Ticao Jiao

Water management remains a critical challenge in proton exchange membrane fuel cells (PEMFCs) owing to the need to balance flooding and membrane dry‐out. However, the lack of direct measurements of liquid water saturation in the catalyst layer hinders effective water‐state monitoring and control. To address this issue, a nonlinear dynamic observer is proposed for the online estimation of liquid water saturation based on a lumped‐parameter state‐space model with a triangular structure. A radial basis function neural network (RBFNN) is incorporated to approximate nonlinear terms associated with unmeasurable current derivatives and compensate for modeling uncertainties. Observer gain matrices and adaptive RBFNN weight update laws are systematically derived to guarantee estimation performance and stability. Furthermore, a computationally tractable observer design framework is established through the integration of linear matrix inequalities (LMIs) and algebraic constraint parameterization. Comparative simulations show that the proposed observer improves the estimation accuracy of stack temperature and liquid water saturation by 67.7 and 46, respectively, compared with a conventional high‐gain observer. These results demonstrate the effectiveness and potential of the proposed approach for real‐time PEMFC water‐state monitoring.

This work explores the core challenge of adaptive event-triggered sliding mode control (SMC) for networked discrete singular systems (NDSSs) in the presence of false data injection (FDI) attacks. This innovative adaptive event-triggered design draws inspiration from the median filtering technique to use the average values of current sampled output signal and the latest transmitted trigger signal as the trigger condition. By focusing on the instantaneous characteristics, the accuracy of the trigger judgment of the proposed adaptive event-triggered mechanism (AETM) is improved. Taking into account the false data injection attacks, a collaborative control method integrating the AETM and the observer-based sliding mode control is specifically formulated to utilize the robustness of sliding mode control to enhance the security and reliability of the NDSSs. By applying the singular value decomposition technique, fresh criteria for the NDSS to be regular, causal, and bounded are obtained based on linear matrix inequalities (LMIs). Furthermore, the finite-time convergence of the system trajectories to a region in the vicinity of the equilibrium point is guaranteed by the proposed SMC law. Ultimately, a direct current (DC) motor model and a tunnel diode circuit system simulations verify the effectiveness and practicality of the proposed method.

This letter focuses on designing an optimal Markov transmission strategy for the remote estimation in renewable energy microgrids (REMs). To jointly account for estimation accuracy, transmission energy consumption, and future energy availability, a multiple discount weighting criterion (MDWC) is introduced as a unified performance index. The resulting sensor scheduling problem is formulated as a multiple-discount Markov decision process (MDP) under energy harvesting constraints, based on which the existence of an optimal Markov strategy is rigorously established. Furthermore, an explicit $(N,\infty)$ -stationary optimal Markov strategy with a switch-like structure is derived. Ultimately, a practical example of REM is employed to verify the efficacy of the obtained results.

For a visual servoing system with an eye-to-hand configuration, there is often a trade-off between the camera’s field of view (FOV) and visual positioning accuracy. This study presents an innovative visual servoing system that employs dual robotic arms performing distinct functions, coupled with a novel multi-distances observation-based visual servoing technique. To ensure that the target remains within the camera’s FOV, two distinct image path planning strategies are developed for each robotic arm. These strategies are notably independent of system parameters, which enhances their versatility. Numerical simulations and experimental results demonstrate that the proposed method effectively mitigates the conflict between the camera’s FOV and positioning accuracy, making it well-suited for high-precision assembly tasks, while also providing a new approach for enhancing the intelligence of visual servoing systems.

This article addresses the attitude control issue subject to unknown harmonic disturbances and inertial uncertainties for flexible spacecraft based on a dynamic event‐triggered mechanism and a T–S fuzzy modeling method. To estimate the flexible modes, a flexible vibration observer is constructed. An auxiliary filter is applied to transform unknown harmonic disturbances into an expression with unknown parameters. Then according to the estimations of unknown parameters, an adaptive disturbance observer is constructed. Additionally, the control scheme based on the dynamic event‐triggered mechanism is designed to guarantee the boundedness of all closed‐loop. Finally, two simulation cases are given for disturbances with single frequency and multiple frequencies to show the feasibility and effectiveness of the proposed control strategy.