Communications in Nonlinear Science and Numerical Simulation最新文献

英文中文

Event-triggered bilayer game-based coordination control of multiple modular robot manipulators with input constraints 具有输入约束的多模块机器人的事件触发双层博弈协调控制

IF 3.8 2区数学 Q1 MATHEMATICS, APPLIED

Communications in Nonlinear Science and Numerical Simulation

Pub Date : 2026-01-10 DOI: 10.1016/j.cnsns.2026.109691

Yiming Cui , Tianjiao An , Bo Dong , Bing Ma , Zhenguo Zhang

This paper presents an event-triggered bilayer game-based coordination control framework for multiple Modular Robot Manipulators (MRMs) operating under stringent resource and actuator saturation constraints. To alleviate the computational and communication burdens characteristic of extreme environments, we introduce an event-triggering mechanism built on Neural Dynamic Programming (NDP) and differential-game theory, while casting intra-MRM subsystem interactions and inter-MRM collaborative transport as a bilayer nonzero-sum differential game. Coupled Hamilton-Jacobi equations are approximately solved via adaptive critic networks, yielding near-optimal control policies that explicitly enforce input constraints. A Lyapunov-based analysis proves that the subsystem trajectory tracking errors are Ultimately Uniformly Bounded (UUB), and the multi-MRM system under the collaborative handling task is asymptotically stable. Experiments conducted on both a 7-DOF MRM, which is used to validate high-dimensional joint coordination, and a 2-DOF MRM platform, which provides a simplified setting for cooperative manipulation tasks, demonstrate the proposed method’s efficacy and highlight its performance advantages over existing approaches.

提出了一种基于事件触发的多层博弈协调控制框架，用于在严格的资源和执行器饱和约束下运行的多模块机器人（MRMs）。为了减轻极端环境的计算和通信负担，我们引入了一种基于神经动态规划（NDP）和微分博弈论的事件触发机制，并将mrm内部子系统交互和mrm之间的协同传输转换为双层非零和微分博弈论。耦合Hamilton-Jacobi方程通过自适应批评网络近似求解，产生近似最优的控制策略，明确执行输入约束。基于lyapunov的分析证明了子系统轨迹跟踪误差是最终一致有界的，多mrm系统在协同处理任务下是渐近稳定的。在用于验证高维关节协调的7自由度MRM和为协作操作任务提供简化设置的2自由度MRM平台上进行的实验证明了该方法的有效性，并突出了其相对于现有方法的性能优势。

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引用次数: 0

Approximation-free full-state error constrained distributed formation control with unified preset-time performance 具有统一预置时间性能的无逼近全状态误差约束分布式编队控制

IF 3.8 2区数学 Q1 MATHEMATICS, APPLIED

Communications in Nonlinear Science and Numerical Simulation

Pub Date : 2026-01-10 DOI: 10.1016/j.cnsns.2026.109727

Yiwei Liu , Ning Pang , Ziming Wang , Xin Wang

This paper proposes a unified preset-time formation control framework for multi-input multi-output (MIMO) uncertain nonlinear systems. The framework is built upon a newly developed family of performance functions that ensure all error signals remain within prescribed boundaries and converge to arbitrarily designated sets within a preset time, independent of their initial conditions. In contrast to existing MIMO control schemes that rely on adaptive parameter estimation or disturbance observers, the proposed strategy achieves an approximation-free design, thereby simplifying the overall control architecture. To realize prescribed performance control (PPC) for agents with arbitrarily bounded initial states, a novel transformation mechanism is incorporated, which further avoids dependence on the homogeneity of agents’ dynamics. An event-triggered mechanism is also integrated to reduce communication burden between controllers and actuators, while rigorous analysis guarantees boundedness of all closed-loop signals and strictly excludes Zeno behavior. The effectiveness and advantages of the proposed approach are validated through numerical simulations of spacecraft formation control.

针对多输入多输出（MIMO）不确定非线性系统，提出了一种统一的预置时间编队控制框架。该框架建立在新开发的一系列性能函数的基础上，这些函数确保所有错误信号保持在规定的边界内，并在预设的时间内收敛到任意指定的集合，与它们的初始条件无关。与现有依赖自适应参数估计或干扰观测器的MIMO控制方案相比，本文提出的策略实现了无逼近设计，从而简化了整体控制体系结构。为了实现具有任意有界初始状态的智能体的规定性能控制（PPC），引入了一种新的转换机制，进一步避免了对智能体动态同质性的依赖。还集成了事件触发机制，减少了控制器和执行器之间的通信负担，同时严格的分析保证了所有闭环信号的有界性，严格排除了芝诺行为。通过航天器编队控制的数值仿真，验证了该方法的有效性和优越性。

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引用次数: 0

Hybrid consensus algorithms for Lipschitz nonlinear multi-agent systems in the presence of asynchronous sampling 存在异步采样的Lipschitz非线性多智能体系统的混合一致性算法

IF 3.8 2区数学 Q1 MATHEMATICS, APPLIED

Communications in Nonlinear Science and Numerical Simulation

Pub Date : 2026-01-10 DOI: 10.1016/j.cnsns.2026.109705

Xiaoyun Lu , Wu-Hua Chen

This paper is concerned with the asynchronous consensus problem of a class of Lipschitz nonlinear multi-agent systems (MASs) with intermittent information transmission. A distributed hybrid control protocol is proposed based on the relative measurements at discrete instants, under which each agent independently updates its protocol state in an impulsive fashion. In contrast to the conventional distributed impulsive control protocols, the agents’ states do not need to be changed instantaneously. The asynchronous resetting dynamics governing the protocol state is modelled by a time-varying difference equation, whose dynamic properties are leveraged to construct a time-dependent Lyapunov function for consensus analysis. By employing the protocol-dependent Lyapunov function and some analysis techniques, sufficient conditions are derived for the existence of the state feedback and static output feedback based hybrid control protocols, where the control gains can be obtained by solving a class of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed hybrid consensus algorithms is demonstrated by virtue of three examples.

研究了一类具有间歇信息传输的Lipschitz非线性多智能体系统的异步一致性问题。提出了一种基于离散时刻相对测量值的分布式混合控制协议，每个agent以脉冲方式独立更新协议状态。与传统的分布式脉冲控制协议相比，智能体的状态不需要瞬间改变。控制协议状态的异步重置动态由时变差分方程建模，利用其动态特性构造时变Lyapunov函数进行一致性分析。利用协议相关的Lyapunov函数和一些分析技术，推导了基于状态反馈和静态输出反馈的混合控制协议存在的充分条件，其中控制增益可以通过求解一类线性矩阵不等式（lmi）来获得。最后，通过两个算例验证了所提混合共识算法的有效性。

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引用次数: 0

Development of state-space model predictive control with L1 cost-function and deep state-space neural models 基于L1代价函数和深度状态空间神经模型的状态空间模型预测控制研究

IF 3.8 2区数学 Q1 MATHEMATICS, APPLIED

Communications in Nonlinear Science and Numerical Simulation

Pub Date : 2026-01-10 DOI: 10.1016/j.cnsns.2026.109742

Robert Nebeluk

The purpose of this paper is to provide a detailed implementation procedure for a Model Predictive Control (MPC) algorithm with a L₁ cost-function based on a deep state-space neural model with any number of hidden layers, number of nodes in each layer, and differentiable activation function. The formulation includes an Extended Kalman Filter (EKF) to handle the problem of unmeasurable state variables. The effectiveness of shallow and deep neural state-space models in modeling is also discussed. The presented control scheme provides less computational load thanks to the use of a neural approximator of the L₁ norm and an advanced on-line trajectory linearization technique. The algorithm is tested for a given nonlinear polymerization process. The advantages of deep neural state-space models in control are assessed in detail for three simulation scenarios. It is shown that the control performance obtained for the presented algorithm is similar to that of an MPC algorithm requiring nonlinear optimization.

本文的目的是提供一个具有L1代价函数的模型预测控制（MPC）算法的详细实现过程，该算法基于具有任意数量隐藏层、每层节点数和可微激活函数的深度状态空间神经模型。该公式包括一个扩展卡尔曼滤波（EKF）来处理不可测量状态变量的问题。讨论了浅层和深层神经状态空间模型在建模中的有效性。由于使用了L1范数的神经逼近器和先进的在线轨迹线性化技术，所提出的控制方案减少了计算量。对给定的非线性聚合过程进行了实验。针对三种仿真场景，详细评价了深度神经网络状态空间模型在控制中的优势。结果表明，该算法的控制性能与需要非线性优化的MPC算法相似。

引用次数: 0

Fixed-time adaptive output feedback control of stochastic nonlinear systems with actuator faults and unmodeled dynamics 具有执行器故障和未建模动力学的随机非线性系统的定时自适应输出反馈控制

IF 3.8 2区数学 Q1 MATHEMATICS, APPLIED

Communications in Nonlinear Science and Numerical Simulation

Pub Date : 2026-01-10 DOI: 10.1016/j.cnsns.2026.109690

Mohamed Kharrat

This paper investigates the problem of adaptive fixed-time output feedback control for a class of nonstrict-feedback stochastic nonlinear systems subject to unmodeled dynamics and actuator faults. To handle the issue of partial state availability, a state observer is designed to estimate the unmeasurable system states. Additionally, a dynamic compensation mechanism is developed to mitigate the effects of unmodeled dynamics. By combining the approximation capabilities of fuzzy logic systems with a backstepping-based control framework, a fixed-time adaptive controller is constructed. The proposed method guarantees that all closed-loop system signals remain bounded and that the tracking error converges to a small residual set within a predefined fixed-time, independent of initial conditions. The robustness and effectiveness of the control strategy are validated through a numerical example and a real-world example on a single-link robot system.

研究了一类具有未建模动力学和执行器故障的非严格反馈随机非线性系统的自适应定时输出反馈控制问题。为了处理部分状态可用性问题，设计了状态观测器来估计不可测量的系统状态。此外，还开发了一种动态补偿机制来减轻未建模动力学的影响。将模糊逻辑系统的逼近能力与基于后退的控制框架相结合，构造了一种定时自适应控制器。该方法保证了所有闭环系统信号保持有界，跟踪误差在预定义的固定时间内收敛到一个小残差集，与初始条件无关。通过数值算例和单连杆机器人系统实例验证了该控制策略的鲁棒性和有效性。

引用次数: 0

Fixed-time neural adaptive sliding mode control for second-order multi-agent systems with input quantization: A relative-velocity-free case 输入量化的二阶多智能体系统的固定时间神经自适应滑模控制：一种相对无速度的情况

IF 3.8 2区数学 Q1 MATHEMATICS, APPLIED

Communications in Nonlinear Science and Numerical Simulation

Pub Date : 2026-01-10 DOI: 10.1016/j.cnsns.2026.109679

Xinchen Guo , Hongxiang Zhang , Zhenying Liang

This paper focuses on the fixed-time consensus control problem for nonlinear second-order multi-agent systems affected by quantized inputs and external disturbances whose upper bounds are unknown via a neural-network-based distributed adaptive sliding mode control scheme. A novel distributed integral sliding surface (DISS) is put forward to ensure that agents arriving on the sliding surface can achieve fixed-time consensus without the relative velocity information, and the unknown nonlinear terms of systems and quantizers are approximated through neural networks (NNs). Based on such a DISS, an NN-based distributed adaptive sliding mode controller is developed, which does not depend on any global information, to guarantee the fixed-time reachability of the sliding manifold by designing suitable adaptive rules and selecting appropriate parameters. Finally, the effectiveness of the presented control scheme is further verified by two numerical simulation experiments.

本文采用基于神经网络的分布式自适应滑模控制方法，研究了受量化输入和上界未知外部干扰影响的非线性二阶多智能体系统的定时一致性控制问题。提出了一种新的分布积分滑动面（DISS），以保证到达滑动面上的智能体在没有相对速度信息的情况下能够达到定时一致性，并通过神经网络（NNs）逼近系统和量化器的未知非线性项。在此基础上，设计了一种不依赖全局信息的基于神经网络的分布式自适应滑模控制器，通过设计合适的自适应规则和选择合适的参数来保证滑模流形的定时可达性。最后，通过两个数值仿真实验进一步验证了所提控制方案的有效性。

引用次数: 0

Analytical valuation of nonlinear payoff volatility derivatives with discrete sampling under a mixed fractional geometric Brownian motion model 混合分数阶几何布朗运动模型下离散采样非线性收益波动导数的分析估值

IF 3.8 2区数学 Q1 MATHEMATICS, APPLIED

Communications in Nonlinear Science and Numerical Simulation

Pub Date : 2026-01-10 DOI: 10.1016/j.cnsns.2025.109626

Sanae Rujivan

This study introduces a comprehensive analytical framework for valuing nonlinear payoff volatility derivatives under a mixed fractional geometric Brownian motion model with Hurst parameter

H \in (\frac{3}{4}, 1)

. The model ensures no-arbitrage pricing within a complete-market environment while capturing both the stochastic features of standard Brownian motion and the long-memory characteristics of fractional Brownian motion. A central contribution is the derivation of the probability distribution of discretely sampled realized variance, addressing a longstanding challenge in the analytical pricing of volatility-linked derivatives. By expressing realized variance as a quadratic form of mixed fractional Brownian motion increments, we obtain a Laguerre series expansion for its probability density function and a generalized series representation for its conditional moments. These results enable closed-form evaluation of expectations involving nonlinear functions of the square root of realized variance, leading to tractable pricing formulas for a wide range of volatility-linked instruments, including swaps, options, capped or floored variants, and contracts with knock-out or corridor features. Monte Carlo simulations based on the exact Gaussian structure implied by the model demonstrate strong internal consistency and computational efficiency of the proposed pricing formulas within the simulated setting, and reveal notable sensitivity of fair strike prices to the Hurst parameter. Overall, this study advances the theoretical foundations of volatility derivative valuation by providing a tractable approach for incorporating long-memory dynamics under a no-arbitrage framework, while noting that empirical validation remains an avenue for future research.

本文引入了Hurst参数H∈(34,1)的混合分数阶几何布朗运动模型下非线性收益波动导数的综合分析框架。该模型保证了在完全市场环境下的无套利定价，同时捕获了标准布朗运动的随机特征和分数布朗运动的长记忆特征。核心贡献是离散抽样实现方差的概率分布的推导，解决了波动性相关衍生品分析定价的长期挑战。通过将实现方差表示为混合分数布朗运动增量的二次形式，得到了其概率密度函数的拉盖尔级数展开式及其条件矩的广义级数表示。这些结果能够对涉及已实现方差平方根非线性函数的期望进行封闭式评估，从而为广泛的波动性相关工具（包括掉期、期权、上限或下限变量以及具有淘汰或走廊特征的合约）提供易于处理的定价公式。基于模型隐含的精确高斯结构的蒙特卡罗模拟表明，在模拟设置下，所提出的定价公式具有较强的内部一致性和计算效率，并揭示了公平执行价格对Hurst参数的显著敏感性。总体而言，本研究通过提供一种在无套利框架下纳入长记忆动态的易于处理的方法，推进了波动性衍生品估值的理论基础，同时注意到实证验证仍然是未来研究的一个途径。

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引用次数: 0

Sampled-data synchronization of Markovian jumping inertial Cohen-Grossberg neural networks with parameter uncertainty and its application in secure communications 具有参数不确定性的马尔可夫跳跃惯性Cohen-Grossberg神经网络的采样数据同步及其在保密通信中的应用

IF 3.8 2区数学 Q1 MATHEMATICS, APPLIED

Communications in Nonlinear Science and Numerical Simulation

Pub Date : 2026-01-10 DOI: 10.1016/j.cnsns.2026.109726

Sasikala Subramaniam, Prakash Mani

This paper performs the synchronization analysis of Cohen-Grossberg-type neural networks (CGNNs) through the sampled-data control (SDC) design. This study introduces inertial coefficients (second-order derivatives) to reveal the underlying complex dynamical behaviors. Theoretically, employing the variable transformation approach helps to derive equivalent first-order derivatives-based model equations for inertial CGNNs (ICGNNs). Further, this study enhances the ICGNN model by considering the probabilistic-based switching behaviors in terms of Markovian-jumping model coefficients, the effect of time-dependent delays during network transmission among the neurons, and parameter uncertainties. The synchronization analysis of the enhanced ICGNN model will ensure its interpretability in particular applications such as secure communication of texts, audios, and images. This study tracks the synchronization process of open-loop (drive) and closed-loop (response) models through the convergence of their error dynamics. Theoretically, this study utilizes Lyapunov stability theory to ensure the global asymptotic stability of the error model by proposing the linear matrix inequalities (LMIs), sufficient to guarantee the convergence of the error to the origin. Numerical simulations are added to support the proposed frameworks, followed by an algorithm that demonstrates the utilization of drive-response models by the sender and receiver to encrypt and decrypt the information.

本文通过采样数据控制（SDC）设计对cohen - grossberg型神经网络（cgnn）进行同步分析。本研究引入惯性系数（二阶导数）来揭示潜在的复杂动力学行为。从理论上讲，采用变量变换方法有助于导出惯性cgnn （icgnn）的等效一阶导数模型方程。此外，本文还考虑了基于概率的切换行为，包括马尔可夫跳变模型系数、神经元间网络传输时的时延影响以及参数的不确定性，对ICGNN模型进行了改进。增强型ICGNN模型的同步分析将确保其在特定应用中的可解释性，例如文本、音频和图像的安全通信。本研究通过误差动态的收敛来跟踪开环（驱动）和闭环（响应）模型的同步过程。在理论上，本研究利用Lyapunov稳定性理论，通过提出线性矩阵不等式（lmi）来保证误差模型的全局渐近稳定性，足以保证误差收敛到原点。为了支持所提出的框架，本文添加了数值模拟，随后给出了一种算法，该算法演示了发送方和接收方利用驱动响应模型对信息进行加密和解密。

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引用次数: 0

Fixed-time synchronization of fuzzy stochastic delayed memristor-based neural networks subject to algebraic constraints 代数约束下模糊随机延迟忆阻器神经网络的定时同步

IF 3.8 2区数学 Q1 MATHEMATICS, APPLIED

Communications in Nonlinear Science and Numerical Simulation

Pub Date : 2026-01-09 DOI: 10.1016/j.cnsns.2026.109686

Xiang Wu , Hai Zhang , Xiaofeng Ye , Wenqi Zhang , Jinde Cao

This study investigates the fixed-time synchronization problem for stochastic delayed memristor-based neural networks with algebraic constraints within the T-S fuzzy logic framework. Algebraic constraints are first incorporated to accurately capture the inherent constrained properties of memristor-based neural networks. Then, the fixed-time stability lemma is extended to further shorten the synchronization convergence time. Leveraging this lemma, an appropriate Lyapunov function is constructed and a strictly aperiodic intermittent control strategy is developed, from which sufficient conditions and an explicit settling time for fixed-time synchronization are derived. The strictly aperiodic intermittent control mechanism effectively avoids unnecessary costs. Simulation results validate the theoretical analysis and demonstrate the enhanced performance of the proposed control scheme.

在T-S模糊逻辑框架下，研究了具有代数约束的随机延迟忆阻器神经网络的固定时间同步问题。首先引入代数约束来准确捕捉基于忆阻器的神经网络固有的约束特性。然后，对固定时间稳定性引理进行了推广，进一步缩短了同步收敛时间。利用这一引理，构造了合适的Lyapunov函数，提出了严格非周期间歇控制策略，并由此导出了固定时间同步的充分条件和显式稳定时间。严格的非周期间歇控制机制有效地避免了不必要的成本。仿真结果验证了理论分析的正确性，并证明了所提控制方案的性能有所提高。

引用次数: 0

Asymptotic stability of quaternion-valued neural networks under diversified random network attacks with adaptive event-triggered communication protocol and applications in PRNGs 具有自适应事件触发通信协议的四元数值神经网络在多种随机网络攻击下的渐近稳定性及其在prng中的应用

IF 3.8 2区数学 Q1 MATHEMATICS, APPLIED

Communications in Nonlinear Science and Numerical Simulation

Pub Date : 2026-01-09 DOI: 10.1016/j.cnsns.2026.109667

Jinlong Shu , Lianglin Xiong , Quanxin Zhu

This research focuses on the asymptotic stability of quaternion-valued neural networks (QVNNs) under diversified random network attacks and introduces a novel adaptive event-triggered (AET) communication protocol. First, this study explicitly accounts for the potential for network attacks. A dynamic threshold update mechanism is proposed, which incorporates a weighted factor to modulate the update strength of the threshold, and designs an AET control strategy based on non-periodic sampling. This approach enhances the system’s responsiveness to state changes and optimizes bandwidth utilization. Next, a new loop function is constructed, and less conservative asymptotic stability criteria are formulated depending on AET conditions. Finally, numerical simulations and application examples in pseudo random number generators (PRNGs) validate the reliability of the proposed approach, demonstrating its potential value in the fields of cryptography and secure communications.

研究了四元数值神经网络（qvnn）在多种随机网络攻击下的渐近稳定性，提出了一种新的自适应事件触发（AET）通信协议。首先，这项研究明确说明了网络攻击的可能性。提出了一种动态阈值更新机制，利用加权因子调节阈值更新强度，设计了一种基于非周期采样的AET控制策略。这种方法增强了系统对状态变化的响应能力，并优化了带宽利用率。其次，构造了一个新的环函数，并根据AET条件给出了较保守的渐近稳定性判据。最后，在伪随机数生成器（prng）中的数值模拟和应用实例验证了该方法的可靠性，展示了其在密码学和安全通信领域的潜在价值。

引用次数: 0

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