Interleaved boost converters (IBCs) are commonly used as interface converters for DC microgrids (MGs) due to their high efficiency and low output ripple. However, the MGs system can easily become unstable due to the negative impedance characteristics of constant power load (CPL) and rapid power fluctuations. This paper proposes a fixed-time backstepping sliding-mode controller (FTBSMC) aimed at stabilizing the MGs system and achieving fixed-time tracking of the DC bus voltage. Firstly, the fixed-time disturbance observer (FxTDO) estimates the load disturbance at a fixed time, which improves the fast disturbance resistance of the system. Then, based on the dis-turbance estimation, the FTBSMC is designed, which combines the fast dynamic response of the sliding-mode control with the global stability of the backstepping control, avoiding the singularity problem of the conventional sliding-mode control. In addition, a first-order nonlinear filter is employed to avoid the direct differentiation of conventional backstepping control and at the same time to ensure global fixed-time stability. The fixed-time convergence of the proposed FTBSMC is rigorously demonstrated by using Lyapunov stability analysis. Finally, the FTBSMC proposed is verified by simulation and experiment in terms of faster dynamic response and stronger robustness.

To address the issues of high computational load and slow dynamic performance in traditional fractional-order sliding mode control for LCL-type grid-connected inverters, this paper proposes a fixed-time robust fractional-order sliding mode control strategy based on weighted average current control. Firstly, the weighted average current control (WACC) is used to reduce the third-order LCL filter to the first order, which simplifies the system model; secondly, in order to suppress the disturbance caused by the filter parameter perturbation to the weighted average current accuracy, a fixed-time disturbance observer (FTDO) is used to quickly estimate the disturbance caused by the filter parameter perturbation in a fixed time, so as to improve the anti-interference ability of the system; moreover, a fixed-time fractional-order sliding mode controller (FTFOSMC) is designed to achieve rapid tracking of the incoming reference current, and the stability of the proposed control strategy is confirmed by the strict Lyapunov method, which proves that the upper bound of the stability time is independent of the initial state of the system. Finally, simulation and experimental results show that the proposed method has better steady-state performance and a higher dynamic performance.

The operation of parallel inverters can enhance the reliability of power supply and meet the demand of the grid forming system; however, due to the difference in the Zero Sequence Voltage (ZSV) and the existence of the Zero Sequence Path (ZSP), the problem of Zero Sequence Circulating Current (ZSCC) inevitably occurs. This paper proposes a fixed-time control strategy based on the Extended Kalman Filter (EKF), used in order to suppress the ZSCC issue in a paralleled inverter system. Firstly, the detailed mathematical model of ZSCC is described, where the inductance perturbations are considered according to the generation mechanism of ZSCC, and a novel ZSCC controller is designed based on the principles of the fixed-time stability theory which can assure the action time of the zero vectors in one switching cycle. Secondly, to reduce the influence of the inductor parameters on the ZSCC control effect, the EKF is used to identify the online inductance parameters of the filter, and the robustness of the algorithm can be improved. Subsequently, based on the Lyapunov stability criterion, it has been proved that the proposed control strategy is fixed-time stable. Finally, the simulation and experiments are employed to demonstrate the effectiveness of the proposed control method.

Single-modal object Re-identification (Re-ID) frequently suffers from performance degradation under complex and dynamic scene conditions. Although multi-modal object Re-ID leverages complementary information across diverse modalities to alleviate this issue, existing approaches remain highly susceptible to irrelevant background interference. To address these challenges, we propose the Spatial–Frequency Token Interaction Network (SFTINet) for object Re-ID. Specifically, SFTINet utilizes a Vision Transformer (ViT) to extract multi-modal tokens, which are subsequently processed by a Spatial–Frequency Token Interaction Module. This module captures fine-grained local details in the spatial domain while concurrently encoding global structural information in the frequency domain, thereby augmenting the multi-modal fine-grained representation capability. A dynamic token interaction mechanism further regulates cross-modal information flow via attention, facilitating the deep fusion of RGB, Near-Infrared (NIR), and Thermal Infrared (TIR) features. Additionally, a Reconstruction Loss Module minimizes the distribution discrepancy across modalities through token-level constraints, and a Token Mask Generation Module encourages foreground-centric learning by strategically masking key tokens. By seamlessly integrating spatial–frequency interaction with token-level masking, SFTINet effectively suppresses background interference while preserving cross-modal complementary information. Experimental results demonstrate that SFTINet achieves competitive performance on multi-modal benchmarks, validating its effectiveness in robust cross-modal feature fusion and fine-grained semantic modeling.