Xin Zhang

This paper presents a differential evolution (DE)-optimized event-triggered sliding mode control framework for Markovian jump systems subject to nonlinear disturbances. A key innovation is the development of a DE-based optimization strategy for the dynamic event-triggering mechanism, where a novel hyperbolic-type objective function is formulated and optimized to achieve optimal bandwidth utilization. The control scheme integrates an observer-based sliding mode controller with an adaptive compensator designed to counteract unknown disturbances. Finite-time convergence to the sliding surface is guaranteed, while stochastic stability and H∞ performance are established through rigorous Lyapunov analysis. Practical simulations demonstrate that the DE-optimized approach significantly reduces communication frequency while maintaining control performance, outperforming conventional fixed-parameter and static event-triggering implementations. The results highlight DE’s effectiveness in solving complex event-triggered control optimization problems.

In this article, the state and fault estimation (SFE) issue is studied for nonlinear systems with parameter uncertainties subject to censored measurement under the dynamic event‐triggering protocol. The well‐known Tobit measurement model is introduced to characterize the censored measurement and the nonlinearity satisfies specific constraints. For the energy‐saving purposes, a dynamic event‐triggering protocol is employed to govern the measurement transmissions between the sensor and its corresponding estimator. The primary objective of the addressed problem is to design a dynamic event‐triggering estimator to estimate the system state and fault signal simultaneously such that, the upper bound (UB) of the estimation error covariance is guaranteed at each iteration, then such UB is minimized by properly choosing the estimator gain. Moreover, the boundedness analysis of the designed SFE approach is conducted firstly and then a sufficient condition is presented to validate the estimation error is stable in mean‐squared sense. Lastly, two experimental results are employed to validate the feasibility of the proposed estimation algorithm.

Cluster synchronization means that all multiagents are divided into different clusters according to the equations or roles of nodes in a complex network, and by designing an appropriate algorithm, each cluster can achieve synchronization to a certain value or an isolated node. However, the synchronization values between different clusters are different. With a feedback controller based on the calculation of the control input value and a trigger condition leading to the updating instants, this article introduces the trigger mechanism and designs a new data sampling strategy to achieve cluster synchronization of the coupled neural networks (CNNs), which reduces the number of updates of the controller, thereby reducing unnecessary waste of limited resources. In addition, an example proposes a synchronization algorithm and gives iterative procedures to calculate the trigger instants and prove the validity of the theoretical results.

In this article, the cluster synchronization problem for a class of the nonlinearly coupled delayed neural networks (NNs) in both finite- and fixed-time cases are investigated. Based on the Lyapunov stability theory and pinning control strategy, some criteria are provided to ensure the cluster synchronization of the nonlinearly coupled delayed NNs in both finite-and fixed-time aspects. Then, the settling time for stabilization that is dependent on the initial value and independent of the initial value is estimated, respectively. Finally, we illustrate the feasibility and practicality of the results via a numerical example.

研究了激光Maxwell-Bloch 方程时空混沌网络的同步问题.对单模激光Maxwell-Bloch方程进行了修正. 以N个修正后具有时空混沌特性的单模激光Maxwell-Bloch方程作为网络节点构成复杂网络. 在考虑到网络连接过程中,节点时空混沌系统中的参量可能受到某种干扰而与实际值产生微小偏差的情况下,采用网络第一个节点的时空混沌系统同时并行驱动其余N-1个时空混沌系统达到同步. 进一步通过仿真模拟验证了同步方案的有效性.