ABSTRACT An event‐triggered model‐free adaptive sliding mode control (MFASMC) method is investigated in this article for networked control systems under intermittent denial‐of‐service (IDoS) attacks. To address unmodeled dynamics and bandwidth limitations, MFASMC is integrated with an event‐triggered mechanism and formulated within a data‐driven framework. By combining optimization theory with sliding mode reaching laws, an event‐triggered MFASMC scheme is developed, enabling adaptive responses to system states and external disturbances. Pseudo partial derivatives are employed to transform the nonlinear system into an equivalent data‐driven model. IDoS attacks are modeled as processes constrained by both frequency and duration, without specific assumptions regarding their structure or strategy. Through theoretical analysis, stability conditions for closed‐loop operation are derived. The effectiveness of the proposed method is validated by simulation results, which show significant improvements in system performance under complex network conditions.
This study develops and analyses a mathematical model for chickenpox transmission dynamics. The model's basic properties, including positivity, boundedness, and stability of disease-free and endemic equilibria, are rigorously established. The basic reproduction number R0$$ {\mathcal{R}}_0 $$ is derived, and sensitivity analysis identifies the most sensitive parameters. An optimal control problem is formulated with four time-dependent controls (preventive measures, vaccination, pharmaceutical interventions, and non-pharmaceutical treatments). Numerical simulations using a forward-backward sweep method (FBSM) are conducted. Several control scenarios that resulted from the intersection of the recommended controls to clarify the impact of these controls and explore the most effective way to halt the disease's progress are presented. Moreover, the cost-effectiveness analysis is presented to offer a quantitative comparison between the suggested five control scenarios. We conclude that while all proposed controls contribute to disease mitigation, the optimal strategy depends on resource availability: Scenario I (all controls) is recommended for maximum infection reduction, whereas Scenario II (excluding preventive measures) is preferred under budget constraints.
ABSTRACT This paper proposes a multimodal fusion‐based cooperative adaptive de‐icing control method. The method aims to achieve optimal de‐icing performance while maintaining wind turbine efficiency and operational safety. First, a control‐oriented reduced‐order thermodynamic model of blade electric‐heating de‐icing is established from the Messinger‐type energy‐balance principle. Then, a smooth switching function is introduced to enable multimodal fusion, providing a unified representation of sensible heating and latent melting modes. Next, to handle unknown system dynamics, a neural network (NN)‐based state identifier is designed to approximate the unknown nonlinear dynamics and provide online state reconstruction from available local measurements. Each independently heated zone on the blade surface is treated as an agent, forming a multi‐agent (MA) consensus cooperative control framework that incorporates both its own and neighboring control‐effort costs. This framework transforms the de‐icing problem into a MA cooperative optimal control task. Using adaptive dynamic programming (ADP), a critic NN approximates each agent's optimal value function online, from which a near‐optimal consensus cooperative control law is derived. The control law achieves coordinated de‐icing across all heating zones while minimizing the prescribed cooperative performance index. Appropriate Lyapunov functions are constructed to rigorously establish the stability of the NN identifier and the closed‐loop cooperative control system, guaranteeing the uniform ultimate boundedness of the estimation, learning, and consensus errors. Finally, simulation results validate the effectiveness of the proposed method.
ABSTRACT This paper investigates an adaptive fuzzy hybrid optimal event‐triggered output‐feedback control strategy for an unmanned surface vehicle (USV) subject to external disturbances. To estimate the unmeasurable velocities of the USV system and reduce the number of actuator updates, a fuzzy state estimator is designed and two event‐triggered mechanisms are established. Subsequently, a novel adaptive fuzzy hybrid optimal event‐triggered output‐feedback control scheme is proposed, which consists of a feed‐forward adaptive fuzzy controller and an optimal error feedback controller. The former is designed using a recursive backstepping design, while the latter is formulated within the adaptive dynamic programming (ADP) framework. It is shown that the designed hybrid optimal control scheme ensures that all variables in the USV system are uniformly ultimately bounded (UUB) while achieving optimal control performance. Simulation results are given to verify the effectiveness of the developed fuzzy hybrid optimal control scheme.
This work newly proposes a direct data-driven, linear matrix inequality (LMI)-based reference tracking proportional-integral (PI) controller for a marginally stable single-link flexible system. The inherent flexibility of the link, coupled with unmodeled system dynamics, poses significant challenges in achieving accurate trajectory tracking and minimizing tip deflection. The system is first stabilized by a direct data-driven state feedback controller (DDSFC). To enhance tracking performance, the DDSFC is complemented with the proposed PI reference tracking controller. This design is compared with a data-driven neural network-based proportional-integral-derivative (NNPID) controller. Experimental findings confirm that the proposed data-driven control framework effectively captures real-world dynamics. The resilience of the controller is further demonstrated under false data injection (FDI) and denial-of-service (DoS) attacks, validating its robustness and practical suitability for real-time control applications. To the best of our knowledge, this problem has not been addressed in the existing literature. The present work aims to bridge the gap between control theory and practical implementation.
