Load frequency control is critical for maintaining grid stability, particularly in modern power systems with wind power penetration and increasing exposure to denial-of-service attacks. This paper presents a data-driven dynamic event-triggered reinforcement learning framework for constrained H∞load frequency control in multi-area power systems. The control problem is formulated as a min–max optimization task, and a dynamic event-triggered strategy is designed to reduce computational and communication burdens. A neural network-based reinforcement learning framework is developed to approximate the near-optimal event-triggered control strategy without requiring explicit system dynamics. To further counteract the impact of frequency-based denial-of-service attacks, a dedicated attacks compensation mechanism is designed. Theoretical analysis proves input-to-state stability of the closed-loop system and guarantees convergence of the neural network parameters. Extensive simulation studies on multi-area power systems with wind power integration demonstrate that the proposed method ensures stable frequency regulation while effectively alleviating the transmission burdens and mitigating the adverse effects of cyberattacks.

This article deals with fully distributed data-driven bipartite formation control for nonlinear discrete-time multi-input-multi-output multiagent systems (MASs) with unknown dynamics models and quantized information. Initially, a distributed combined measurement error function (DCMEF) is developed for MASs characterized by cooperative and competitive interactions. This function is designed to transform bipartite formation challenges into traditional consensus problems. Subsequently, a distributed compact form dynamic linearization model is established based on the designed DCMEF and input-output data of the MASs, eliminating the need for a strongly connected communication topology. Following this, a logarithmic quantization scheme and a dynamic event-triggered communication mechanism are devised to reduce the communication burden and enhance convergence speed. Finally, a data-driven fully distributed dynamic event-triggered bipartite formation control method is proposed, and its convergence is rigorously proven. Simulation studies and hardware experiments are conducted to validate the effectiveness of the proposed method.

This article studies a data-driven event-triggered sliding-mode control approach for wind turbines with prescribed performance and quantified information to maximize power generation efficiency. Initially, a partial form of the dynamic linearization model is established for the controlled wind turbine system. A logarithmic quantizer is considered to quantize data before it is transmitted. Then, a data-driven event-triggered sliding-mode control scheme is established, where a smooth function is employed to limit the control error to a prescribed range, and an event-triggered scheme is designed to reduce the communication frequencies of the controlled plant. Finally, the convergence of the formulated approach is rigorously demonstrated, and the simulation results further verify the effectiveness of the developed method.

This paper addresses a data-driven sliding mode bipartite consensus issue for nonlinear discrete-time multi-agent systems with antagonistic interactions and limited communication resources. Initially, the signed graph theory is employed, and a combined measurement error function is formulated, transforming the bipartite consensus issue into a traditional consensus issue. An enhanced compact form dynamic linearization model is then established based on the input/output data and the formulated combined measurement error function. Moreover, a dynamic event-triggered function and a sliding-mode surface are designed, leading to the development of a fully distributed dynamic event-triggered sliding-mode bipartite consensus (DET-SMBC) approach. The proposed DET-SMBC approach is subsequently extended to a fully distributed dynamic event-triggered robust sliding-mode bipartite consensus (DET-RSMBC) scheme to improve robustness. The convergences of the tracking errors of both methods are rigorously deduced. Finally, simulation studies and hardware experiments are conducted to demonstrate the effectiveness of the proposed methods. Note to Practitioners—In multi-agent systems, the applicability of existing methods can be reduced by some issues, such as uncertain dynamics models, unknown disturbances, and the limitation of communication bandwidth. These issues can influence existing methods’ usefulness and cause instability, so DET-SMBC and DET-RSMBC methods are proposed in this paper. Compared with existing results, identifying a precise dynamics model for each controlled plant is unnecessary, the necessity of high-performance hardware for data transmission is relieved, and the effects of unknown disturbances are reduced. Moreover, the proposed methods are applied to realistic servo motor systems to conduct speed bipartite consensus tasks well. It is noted that most complicated mechanisms are controlled by servo motors, so the proposed methods can be applied to more practical engineering systems.

Load frequency control is essential for maintaining power system stability, especially under uncertainties and input delays. This paper proposes a reinforcement learning-based dual-channel dynamic event-triggered fixed-time load frequency control approach for uncertain multi-area power systems with input delays. A non-singular fast terminal sliding mode technique is employed to guarantee that the tracking error converges within a fixed time. To address system uncertainties and input delays, actor neural networks are designed to estimate the modeling uncertainties and provide compensation, and critic neural networks evaluate execution costs. To further enhance efficiency, a dual-channel event-triggered mechanism is designed, reducing communication overhead through independent dynamic event-triggering strategies for control input and output channels. The stability of the proposed method is rigorously analyzed using the Lyapunov method. Simulation results demonstrate faster convergence, reduced communication costs, and improved frequency stability compared to existing methods.

This paper investigates a data-driven dynamic event-triggered sliding mode heading control problem for un-manned surface vehicles with uncertain dynamics models. First, a virtual sensor is introduced to establish a compact dynamic linearization model for the unmanned surface vehicle. Then, a dynamic event-triggered scheme is developed to alleviate the communication burden. Moreover, a sliding mode surface is designed, and a data-driven dynamic event-triggered sliding mode heading control approach is formulated. Finally, rigorous mathematical proofs are given, and several simulations demon-strate the effectiveness and superiority of the proposed method compared to existing approaches.

Graph adversarial attacks can be classified as either white-box or black-box attacks. White-box attackers typically exhibit better performance because they can exploit the known structure of victim models. However, in practical settings, most attackers generate perturbations under black-box conditions, where the victim model is unknown. A fundamental question is how to leverage a white-box attacker to attack a black-box model. Some current black-box attack approaches employ white-box techniques to attack a surrogate model, resulting in satisfactory outcomes. Nonetheless, such white-box attackers must be meticulously designed and lack theoretical assurances for attack effectiveness. In this paper, we propose a novel framework that utilizes simple white-box techniques to conduct black-box attacks and provides the lower bound for attack performance. Specifically, we first employ a more comprehensive GCN technique named BiasGCN to approximate the victim model, and subsequently, use a simple white-box approach to attack the approximate model. We provide a generalization guarantee for our BiasGCN and employ it to obtain the lower bound on attack performance. Our method is evaluated on various datasets, and the experimental results indicate that our approach surpasses recently proposed baselines.

This paper addresses the challenge of resource-efficient control in vehicle platooning systems, particularly focusing on communication requirements that involve network encryption. First, we construct a virtual output function to establish a virtual dynamic linearization model for these systems. Then, we design an encoding-decoding mechanism based on a logarithmical quantizer to facilitate digitized encryption communication. Furthermore, we investigate a dead-zone-based event-triggered communication strategy, enabling users to balance costs and performances. Subsequently, we formulate an event-triggered model-free adaptive platooning control method relying solely on input and output data from the vehicle platooning systems. Finally, the convergence of the proposed method is rigorously proved, and simulation study results demonstrate the proposed method’s effectiveness.

This article investigates the online learning and energy-efficient control issues for nonlinear discrete-time multiagent systems (MASs) with unknown dynamics models and antagonistic interactions. First, a distributed combined measurement error function is formulated using the signed graph theory to transfer the bipartite formation issue into a consensus issue. Then, an enhanced linearization controller model for the controlled MASs is developed by employing dynamic linearization technology. After that, an online learning adaptive event-triggered (ET) actor-critic neural network (AC-NN) framework for the MASs to implement bipartite formation control tasks is proposed by employing the optimized NNs and designed adaptive ET mechanism. Moreover, the convergence of the designed formation control framework is strictly proved by the constructed Lyapunov functions. Finally, simulation and experimental studies further demonstrate the effectiveness of the proposed algorithm.

In this study, we investigate the event-triggering time-varying trajectory bipartite formation tracking problem for a class of unknown nonaffine nonlinear discrete-time multiagent systems (MASs). We first obtain an equivalent linear data model with a dynamic parameter of each agent by employing the pseudo-partial-derivative technique. Then, we propose an event-triggered distributed model-free adaptive iterative learning bipartite formation control scheme by using the input/output data of MASs without employing either the plant structure or any knowledge of the dynamics. To improve the flexibility and network communication resource utilization, we construct an observer-based event-triggering mechanism with a dead-zone operator. Furthermore, we rigorously prove the convergence of the proposed algorithm, where each agent's time-varying trajectory bipartite formation tracking error is reduced to a small range around zero. Finally, four simulation studies further validate the designed control approach's effectiveness, demonstrating that the proposed scheme is also suitable for the homogeneous MASs to achieve time-varying trajectory bipartite formation tracking.

This article investigates the information congestion problems for nonlinear discrete-time multi-input–multi-output multiagent systems (MASs) with fading channels when executing formation tasks. We first establish a virtual linear data model with a time-varying pseudo-Jacobian matrix variable for the MASs, which is independent of the dynamics model. Then, we formulate an event-triggered control scheme and a predictive compensation method to alleviate the communication burden and information congestion effects, respectively. Moreover, we propose two formation schemes for the MASs, considering limited communication resources, fading channels, and random delays to perform formation control and bipartite formation control tasks. The convergences of these two control protocols are strictly proved. Finally, simulations and hardware tests are conducted to verify the effectiveness of the proposed strategies.

Sun-induced chlorophyll fluorescence (SIF) is a promising remote sensing signal for early stress detection due to its close link with photosynthesis. Canopy SIF signals are controlled by leaf physiology, canopy structure, radiation intensity and sun-observer geometry. Variations in SIF observations are affected by variations in these controlling factors besides water stress. Mitigating the interference of non-drought factors on the variations in canopy SIF to accurately evaluate drought degree is still challenging. In this study, we explore the response of apparent SIF yield (SIFy) to progressive drought in maize. With experimental evidence, we show that the difference between noon and morning SIFy was a better indicator of drought than mono-temporal SIFy measurements. We proposed the noon-to-morning ratio (NMR) to characterize diurnal dynamics and assess the severity of drought. The results show that midday measurements of SIFy were the most affected by water stress, and morning measurements were the least. The NMR of SIFy successfully revealed water stress by tracking the timing of the transition from light-limited to water-limited conditions of SIF within a day. Hence, the NMRs of SIFy were considerably more sensitive to drought than their mono-temporal values, and traditional vegetation indices, especially during the early phase of drought. This demonstrates that the use of multi-temporal or diurnal SIF measurements is more reliable than mono-temporal observations for stress detection.

This article studies fully distributed data-driven problems for nonlinear discrete-time multiagent systems (MASs) with fixed and switching topologies preventing injection attacks. We first develop an enhanced compact form dynamic linearization model by applying the designed distributed bipartite combined measurement error function of the MASs. Then, a fully distributed event-triggered bipartite consensus (DETBC) framework is designed, where the dynamics information of MASs is no longer needed. Meanwhile, the restriction of the topology of the proposed DETBC method is further relieved. To prevent the MASs from injection attacks, neural network based detection and compensation schemes are developed. Rigorous convergence proof that the bipartite consensus error is ultimately bounded is presented. Finally, the effectiveness of the designed method is verified through simulations and experiments.

This paper deals with the trajectory tracking control problem of a planar underactuated tendon-driven truss-like manipulator (UTTM) by using fuzzy logic control methods, including type-1 fuzzy and interval type-2 fuzzy approaches. The UTTM is a novel designed underactuated robot that excels in manipulating in harsh environments due to its tendon-driven parallelogram structure. This unique structure, however, poses challenges for UTTM's trajectory tracking controller design, including underactuation, excessive nonlinearity, parameter uncertainty, and so on. To solve these problems, a fuzzy logic-based approach is proposed. First, the dynamic model of UTTM is transformed into a partially linearized form. Then a type-1 fuzzy controller is designed according to a linear quadratic regulator controller for the linearized system. Then by blurring the membership functions of type-1 fuzzy controllers, an interval type-2 fuzzy logic controller is designed, aiming at dealing with uncertainties. The proposed type-1 and interval type-2 fuzzy logic controllers are validated through simulations, and made comparisons with each other, especially when the system is involved with uncertainties. Simulation results show that the interval type-2 fuzzy-based trajectory tracking controller provides better performance than the type-1 counterpart in terms of tracking accuracy and capacity against uncertainties.