Xin Li

The paper addresses the distributed secondary control (DSC) problem of the islanded microgrid composed of six distributed generators (DGs) with communication delay and external disturbances. Firstly, by utilizing the Artstein model reduction method, a distributed delay-compensated strategy is proposed to restore the voltage and frequency of each DG to reference values in a fixed time. Secondly, a novel sliding mode reaching law is designed to resist disturbances and accelerate convergence, thereby ensuring the sliding surface is reached within a fixed-time. Thirdly, the proposed approach integrates an event-triggered (ET) mechanism with an integral sliding mode surface, effectively conserving communication resources while avoiding Zeno behavior. In summary, the proposed delay-compensation strategy integrated with sliding mode control (SMC) guarantees the fixed-time synchronization of voltage and frequency for DGs with coordinated power sharing control. Finally, the simulation results validate the control strategy’s effectiveness, robustness to load changes, plug-and-play capability, and indicate that the proposed ET mechanism can greatly decrease communication frequency.

1. Wastewater treatment plays a crucial role in addressing the dual challenges of global freshwater scarcity and aquatic environmental pollution, serving as a crucial technological approach for saf...

This paper presents a distributed filtering framework based on the maximum correntropy criterion for sensor networks under non-Gaussian noise. To improve robustness against impulsive disturbances, the proposed method integrates a Cauchy kernel-based maximum correntropy criterion into the unscented Kalman filtering (UKF). A linear encoding-decoding mechanism is further introduced to address communication security in sensor networks, where artificial noise is added to the encoded signal prior to transmission and removed through a decoding process at the receiver. The resulting distortion is modeled as measurement noise and handled within the filtering scheme. Covariance intersection is employed to fuse local estimates from neighboring nodes, enabling consistent distributed filtering without requiring exact cross-covariance information. The artificial noise variance is analytically bounded to maintain signal concealment below a prescribed signal-to-noise ratio threshold, while rigorous proof of estimator convergence is provided. Simulation results verify that the proposed approach improves both estimation accuracy and transmission confidentiality compared to conventional distributed UKF methods.

A robust data-driven model predictive control (RDD-MPC) with a soft constrained strategy is proposed in this article. This method is designed to address model predictive control (MPC) constraint violations caused by external disturbances and outliers arising from measurement errors. First, a robust data-driven prediction model is constructed using kernel functions, and its parameters are updated via the soft-margin-based iterative quadratic programming (SMIQP) algorithm. This algorithm introduces a model slack variable (MSV) to tolerate the negative effects of outliers. This algorithm improves the robustness of the prediction model against outliers and ensures that the model can capture the complex dynamic characteristics of the system. Second, an enhanced soft constrained method (SCM) is designed to handle constraint violations. Unlike SCMs with a single slack variable, which may lead to excessive or insufficient constraint relaxation, this method employs two controller slack variables (CSVs) for coordinated constraint relaxation, thereby ensuring optimization feasibility. Combined with an adaptive weight penalty term, the proposed SCM maintains acceptable control performance in the presence of disturbances. Third, the input-to-state stability (ISS) of the proposed RDD-MPC method is rigorously proven. Finally, experimental results on classic nonlinear systems demonstrate that the proposed method can effectively improve both control performance and robustness. In addition, this method is evaluated on the Benchmark Simulation Model No. 1 (BSM1) for wastewater treatment processes (WWTPs). The experimental results further validate the effectiveness of the RDD-MPC method for systems engineering applications.

Multitasking optimization (MTO), addressing multiple optimization problems synchronously, has achieved significant success in the field of evolutionary computation. However, in practice, few tasks are accomplished synchronously due to asynchronous initialization. In this article, an asynchronous MTO (AMTO) paradigm is proposed, which aims to deal with multiple optimization problems with asynchronous arrivals. Due to the asynchronous characteristic of tasks, there is multiple tenses knowledge in an AMTO environment. Transferring multitense knowledge may accelerate the optimization process of the target task. Also, an AMTO algorithm is proposed to transfer multitense knowledge. The past-tense knowledge is transferred by an initialization strategy, which selects effective knowledge to deal with mismatched tenses. And the present-tense knowledge is transferred by knowledge reuse, which aligns convergence intervals to handle mismatched evolutionary states. Finally, several AMTO test problem sets and a practical problem are designed to verify the performance of the proposed algorithm. The experimental results show that the performance of the algorithm can be improved by multitense knowledge transfer.

Nonlinear networked systems (NSs) subject to actuator faults are addressed in this paper through proposed fault diagnosis (FD) and fault-tolerant control (FTC) schemes. The nonlinear NSs are represented by an interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model with mismatched membership functions (MFs), in which uncertainties are described as a fuzzy interval with upper and lower bounds. To concurrently estimate system states and actuator faults, an interval type-2 (IT2) T-S fuzzy fault diagnosis observer is developed, serving to improve the system’s resilience against uncertainties and external disturbances. Following the observer’s fault estimation, an FTC strategy is formulated to guarantee asymptotic stability and H_∞ performance in the closed-loop system. The proposed framework employs Lyapunov stability theory, with sufficient conditions for the FD observer and FTC gain matrices derived via linear matrix inequalities. Finally, through simulation experiments, the effectiveness and practicality of the designed FD and FTC strategies are validated.