown linear multi-agent systems has been studied by both policy-iteration method and value-iteration method via distributed internal model approach. However, the original results were limited to single-input single-output linear multi-agent systems under the assumption that the communication digraph is acyclic. Recently, the authors have extended the existing result to multi-input multi-output linear multi-agent systems over a general static and connected digraph by a more efficient value-iteration method. Since the policy-iteration method is simpler and has a much faster convergence rate than the value-iteration method, in this paper, the authors further apply the policy-iteration method to the cooperative output regulation problem of unknown multi-input multi-output multi-agent systems over a general static and connected digraph. Compared with the existing policy-iteration method, the proposed policy-iteration approach not only drastically reduces the computational cost, but also significantly weakens the solvability conditions. Moreover, by introducing a virtual exosystem, the proposed policy-iteration approach eliminates the need for employing a distributed observer. As a result, the data collection can start at any time, and the computing cost for each agent is also reduced.
  • ISIDORI Alberto
    系统科学与复杂性(英文). 2026, 39(2): 525-542. https://doi.org/10.1007/s11424-026-5616-7
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    In this paper, it is shown how a recent enhancement of a method for asymptotic stabilization of an MIMO nonlinear system via state-feedback suggested by Liberzon in the early 2000s can be profitably used to solve a problem of robust output regulation. The proposed method requires assumptions weaker than those proposed earlier in the literature and yields a simpler structure of the controller.
  • DISARò Giorgia, VALCHER Maria Elena
    系统科学与复杂性(英文). 2026, 39(2): 543-570. https://doi.org/10.1007/s11424-026-5521-0
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    In this paper, the problem of tracking a given reference output trajectory is investigated for the class of Boolean control networks, by resorting to their algebraic representation. First, the case of a finite-length reference trajectory is addressed, and the analysis and algorithms first proposed in (Zhang, et al., 2018) are extended to be able to deal with arbitrary initial conditions and to identify all possible solutions. The case of delayed tracking is also investigated. The approach developed for the finite-length case is then adjusted to cope with periodic reference output trajectories. First, exact tracking of periodic output trajectories from all possible initial states is considered and shown to be equivalent to exact tracking, from all possible initial states, of the finite length trajectory obtained by restricting the original one to a single period. Then, delayed tracking (both with an arbitrary delay and with a delay that is a multiple of the period) is explored. Several algorithms support the analysis and the numerical implementation of the necessary and sufficient conditions derived in this paper for the solvability of the various problems. The results of this paper are illustrated through examples.
  • WU Qi, LI Yuanlong, LIN Zongli
    系统科学与复杂性(英文). 2026, 39(2): 571-591. https://doi.org/10.1007/s11424-026-6109-4
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    This paper investigates the data-driven event-triggered control problem for unknown continuous-time linear systems. To reduce unnecessary data transmissions, a topology-aware dynamic event-triggered mechanism is proposed. Unlike strategies that rely solely on the magnitude of the measurement error, the proposed design incorporates the topological relationship between measurement errors and system states by integrating the Lyapunov-function matrix into the triggering condition. By identifying when the directional state-error interaction is favorable, the mechanism automatically decelerates the decay of the dynamic threshold, thereby extending inter-event intervals. A robust design framework is established using noisy offline data, where both the controller gain and triggering parameters are jointly determined via linear matrix inequalities (LMIs). Theoretical analysis guarantees exponential input-to-state stability (ISS) and excludes Zeno behavior. Simulation results validate the effectiveness of the method.
  • WANG Xiaochang, MARTIN Clyde
    系统科学与复杂性(英文). 2026, 39(2): 592-623. https://doi.org/10.1007/s11424-026-5385-3
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    In this paper, a classical topic from computer science is reexamined from the perspective of modern control theory. Vector addition systems, first introduced in 1969, saw their first major results published in 1976. More recently, new developments have appeared in the computer science literature. Here, the authors study controllability, positive controllability, and reachability in detail, and establish necessary and sufficient conditions for each of these three concepts. While reachability has been the primary focus of the computer science works, those studies emphasize decidability-a topic not addressed in this paper. Instead, the main contribution of this work is the derivation of necessary and sufficient conditions for reachability expressed in terms of the system matrix.
  • CHAI Yingying, ZHANG Yuexi, GUO Wanying, SHEN Tielong, WU Yuhu
    系统科学与复杂性(英文). 2026, 39(2): 624-648. https://doi.org/10.1007/s11424-026-5541-9
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    Finite-population games (FPGs) provide a unified paradigm for modeling strategic interactions among anonymous players, where interactions depend on other players only through their aggregate distribution. However, as the population size increases, computing Nash equilibria of FPGs becomes computationally intractable. As the limiting framework of FPGs when the population size tends to infinity, mean field games (MFGs) constitute a viable method for tackling this challenge. This review summarizes recent progress in the study of FPGs with a focus on MFG-based theoretical connections. First, the fundamental concepts of static and dynamic FPGs are introduced. Then, attention is devoted to the construction of their MFG counterparts. This review of recent research leads to the following conclusion: The mean field equilibria of the MFGs correspond to the $\epsilon$-symmetric Nash equilibria of the associated FPGs. Finally, to demonstrate the foundation of a physical application with MFG-based approaches, a decentralized charging/discharging mode decision problem for large-scale electric vehicles influenced by collective behaviour is taken as an illustration. It will be shown that the formulation is carried out in the fashion of a multi-valued logical system using the semi-tensor product framework.
  • YAO Yuhua, ZOU Zhuo, DJEHICHE Boualem, HU Xiaoming
    系统科学与复杂性(英文). 2026, 39(2): 649-676. https://doi.org/10.1007/s11424-026-5613-x
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    The interbank market is essential for liquidity allocation but also a major conduit for systemic risk. This paper develops a dynamic network game that models how financial institutions, acting as borrowers, lenders, or intermediaries, form and adjust bilateral exposures while optimizing liquidity decisions subject to balance-sheet and trust. The equilibrium structure of the lending network is characterized through a dynamic game-theoretic analysis based on best response dynamics and fixed-point conditions, showing how strategic complementarities and balance-sheet feedback generate stable configurations. Building on this framework, the authors analyze how exogenous shocks propagate through the network using a control-theoretic formulation. For asymptotically stable equilibria, the authors give an upper bound for the peak deviation following such a shock. In unstable scenarios, the authors establish sufficient conditions for structural stabilizability using policy interventions. Finally, a constructed case study simulates the dynamic generation of the endogenous equilibrium.
  • YU Yang, LI Xiuxian, LI Li, XIE Lihua
    系统科学与复杂性(英文). 2026, 39(2): 677-696. https://doi.org/10.1007/s11424-026-5394-2
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    This paper focuses on the problem of distributed online convex optimization with nonlinear switching costs in a multi-agent network. In this problem, each agent has its own time-varying private loss function, which is either convex or convex and smooth, and is restricted to partial access to information about the global time-varying loss functions. Consequently, agents need to engage in local information exchange with their neighbors to make decisions, and any changes in decisions will incur additional costs in a nonlinear manner. To address this problem, two algorithms are proposed: The distributed online gradient descent algorithm (DOGD) for general convex loss functions and the distributed online gradient tracking algorithm (DOGT) for convex and smooth loss functions. It is shown that both the proposed algorithms have sublinear dynamic regret bounds when the environment undergoes sublinear changes. In the end, a numerical simulation of a distributed online learning example is conducted to validate the theoretical results.
  • LEI Lei, CHEN Xi, CHEN Ben M.
    系统科学与复杂性(英文). 2026, 39(2): 697-712. https://doi.org/10.1007/s11424-026-5478-z
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    Accurate large-scale ocean environment modeling is challenged by multi-scale dynamics, sparse sampling, and measurement noise. This paper presents a fast spatiotemporal (ST) modeling and robust target diagnosis for large-scale ocean environments. First, the four-dimensional field is factorized via a Karhunen-Loève (KL) expansion into orthonormal spatial modes and their temporal features. The spatial modes are further regularized by smooth parametric functions, while the temporal features follow a compact nonlinear evolution, enabling efficient ST fusion for reconstruction. Building on the denoised field, the diagnosis module applies depth-wise Savitzky-Golay smoothing, prominence-based peak search on the vertical temperature gradient, and a half-maximum rule to estimate thermocline depth and thickness. Experiments on a Pacific Ocean dataset demonstrate favorable efficiency, stability, and interpretability. The proposed method achieves a root mean square error $0.1945^{\circ}$C in temperature reconstruction tests, while delivering reliable thermocline localization and thickness estimation suitable for online deployment.
  • QIU Ruiyang, XU Xiang, FENG Gang
    系统科学与复杂性(英文). 2026, 39(2): 713-728. https://doi.org/10.1007/s11424-026-5569-x
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    This paper addresses the sampled-data boundary stabilization problem of a cascaded system comprising an ordinary differential equation (ODE) and two coupled reaction-diffusion partial differential equations (PDEs), and in particular, tackles challenges arising from the spatial interconnections among PDE states and arbitrarily large but bounded distributed delays in the input channel. Initially, a continuous-time control law is developed using a backstepping-forwarding transformation, with the global exponential stability of the closed-loop system established. Subsequently, a sampled-data control strategy is obtained by applying a sample-and-hold mechanism to the continuous-time signal. The stability analysis for this digital implementation integrates spectral analysis of the discretized target system with input-to-state stability (ISS) estimates for the infinite-dimensional dynamics. It is demonstrated that global exponential stability is preserved, provided that the sampling period meets a specified spectral radius condition. Numerical simulations confirm the effectiveness of the proposed control strategies and validate the stability bound on the sampling period.
  • CHAKRABORTY Sayan, GAO Weinan, JIANG Zhong-Ping
    系统科学与复杂性(英文). 2026, 39(2): 729-758. https://doi.org/10.1007/s11424-026-5502-3
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    This paper presents a comprehensive review and synthesis of recent advances in learning-based resilient control methods for uncertain systems subject to denial-of-service (DoS) attacks. Across discrete-time and continuous-time settings, these frameworks integrate techniques from reinforcement learning (RL), adaptive dynamic programming (ADP), output regulation, switching-systems theory and small-gain analysis to achieve stability and robustness under cyberattacks and model uncertainties. The reviewed works demonstrate that active and data-driven control policies can be learned directly from input-state data, without requiring prior system knowledge, even in the presence of adversarial DoS attacks. Critical DoS attack duration and frequency bounds are characterized to ensure closed-loop stability. Moreover, these bounds are shown to be learnable using input-state data. Together, these advances highlight a unified perspective on resilient control-where learning, robustness, and security are jointly addressed to guarantee stable performance of cyber-physical systems under adverse network conditions.
  • WANG Guocheng, WANG Long
    系统科学与复杂性(英文). 2026, 39(2): 759-780. https://doi.org/10.1007/s11424-026-5488-x
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    Uncertainty is ubiquitous in natural and engineered systems, influencing both individual decisions and collective dynamics. While robust control theory has provided powerful tools for analyzing how uncertainty affects engineered systems, the study of robustness in game theory remains limited. This paper introduces three fundamental models to capture distinct sources of uncertainty: Environmental stochasticity affecting the payoff structure, demographic fluctuations arising from stochastic reproduction, and perceptual uncertainty shaped by noisy observation and subjective risk preference, extending the concept of robustness to strategic and evolutionary systems. Together, these models reveal how uncertainty at different levels-external, internal, and cognitive-can reshape evolutionary outcomes, alter stability, and generate complex dynamical patterns such as coexistence, multistability, and oscillations. Based on these theoretical foundations, the authors further study the evolution of cooperation in variable-sized populations with mutation. The analysis shows that when the population is divided into multiple subpopulations, migration among subgroups can effectively protect cooperators from the invasion of defectors and sustain cooperation even under mutation. This result demonstrates how robustness concepts can elucidate the emergence and persistence of cooperation in uncertain and heterogeneous environments.
  • MEI Shengwei, WEI Wei, LIU Feng, CHEN Laijun
    系统科学与复杂性(英文). 2026, 39(2): 781-812. https://doi.org/10.1007/s11424-026-5581-1
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    Optimization and decision-making problems in the planning and scheduling of power systems, and more generally of engineering systems, are subject to high levels of uncertainty, must accommodate multiple (often conflicting) objectives, and involve complex competitive and cooperative interactions among different decision makers. Such problems are representative in engineering design and are difficult to address using conventional optimization methods. Inspired by Qian Xuesen's development of engineering cybernetics from Wiener's feedback-based cybernetics, the authors have proposed engineering game theory, whose core idea is to reconcile conflicts via game-theoretic equilibrium, opening a new avenue for optimal decision making in complex large-scale systems. This paper presents the fundamental principles, mathematical models, and engineering applications of engineering game theory, aiming to provide a general paradigm and reference for decision-making problems encountered in engineering practice.
  • SHEIKHAHMADI Hêmin, XIE Yijing, LIN Zongli
    系统科学与复杂性(英文). 2026, 39(2): 813-830. https://doi.org/10.1007/s11424-026-5555-3
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    Resource allocation plays a crucial role in the reliable operation of large-scale multi-agent systems, particularly when the operating environments and the optimization goals are time-varying. This paper investigates a distributed real-time resource allocation problem over a directed communication network. The global objective is to minimize the sum of time-varying local costs subject to a time-varying resource allocation constraint. The time-varying nature of both the local cost functions and the resource allocation constraint is modeled by an exosystem. To address the combined difficulties of time-varying optimization and directed information flow, a distributed algorithm is designed for each agent that only utilizes the information of its own cost function and the information obtained through a network represented by a strongly connected and weight-balanced digraph. The distributed algorithm contains a distributed estimator that estimates global information involving all agents. It is proven that the proposed method achieves exponential convergence of all agents' decisions toward the time-varying optimal solution with any pre-specified level of accuracy. Simulation studies involving distributed energy resources in a virtual power plant further demonstrate the practical effectiveness of the algorithm.
  • HUANG Shijie, LEI Jinlong, HONG Yiguang
    系统科学与复杂性(英文). 2026, 39(2): 831-856. https://doi.org/10.1007/s11424-026-5503-2
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    No-regret learning has been widely used to compute a Nash equilibrium in two-person zero-sum games. However, there is still a lack of regret analysis for network stochastic zero-sum games, where players competing in two subnetworks only have access to some local information, and the cost functions are subject to stochastic uncertainty. Such a game model can be found in network interdiction problems, when a group of inspectors work together to detect a group of evaders. In this paper, the authors propose a distributed stochastic mirror descent (D-SMD) method, and establish the regret bounds $O(\sqrt{T})$ and $O(\log T)$ in the expected sense for convex-concave and strongly convex-strongly concave costs, respectively. The proposed bounds match those of the best known first-order online optimization algorithms. The authors then prove the convergence of the time-averaged iterates of D-SMD to the set of Nash equilibria. Finally, the authors show that the actual iterates of D-SMD almost surely converge to the Nash equilibrium in the strictly convex-strictly concave setting.
  • LI Yiliang, QI Hongsheng, FENG Jun-e
    系统科学与复杂性(英文). 2026, 39(2): 857-871. https://doi.org/10.1007/s11424-026-5496-x
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    The semi-tensor product (STP) of matrices is a powerful mathematical tool that has developed rapidly, owing to its successful applications not only in engineering but also in algebraic theory. This survey summarizes the engineering applications of STP, which mainly include those in logical systems, finite games, nonlinear feedback shift registers, compressed sensing, fuzzy systems, as well as practical implementations in combustion engines and hybrid electric vehicles. In this regard, several unsolved problems and research directions concerning the further applications of STP are proposed, which can help explore how engineering applications drive the development of STP.
  • XUE Shengli, ZHANG Lijun
    系统科学与复杂性(英文). 2026, 39(2): 872-894. https://doi.org/10.1007/s11424-026-5546-4
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    Conventional Transformer models for multivariate time series (MTS) rely on fixed-dimension inputs, necessitating explicit masking or pre-filling strategies when handling missing data. These strategies can distort observational distributions, underestimate predictive uncertainty, and introduce bias, particularly in scenarios characterized by dynamic dimensionality and unknown missing patterns. To address these challenges, the authors propose VarDim-Transformer, a novel architecture that natively supports variable input dimensions without requiring padding or channel identifiers, leveraging the Semi-Tensor Product (STP) of matrices. The core mechanism, the PiRegistry, dynamically projects arbitrary-length observation vectors into a unified latent feature space, enabling interaction via VarDim-Attention and VarDim-FFN before inverse projection. The authors evaluate the model under a rigorous "Random Dynamic Two-Level Missingness" protocol, which simulates long-term sensor failure and transient packet loss under privacy constraints. Experiments on the C-MAPSS FD001 remaining-useful-life prediction task demonstrate that VarDim-Transformer significantly outperforms imputation-based baselines. Notably, in a "Top-K" worst-case error analysis, VarDim-Transformer reduces the penalized error score by 21.28% compared to baselines and achieves a 77.1% win rate on the most critical samples. This confirms its superior robustness and generalization capability in extreme, privacy-sensitive missingness scenarios.