This paper presents an innovative framework that boosts computing power by utilizing ubiquitous computing power distribution and enabling higher computing node accessibility via adaptive UAV positioning, establishing a UAV-enabled Computing Power Network (UAV-CPN). In a UAV-CPN, a UAV functions as a dynamic relay, outsourcing computing tasks from the request zone to an expanded service zone with diverse computing nodes, including vehicle onboard units, edge servers, and dedicated powerful nodes. This approach has the potential to alleviate communication bottlenecks and overcome the "island effect" observed in multi-access edge computing. A significant challenge is to quantify computing power performance under complex dynamics of communication and computing. To address this challenge, we introduce task completion probability to capture the capability of UAV-CPNs for task computing. We further enhance UAV-CPN performance under a hybrid energy architecture by jointly optimizing UAV altitude and transmit power, where fuel cells and batteries collectively power both UAV propulsion and communication systems. Extensive evaluations show significant performance gains, highlighting the importance of balancing communication and computing capabilities, especially under dual-energy constraints. These findings underscore the potential of UAV-CPNs to significantly boost computing power.

This article presents a rigorous investigation of the global dissipativity (GD) and quasi-synchronization (QS) of fractional-order complex-valued neural networks (FOCVNNs) with mixed delays and external disturbances. The considered mixed delays consist of both time-varying delays (TVDs) and infinite distributed delays (IDDs), which significantly complicate the dynamical analysis of fractional-order systems. To address this challenge, the fractional comparison principle is first extended to accommodate IDD. Based on this extension, a generalized fractional Halanay inequality is established, providing an effective analytical tool for fractional-order systems with mixed delays. By employing the proposed inequality, two sufficient conditions are derived to guarantee GD and QS of FOCVNNs with mixed delays and external disturbances under designed feedback control. Finally, the effectiveness and applicability of the theoretical analysis are demonstrated through two illustrative examples, including a standard numerical simulation and a secure communication scheme constructed from a fractional-order chaotic system with mixed delays.

Privacy leakage and Byzantine issues are two adverse factors to optimization and learning processes of multiagent systems (MASs). Considering an unsafe MAS with these two issues, this article targets the resolution of a category of nonconvex optimization problems under the Polyak-Łojasiewicz (P-Ł) condition. To address this problem, we first identify and construct the unsafe MAS model. Under this kind of unfavorable MASs, we mask the local gradients with Gaussian noise and adopt a resilient aggregation method, self-centered clipping (SCC), to design a differentially private (DP) and Byzantine-resilient (BR) decentralized stochastic gradient algorithm, dubbed DP-SCC-PL, aiming to address a class of nonconvex optimization problems in the presence of both privacy leakage and Byzantine issues. The convergence analysis of DP-SCC-PL is challenging, as the convergence error arises from the coupled effects of DP and BR mechanisms, as well as the nonconvex relaxation, which is resolved via seeking the contraction relationships among the disagreement measure of reliable agents before and after the SCC aggregation, together with the optimal gap. Theoretical results not only reveal the trilemma between algorithm utility, resilience, and privacy, but also show that DP-SCC-PL can achieve consensus among all reliable agents. It has also been proven that if there are no privacy issues and Byzantine agents, then the asymptotic exact convergence can be recovered. Numerical experiments verify the utility, resilience, and privacy of DP-SCC-PL by tackling a nonconvex optimization problem satisfying the P-Ł condition under various Byzantine attacks.

Decentralized stochastic gradient algorithms efficiently solve large-scale finite-sum optimization problems when all agents in the network are reliable. However, most of these algorithms are not resilient to adverse conditions, such as malfunctioning agents, software bugs, and cyber attacks. This article aims to handle a class of general composite optimization problems over multiagent systems (MASs) in the presence of an unknown number of Byzantine agents. Building on a resilient aggregation mechanism and the proximal-gradient mapping method, a Byzantine-resilient decentralized stochastic proximal-gradient algorithmic framework is proposed, dubbedProx-DBRO-VR, which achieves an optimization and control goal using only local computations and communications. To asymptotically reduce the noise variance arising from local gradient estimation and accelerate the convergence, we incorporate two localized variance-reduced (VR) techniques (SAGAandLSVRG) intoProx-DBRO-VRto designProx-DBRO-SAGAandProx-DBRO-LSVRG. By analyzing the contraction relationships among the gradient-learning error, resilient consensus condition, and convergence error in a unified theoretical framework, it is proved that bothProx-DBRO-SAGAandProx-DBRO-LSVRG, with a well-designed constant (resp., decaying) step-size, converge linearly (resp., sublinearly) inside an error ball around the optimal solution to the original problem under standard assumptions. A tradeoff between convergence accuracy and Byzantine resilience in both linear and sublinear cases is also characterized. In numerical experiments, the effectiveness and practicability of the proposed algorithms are manifested via resolving a decentralized sparse machine learning problem under various Byzantine attacks.

Dynamic rotor imbalance is widely identified as the primary source of disturbance in gimbal servo systems and a major factor in the deterioration of their velocity tracking performance. The imbalance disturbance is often not directly measurable, submerged in noise, and with unknown frequency, which makes the estimation of such disturbances a particularly challenging topic. In order to mitigate the effects of the unknown imbalance, this paper investigates the disturbance identification problem, which includes simultaneous identification and estimation of the disturbance. Exploiting the features of the expectation-maximization (EM) framework, the disturbance identification problem is separated into the E-step (state estimation) and the M-step (model identification). A novel disturbance identification observer, where the E-step and the M-step are solved iteratively to simultaneously update the value and internal parameter of the disturbance online is proposed. In contrast to existing work using EM for identification of practical systems, the proposed scheme can be implemented online via stochastic approximation. In addition, a discrete-time anti-disturbance sliding mode controller based on the disturbance estimation is designed. Simulation and experimental results verify the effectiveness of the proposed method.

This article aims to realize a cooperative active power control of doubly fed induction generator (DFIG)-based wind farm (WF) to maximize the total reactive power capacity while maintaining the active power supply-and-demand balance. Difficulties lie in that the accurate PQ-curve expressions of wind turbines therein are unknown and nonuniform, thereby putting an obstacle to distributed optimization. To address the problem, PQ-curve inaccuracy caused by expression simplification is analyzed through the bridge of rotor current frame, rotor overspeeding control prioritized operation is recommended, and a surrogate-assisted distributed optimization (SADO) scheme is proposed from the WF perspective. The proposed method iteratively uses measured operating data to prompt a surrogate model to fit the accurate model, and then the optimal control action is guaranteed by online exploitation-and-exploration process with demonstrated availability through convergence analysis. Further, coordination with offline pretraining ensures that convergence can be obtained within shortened iteration steps. Case studies on 150-MW DFIG WF demonstrate the effectiveness of the proposed SADO scheme regarding shortening the iteration number, a full extraction on reactive power capacity and the better performance for voltage support.