With the large-scale integration of renewable energy sources and widespread application of high-frequency power electronic devices, the issue of supraharmonics in power systems has become increasingly prominent, which imposes a significant challenge to power quality. To mitigate the impact of spectral leakage and the picket-fence effect on the accuracy of supraharmonic disturbance parameter measurements, and reduce the sampling pressure of supraharmonic signal, this article proposes a supraharmonic measurement method based on windowed compressed sensing (CS) and the orthogonal matching pursuit (OMP) algorithm. The four-term third-order Nuttall window function is integrated into CS technology, where a windowed sparse measurement matrix is constructed to enable windowed compressed sampling of supraharmonic signals. Subsequently, the OMP algorithm is used for frequency estimation of the supraharmonic signals, and the three-spectral-line interpolation technique is applied to reduce the picket-fence effect, improving the accuracy of supraharmonic disturbance frequency, amplitude, and phase measurements. Simulation experiments demonstrate that the proposed algorithm significantly addresses the spectral leakage and the picket-fence effect, effectively reduces the data required for supraharmonic detection, and improves the measurement accuracy while improving noise resistance. In practical experiments, the absolute errors in frequency and amplitude measurement for three sets of supraharmonic components are 0.55, 0.36, and 1.09 Hz and 0.0006, 0.0008, and 0.001 V, respectively, validating the effectiveness and practicality of the proposed method.

Effectively tuning the parameters of proportional–integral–derivative (PID) controllers has persistently posed a challenge in control engineering. This study proposes enhanced hippopotamus optimization (EHO) to address this challenge. Latin hypercube sampling and adaptive lens reverse learning are used to initialize the population to improve population diversity and enhance global search. Additionally, an adaptive perturbation mechanism is introduced into the position update in the exploration phase. To validate the performance of EHO, it is benchmarked against hippopotamus optimization and four classical or state-of-the-art intelligent algorithms using the CEC2022 test suite. The effectiveness of EHO is further evaluated by applying it in tuning PID controllers for different types of systems. The performance of EHO is compared with five other algorithms and the classical Ziegler–Nichols method. Analysis of convergence curves, step responses, box plots, and radar charts indicates that EHO outperforms the compared methods in accuracy, convergence speed, and stability. Finally, EHO is used to tune the cascade PID controller for trajectory tracking in a quadrotor unmanned aerial vehicle to assess its applicability. The simulation results indicate that the integrals of the time absolute error for the position channels ( x, y, z ), when the system is optimized using EHO over an 80 s runtime, are 59.979, 22.162, and 0.017, respectively. These values are notably lower than those obtained by the original hippopotamus optimization and manual parameter adjustment.

This paper investigates the adaptive fuzzy event-triggered optimized consensus tracking control problem for uncertain stochastic nonlinear multi-agent systems (MASs) with unknown dynamic and time-delay. Typically, optimal control is derived by the solution of the Hamilton-Jacobi–Bellman (HJB) equation, but it is usually challenging to solve this equation since inherent nonlinearity and unknown dynamics. Specifically, the complexity of the MASs in controller design is further exacerbated by the issue of state interdependence. To achieve optimized consensus control, the adaptive dynamic programming strategy is derived using the negative gradient of a simple positive function. As a result, the designed optimized consensus tracking control is relatively simple and can eliminate the persistence excitation (PE) assumption. The fuzzy logic systems (FLSs) are utilized to approximate the current and delayed states of unknown nonlinear functions; the identifier is proposed to estimate the stochastic multi-agent dynamic; the critic and actor FLSs are designed to evaluate control performance and execute control behavior, respectively. Furthermore, the event-triggered control (ETC) method is developed to save transmission load and communication resources. Moreover, we demonstrate that all signals for the MASs are semi-globally uniformly ultimately bounded (SGUUB) in mean square, and that the states of the follower agents can reach consensus with the leader’s state. Finally, a numerical example is illustrated to demonstrate the effectiveness of the proposed method. Note to Practitioners—With environmental protection and energy conservation becoming dominant trends, improving efficiency is regarded as a fundamental principle in control design. In addition, MASs are widely affected by factors such as stochastic disturbance, model unknown, time-delay, and uncertain factors, which further increase the difficulty of controller design. In this work, the optimized control approach is developed to design the control strategy for MASs to achieve the control task with the minimum utilization rate of control resources. In practical application scenarios such as spacecraft control or aircraft operation, the designed optimized control scheme can not only minimize fuel consumption to complete the predetermined tasks but also eliminate the PE assumption and strong assumptions on the time-delay nonlinear functions. Furthermore, the proposed ETC strategy effectively reduces the communication resource and network transmission in practice. Therefore, the control strategy designed in this work is more easily applicable to practical engineering.

The optimal consensus fault-tolerant control (FTC) problem for unknown time-delays nonlinear stochastic multi-agent systems (MASs) with hybrid actuator faults is investigated via adaptive dynamic programming (ADP) under the dynamic event-triggered control (ETC) mechanism in this paper. In control design, FLSs with delayed states are utilized to design an adaptive state identifier for estimating the unknown delayed dynamics in stochastic MASs. The fault estimator is constructed to detect actuator bias fault in real-time. By utilizing the ADP algorithm with identifier-actor-critic structure, a simplified fuzzy adaptive optimal consensus FTC strategy is developed, which does not require persistent excitation (PE) assumption. Furthermore, a dynamic ETC strategy is designed to further optimize the data transmission efficiency, effectively enhancing the system flexibility and resource utilization. Theoretical analysis indicates that the proposed control scheme can ensure that all signals of the nonlinear stochastic MASs are semi-globally uniformly ultimately bounded (SGUUB) in mean square, while achieving consensus between the follower’s states and the state of the leader. Finally, a numerical simulation validates the efficacy and feasibility of the proposed control strategy. Note to Practitioners—With the intensification of the global energy crisis, energy consumption optimization has become a crucial performance indicator of modern control design. In this paper, the optimal consensus FTC strategy is developed for unknown nonlinear stochastic MASs with time delays and actuator failures, which can achieve control requirements while minimizing control costs. The proposed control strategy can be applied to practical scenarios such as aerospace, formation control, and power systems. Compared with the existing control algorithms, the proposed control strategy avoids complex dynamic modeling and strong assumptions of nonlinear functions, and does not require PE assumption. In addition, a novel dynamic ETC strategy has been developed, which effectively improves resource utilization and data transmission efficiency compared with traditional continuous-time control and general ETC strategies. Consequently, the control strategy designed in this paper exhibits broader applicability and stronger practical value.

With the increasing scale of satellite networks, the cost of global traffic measurement is considerable. Estimating global network traffic data from partial traffic measurements becomes a promising solution. Most of existing methods have limitations in capturing high-dimensional spatio-temporal features and understanding highly dynamic network topology, which may lead to estimation errors. This paper proposes TopoTen, a topology-driven method for accurate traffic data estimation in Low Earth Orbit (LEO) mega-constellation networks. In TopoTen, SatRank module is created to encode input tensor in order to extract inherent topological features. While we introduce graph embedding block to process complex graph structural information. Additionally, we design a novel adaptive sparse spatio-temporal attention mechanism that enhances the model’s sensitivity to details and patterns by paying more attention to specific local regions of the input tensor. This mechanism improves TopoTen’s capacity to capture nonlinear spatio-temporal features. Experiments on small, medium, and large satellite network datasets show that TopoTen significantly outperforms baseline approaches, especially for large-scale Starlink dataset. It offers notable advantages in error reduction, especially for low sampling rates compared to mathematical baselines or for large-scale datasets compared to neural network based baselines.

The operations and maintenance of satellite networks heavily depend on traffic measurements. Due to the large-scale and highly dynamic nature of satellite networks, global measurement encounters significant challenges in terms of complexity and overhead. Estimating global network traffic data from partial traffic measurements is a promising solution. However, the majority of current estimation methods concentrate on low-rank linear decomposition, which is unable to accurately estimate. The reason lies in its inability to capture the intricate nonlinear spatio-temporal relationship found in large-scale, highly dynamic traffic data. This paper proposes Satformer, an accurate and robust method for estimating traffic data in satellite networks. In Satformer, we innovatively incorporate an adaptive sparse spatio-temporal attention mechanism. In the mechanism, more attention is paid to specific local regions of the input tensor to improve the model's sensitivity on details and patterns. This method enhances its capability to capture nonlinear spatio-temporal relationships. Experiments on small, medium, and large-scale satellite networks datasets demonstrate that Satformer outperforms mathematical and neural baseline methods notably. It provides substantial improvements in reducing errors and maintaining robustness, especially for larger networks. The approach shows promise for deployment in actual systems.

The accurate time-frequency (TF) positioning of power quality (PQ) disturbances is the basis of dealing with PQ problems in power systems. To accurately detect PQ disturbances, this article proposes a Kaiser window-based S-transform (KST) that provides better time resolution at fundamental frequency to detect the amplitude information for voltage swell, sag, interrupt, flicker, and better frequency resolution at higher frequencies to detect the frequency of time-varying harmonics and oscillatory transient. Based on short-time Fourier transform and S-transform, KST uses a Kaiser window with the characteristic of inherent optimal energy concentration as the kernel function. The Kaiser window can be adjusted adaptively according to the detection demand of PQ disturbances by the designed control function. This allows KST to easily accommodate different detection requirements at different frequencies. The utilization of Fourier transform ensures that KST can be realized quickly. The complex TF matrix is generated after a signal is transformed by KST, where the column vector is expressed as the distribution of amplitude and phase with time at a certain frequency, and the row vector represents the distribution of amplitude and phase with frequency at a certain sampling time. Experimental results demonstrate that the proposed KST significantly outperforms the state-of-the-art techniques in TF analysis of PQ signals, especially for the energy concentration and the detection of fundamental wave.