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This paper studies the resilient current controller design for the networked DC microgrid system with multiple constant power loads (CPLs) under a new type of time-constrained denial-of-service (DoS) attack. Different from the existing DoS attack models, which are often characterized by DoS frequency and DoS duration, this paper only considers the duration characteristics of the sporadic/aperiodic DoS attacks, and proposes a new type of time-constrained DoS attack model. Under the effects of such DoS attacks, a switching state feedback control law is constructed and a switching-like DC microgrid system model is then established. Furthermore, based on an attack-parameter-dependent time-varying Lyapunov function (TVLF) method, the exponential stability criterion of the resulting DC microgrid system under aperiodic DoS attacks is derived, and a new resilient controller design method is proposed. Finally, simulation studies are given to verify the effectiveness and merits of the proposed resilient control design scheme on achieving the desired control performance and attack resilience. • In contrast to [19], the proposed DoS attack model does only require to know the uniform lower and upper bounds on the sleeping and active periods of the attacks, respectively. • Different from [19-21], in this paper, an attack-parameter-dependent TVLF method is used to obtain the exponential stabilization conditions of the considered DC microgrid. • The proposed resilient controller design procedure is non-conservative, implying that we obtain the optimal controller without any estimation like [20] or approximation [21]. [ABSTRACT FROM AUTHOR]

This paper considers a class of multi-agent distributed convex optimization with a common set of constraints and provides several continuous-time neurodynamic approaches. In problem transformation, l 1 and l 2 penalty methods are used respectively to cast the linear consensus constraint into the objective function, which avoids introducing auxiliary variables and only involves information exchange among primal variables in the process of solving the problem. For nonsmooth cost functions, two differential inclusions with projection operator are proposed. Without convexity of the differential inclusions, the asymptotic behavior and convergence properties are explored. For smooth cost functions, by harnessing the smoothness of l 2 penalty function, finite- and fixed-time convergent algorithms are provided via a specifically designed average consensus estimator. Finally, several numerical examples in the multi-agent simulation environment are conducted to illustrate the effectiveness of the proposed neurodynamic approaches. [ABSTRACT FROM AUTHOR]

The variability of renewable energy sources (RESs) introduces more frequency fluctuations, and the reserve capacity of generation side needs to be sharply enlarged to maintain frequency stability, which inevitably increases the cost. In this paper, massive inverter air conditioners (IACs) as flexible regulation resources are aggregated to provide capacity support in frequency regulation. This paper establishes the state-space dynamic model and then presents a coordinated optimal control strategy by using model predictive control (MPC). However, the communication delay during the control signal transmission to IACs is one of the main obstacles that degrades the system performance in frequency regulation. To handle this issue, a predictive compensation method (PCM) based on MPC is applied to the control loop of IACs to compensate for the communication delay. Moreover, the robustness of the proposed MPC method with PCM against variations of system delay and parameters in the frequency response process is investigated in comparison to the proportional–integral (PI) controller. The simulation results are conducted to validate the superiority of the proposed method to the PI control method in virtue of the dynamic response and the performance indices, which demonstrates faster response, robustness, fewer fluctuations. • The state-space model of new power system incorporating the IACs is firstly proposed. • Based on this model, advanced control methods can be used for frequency control. • MPC is utilized to control aggregated IACs to obtain an optimal control strategy. • A predictive compensation method is applied to handle the impact of transmission delay. • The proposed method is robust over PI controller in the presence of delay. [ABSTRACT FROM AUTHOR]

Traffic guidance and traffic control are effective means to alleviate traffic problems. Formulating effective traffic guidance measures can improve the utilization rate of road resources and optimize the performance of the entire traffic network. Assuming that the traffic coordinator can capture traffic flow information in real-time utilizing sensors installed on each road, we consider the strong resilience constraints to construct an optimal selection problem of balanced flow in the traffic network. Based on multi-agent modeling, each agent has access to the corresponding traffic information of each link. We design a distributed optimization algorithm to tackle this optimization problem. In addition to the inherent advantages of distributed multi-agent algorithms, the communication topology among the sensors is allowed to be time-varying, which is more consistent with reality. To prove the effectiveness of the proposed algorithm, we also give a numerical simulation in the multi-agent environment. It should be reiterated that the optimization problem studied in this paper mainly focuses on traffic managers' perspectives. The goal of the studied optimization problem is to minimize the overall cost of the traffic network by adjusting the optimal equilibrium traffic flow. This study provides a reference for solving congestion optimization in today's cities. • Formulate an equilibrium flow selection problem with resilience constraints. • The distributed algorithm allows the communication among agents to be time-varying. • Communication graph among agents allows for being B-connected. [ABSTRACT FROM AUTHOR]

In the downlink communication, it is currently challenging for ground users to cope with the uncertain interference from aerial intelligent jammers. The cooperation and competition between ground users and unmanned aerial vehicle (UAV) jammers leads to a Markov game problem of anti-UAV jamming. Therefore, a model-free method is adopted based on multi-agent reinforcement learning (MARL) to handle the Markov game. However, the benchmark MARL strategies suffer from dimension explosion and local optimal convergence. To solve these issues, a novel event-triggered multi-agent proximal policy optimization algorithm with Beta strategy (ETMAPPO) is proposed in this paper, which aims to reduce the dimension of information transmission and improve the efficiency of policy convergence. In this event-triggering mechanism, agents can learn to obtain appropriate observation in different moment, thereby reducing the transmission of valueless information. Beta operator is used to optimize the action search. It expands the search scope of policy space. Ablation simulations show that the proposed strategy achieves better global benefits with fewer dimension of information than benchmark algorithms. In addition, the convergence performance verifies that the well-trained ETMAPPO has the capability to achieve stable jamming strategies and stable anti-jamming strategies. This approximately constitutes the Nash equilibrium of the anti-jamming Markov game. [ABSTRACT FROM AUTHOR]

• Device-edge-cloud collaboration architecture. • Keyframe extraction of video streams, remove non-keyframes. • Graphics compression and reconstruction algorithms. • Extract the region of interest of the face. The driver's emotional state directly affects safe driving. Under the "vehicle-human-road-cloud" integrated control framework, we propose an end-edge-cloud collaborative emotion perception network model (EEC-Net). The end side extracts the key frames of the driver's face video stream and performs batch compression; the edge side extracts the region of interest (ROI) of the reconstructed images as the input of the emotion recognition model (tiny_Xception) for classification; the cloud control terminal receives abnormal ROI image data and performs online training to dynamically adjust the operating parameters of the edge model. Finally, we test on open and self-built datasets, and the results show tiny_Xception has a significant improvement in accuracy of 2.45% compared to mini_Xception; the EEC-Net model can reliably perceive the negative emotion period, and the overall system memory consumption is reduced by about 5%, and the network transmission data volume and the computation time of emotion recognition are reduced by 95%, 60% respectively. [Display omitted] [ABSTRACT FROM AUTHOR]