This paper proposes a novel filter-based model-free reinforcement learning (RL) event-triggered control (ETC) method for the fully distributed robust leaderless cooperative output regulation (COR) of unknown heterogeneous multi-agent systems (MASs) with external disturbances over directed graphs. First, the fully distributed event-triggered observers are designed to generate an autonomous system for the robust leaderless COR, in which the frequency of signal transmission and computational burden are significantly reduced, and the Zeno behavior is strictly ruled out. Then, a filter-based model-free RL algorithm without integration operation is developed to obtain the solution of the internal model-based augmented algebraic Riccati equation (AARE) and to release the requirement of recording complete and continuous data. Moreover, with some adaptive parameters, the robust leaderless COR is solved in a fully distributed manner without involving any global information of directed MASs. Finally, simulation results on RLC circuits are illustrated to show the feasibility and effectiveness of the proposed control scheme.

Accurate and reliable power generation forecasts of offshore wind farm clusters are crucial for the low-carbon operation of multienergy power systems. In practice, measurement data may not always be complete due to various failure issues in data acquisition systems or communication interruptions in harsh marine environments, and missing essential data may significantly reduce the credible prediction accuracy of probabilistic models. To address this problem, this article proposes a novel missing-data tolerant model based on confidence-triggered fuzzy clustering quantile-enhanced transformer (CFCQET). First, a quantile-enhanced transformer-based multistep wind power probabilistic forecasting method is developed, where the predicted values are iteratively updated by conditional confidence expectations. Then, based on the spatio-temporal characteristics of wind farms, a FCM clustering model for offshore wind farms is constructed to divide wind farms with similar power curve attributes for joint modeling. Next, a confidence-triggered strategy is designed for probabilistic power forecasting with missing data under wind farm clusters, where the output interpolated predicted values are used to fill in unobserved input data. Finally, probabilistic prediction tests for twelve offshore wind farms at a time resolution of half an hour. The test results demonstrate that the CFCQET achieves a lower negative form of the continuous ranking probability score (CRPS*), as well as superior sharpness and comparable reliability of the prediction intervals with respect to the benchmarks.

Reliable interval forecasting of uncertain load with cyberattacks is critical for grid resilience and decision-making. Nevertheless, various constraints on the predictive distribution assumptions are introduced into existing probability prediction models, limiting the model's capability to approximate the actual distribution. Moreover, load forecasting involving cyberattacks may lead to inappropriate decision-making for power transmissions. To this end, a novel cybersecure distribution-free learning model is devised for load forecasting under cyberattacks. First, an isolation forest is implemented to counter load anomalies with cyberattacks automatically by unsupervised learning. Second, an attention-assisted gated recurrent unit embedded in the developed model is employed to extract long- and short-term temporal dependencies of load time series. Then, an optimal prediction interval (PI) construction strategy based on conformal inference is put forward to eliminate the quantile crossing rates, which approximates the actual cumulative distribution function of load data and attains effective marginal coverage. Finally, numerical experiments have demonstrated that the model surpasses existing models in both PI width and cybersecurity.

To meet the growing demand of electrical dispatch, accurate prediction of a dynamic thermal rating (DTR) for transmission lines is crucial. However, the uncertainty of DTR caused by weather data imbalance constraints poses a risk to secure grid operation. To this end, a secure probabilistic interval prediction model is developed to tap potential DTR using bootstrap plus-guided time-series generative adversarial networks (TimeGAN) and spatiotemporal graph network (STGN), called BP-G2NN. The TimeGAN is used to augment the data to solve the weather data imbalance problem. And the STGN model is developed to dynamically strengthen the weight of the model to the key potential feature. In addition, the designed BP strategy restricts the frequency of maximum DTR exceeding the upper bound of prediction intervals and solves the inherent problem of quantile crossings. The simulation experiments using real data verify the validity of the model for DTR decisions.

This article addresses an improved co-design method of dynamical controller and asynchronous integral-type event-triggered mechanisms (ETMs) for a class of linear systems with external disturbances and measurement noises. First, a dynamical controller is designed for a linear disturbed plant, and two independent integral-type ETMs are synthesized to be embedded in the plant output and control input channels. Then, an augmented hybrid system is constructed, in which the integral-type ETMs in the two channels are not required to be activated synchronously and both channels are affected by their measurement noises. The proposed asynchronous integral-type event-triggered control (IT-ETC) scheme for two-fold signal transmissions can not only avoid the Zeno behavior strictly, but save more communication resources than the static event-triggered control (S-ETC) strategy. Moreover, a criterion is provided to guarantee that the hybrid system is${\mathcal {L}}_{2}$stable, and an improved co-design method is further synthesized to simultaneously obtain the design parameters of ETMs and feasible solutions of the dynamical controller. As a result, a tradeoff can be achieved between the robustness of the control system and the occupancy rate of communication resources. Compared with the S-ETC strategy, the simulation results have illustrated the effectiveness and superiority of the proposed asynchronous IT-ETC scheme.

This article addresses the design and implementation of observer-based sliding mode controller with H∞performance for underactuated rendezvous system under the constraints of fault signals, maximum input, and disturbances. The nonlinear constrained model considered in this article is an underactuated system since the number of control input variables is less than that of state variables, besides, owing to the full state information is unavailable or not so accurate for the controller design, an observer is first constructed to estimate the full state of system, and then the state of observer is adopted to synthesize the integral-type sliding surface function. Considering three fault signals, input constraint, and disturbances, the stability criteria with H∞performance are derived based on Lyapunov method and the ellipsoidal approximation algorithm. Furthermore, the sliding mode control law is formulated to guarantee the sliding mode dynamic could be driven onto the sliding surface and remain there for subsequent time. Finally, the simulation experiments are implemented to verify the effectiveness of the proposed scheme for the underactuated rendezvous system.

A novel asynchronous control for a class of Markovian jump systems (MJSs) via output feedback sliding mode approach with an artificial time-delay is proposed. The asynchronous control strategy is adopted owing to the nonsynchronization between the controlled system and the controller. In some practical applications, the state variables are often difficult to be measured directly from the outside of the system, which makes the implementation of state feedback technique more complex. However, the output information is always accessible to the system. Therefore, an asynchronous output feedback sliding controller, where an artificial time-delay is introduced in the synthesis of the sliding surface, for MJSs is designed to guarantee the sliding mode dynamics satisfying the reaching condition, and a sufficient condition is derived to ensure the resultant system exponentially stable. Besides, a program of optimization is given to optimize the artificial delay-time. Finally, a numerical simulation and a practical application are given to validate the effectiveness of the proposed technique.