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This paper aims to stabilize an uncertain scalar switched linear system whose feedback information is transmitted over a digital communication network. The system has a scalar state. That network can only supply a finite data rate to represent the state and suffers from time-varying bounded network delay. Due to the unknown mode switch time and the unknown network delay, mode mismatch between the plant and the controller is unavoidable. By applying reachable set approximation and propagation approaches, this paper quantitatively investigates the effects of mode mismatch and model uncertainty on the system's stability and designs communication and control strategies to guarantee the desired stability. Moreover, it proposes some novel event-triggered sampling strategies, under which the required stabilizing bit rate can be lower than that required by traditional periodic sampling strategies. Simulations are done to verify the obtained results. • Lower stabilizing bit rate: Mode-dependent event-triggering strategies are proposed to save bit rate. • Network delay: Bounded network delay can be tolerated for stability. • Model uncertainty: Even in existence of model uncertainty, the stability can still be guaranteed. [ABSTRACT FROM AUTHOR]

This paper focuses on the quantized consensus problem of discrete-time second-order multi-agent systems which suffer from bounded process noise. In such systems, neighboring agents exchange information through a digital network that has only a finite bit rate and is subject to bounded network delay. Network delay may exacerbate the effects of quantization error and degrade the control performance by reducing the information to be extracted from receiving time instants of exchange packets. To handle network delay and save communication bit rates, an event-triggered distributed control protocol based on a dynamic encoding–decoding policy is proposed. Under the proposed event-triggered protocol, the asymptotic consensus of the concerned noise-free multi-agent systems can be achieved at a lower bit rate than that of periodic sampling control. This bit rate superiority results from the fact that our protocol makes use of the extra information being extracted from the event-triggering time instants. Moreover, it is shown that no higher bit rate is required to guarantee the desired consensus in the existence of bounded process noise. Simulations are done to demonstrate the effectiveness of our consensus protocol. As shown by our numerical examples, the occupied bit rate to ensure the desired consensus can be saved by up to 50% compared with periodic sampling strategies. • An event-triggered sampling strategy and a dynamic encoding–decoding policy for MASs. • The proposed event-triggered sampling reduces the bit rate required for consensus. • Effects of bounded network delay and process noise on consensus are considered. [ABSTRACT FROM AUTHOR]

Spatio-temporal Forecasting has been implemented in diverse fields, such as energy, traffic, and weather. As one typical paradigm of intelligent power systems, spatio-temporal wind speed forecasting has attracted increasing attention. Recently, some graph-based methods have revealed the great potential in the spatio-temporal wind speed forecasting task. Those methods are usually built on a static graph which is incapable of discovering the dynamic characteristics and the uncertainty of temporal wind patterns. To this end, we propose a novel D ynamic S tochastic G raph N etwork (DSGN) for spatio-temporal wind speed forecasting. Specifically, we characterize each wind turbine of regional power grids with a dynamic stochastic distribution to incorporate the uncertainty into networks. Then Wasserstein distance is adopted to build the stochastic graph, which can effectively represent the spatial dependencies between wind turbines. Additionally, we devise a reconstruction task to preserve the geographical distribution information and regularize the learning of the stochastic graph. To further exploit global spatial dependencies and enhance graph modeling, we augment the stochastic graph view by diffusion operations and integrate a self-supervised learning framework, serving as an auxiliary task, to maximize mutual information between the wind latent states from different graph views. Empirical experiments on four real-world wind speed datasets demonstrate the superiority of DSGN over some state-of-the-art methods. Some ablation studies further validate the effectiveness of the stochastic graph modeling and the self-supervised framework. • A novel dynamic stochastic graph network for spatio-temporal wind speed forecasting. • Characterizing the dependencies between wind turbines in the Wasserstein space. • Exploiting the potential of self-supervised learning for the forecasting task. [ABSTRACT FROM AUTHOR]

Wind energy has attracted more and more attention due to its sustainability and pollution-free nature. As wind energy is highly dependent on wind speed, wind speed forecasting is of great importance for fully harvesting wind energy and ensuring the stability and reliability of wind energy systems. By accurately predicting the future wind speed, we can make efficient power schedules and consequently improve the utilization of wind power. Although existing forecasting methods have achieved satisfactory wind forecasting performance, they may still fail to discover the intricate spatio-temporal dependencies obscured in entangled patterns. To resolve this issue and further improve the prediction accuracy, we propose Mixformer , a novel mixture Transformer with hierarchical context for spatio-temporal wind speed forecasting. Mixformer first applies seasonal-trend decomposition, and then employs an MLP to predict the trend part and an attention model to predict the seasonal part. Besides, Mixformer innovatively proposes a spatio-temporal Gaussian mixture attention (ST-GMA) layer, which fuses the periodic temporal and long-term spatial context. To capture intricate long-term spatial characteristics, Mixformer extracts global information by the Dynamic Time Warping algorithm. Moreover, with the ST-GMA layer, Mixformer builds a hierarchical encoder–decoder architecture to fully utilize the context at different scales for seasonal forecasting and further enhance the spatio-temporal modeling. Empirical experiments on four real-world benchmark datasets show that Mixformer yields the lowest prediction errors with the MAE scores of 1.66, 1.68, 3.14 and 0.70 and the RMSE scores of 2.07, 2.01, 3.99 and 0.98, and improves the prediction accuracy by 8.43%, 5.36%, 4.14%, 6.57% in term of MAE scores and 9.18%, 6.92%, 4.01%, 5.75% in term of RMSE scores compared to state-of-the-art methods, which demonstrates the superiority of Mixformer. In a nutshell, Mixformer presents a promising method for spatio-temporal wind speed forecasting and may achieve desirable performance in real-world applications of wind power systems. [Display omitted] • Mixformer is proposed for spatio-temporal wind speed forecasting. • Mixformer leverages the series decomposition to extract disentangled patterns. • Mixformer devises a spatio-temporal Gaussian mixture attention module. • Experiments on 4 real-world datasets validate the superiority of Mixformer. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of stabilizing an n -dimensional nonlinear system whose feedback packets are transmitted via the digital network. In the presence of the bounded processing delay and network delay, the sampling time instant of a feedback packet is not the same as its receiving time instant. The larger difference between these time instants, the higher bit rate is required to stabilize the concerned system. Moreover, not only these delays, but also feedback dropouts and process noise will deteriorate the system performance. Thus we propose a model-based event-triggered control method to stabilize the system and save the occupied network bandwidth. Besides the transmission bits inside the feedback packets, their sampling time instants also convey the state information. The proposed event-triggered method can make full use of the feedback packets, especially their receiving time instant information. Compared with the periodic sampling method, our method performs better and requires lower stabilizing bit rates while the desired input-to-state stability can still be ensured. The obtained bit rate conditions only depend on the Lipschitz constant, the upper bounds of the delays and the dropout rate, and are independent of the bounded process noise. • An n-dimensional nonlinear system with feedback delay and dropout are stabilized. • Based on event-triggering, stabilizing bit rate saving is achieved. • Simulations confirm the effectiveness of the achieved stabilizing bit rate conditions. [ABSTRACT FROM AUTHOR]