Precise braking force distribution is essential for the safety and efficiency of urban rail vehicles equipped with axle-controlled electro-pneumatic brake systems. However, conventional distribution strategies predominantly rely on static or empirical adhesion estimation, leading to suboptimal performance under time-varying track conditions and longitudinal load transfers. To address these issues, this paper proposes a dynamic distribution strategy based on real-time adhesion estimation. First, a longitudinal vehicle dynamics model incorporating dynamic axle load transfer and the LuGre friction formulation is established. An extended state observer is then designed to reconstruct the wheel-rail contact states and estimate the maximum available adhesion coefficient in real time. Subsequently, the braking force distribution is formulated as a constrained multi-objective optimization problem. An adaptive weight adjustment mechanism is also employed to dynamically coordinate total braking force tracking and wheel slide prevention. The proposed method is validated through hardware-in-the-loop experiments. Compared with conventional load-based and empirical adhesion estimation-based distribution methods, the proposed strategy significantly improves adhesion utilization and reduces braking distance under degraded adhesion scenarios.

Image descriptors are crucial in remote sensing image matching tasks. However, the presence of nonlinear transformation and dimensional collapse inherent in the perspective imaging process often poses challenges to achieving accurate matches. Existing descriptors lack a theoretical analysis of the perspective distortion process and fail to mine the patterns hidden in the perspective imaging process, consequently limiting their efficacy in remote sensing image matching. To uncover the underlying patterns in the image and devise a perspective-invariant descriptor, this paper proposes a perspective-invariant descriptor network (PIDNet). In our approach, we first analyze the remote sensing imaging process and demonstrate that it can be described in a new, conceptually simple linear space named the perspective distortion space. Second, we extract the bases from this space via the intersection-over-union (IoU) metric. As a result, each element in the space can be linearly expressed by the bases. Finally, we utilize these bases to design and learn a perspective-invariant descriptor. The core idea of our descriptor is based on the fact that each base corresponds to a unique imaging viewpoint. Therefore, any imaging viewpoint can be linearly represented as a combination of the bases. To implement our PIDNet, we propose a perspective sampling network module (PSNM) based on the spatial transform networks (STN) since no modules are available for our image sampling process. Furthermore, we introduce a perspective convolutional layer (PCLayer) to extract intermediate covariant features. Then, we concatenate the covariant features to learn a perspective-invariant descriptor. Experimental results on three datasets, including single-modal and multi-modal images, demonstrate the superior performance of PIDNet compared to state-of-the-art methods. Our source code will be publicly available at PIDNet.

Faults in braking actuators can compromise the safety and stability of urban rail train operations. Existing fault-tolerant control methods for trains struggle to guarantee both transient and steady-state braking performance quantitatively. In this paper, we propose a cooperative fault-tolerant braking control with prescribed performance for urban rail trains. A coupled multi-agent braking model is first developed, where each vehicle is treated as an independent and controllable agent subject to various uncertainties, input saturation and different levels of actuator faults. Further, incorporating a prescribed tracking performance function, a distributive adaptive terminal sliding mode controller is developed to ensure safe and reliable train braking control. The control input saturation nonlinearity is addressed by employing a smooth hyperbolic tangent function for approximation. Adaptation laws are introduced to mitigate the effects of parameter uncertainties and external disturbances. The efficacy of the proposed control scheme is validated through comprehensive numerical simulations.

Vector quantization encoding requires expensive time to find the closest codeword through the codebook for each input vector. A fast search algorithm for encoding is proposed in this paper. The multilevel elimination criterion is still derived from the three features (mean, variance, and norm) inequalities constraints, but the order of the three inequalities constraints, instead of the predefined order like other conventional multilevel elimination criterion, is optimized to speed up the encoding procedure. In the proposed algorithm, the elimination criterion at the first level is set to mean inequality constraint because of its narrower search width, and the priority order at the second and/or third level of variance and norm inequalities constraints is optimized based on the codewords distribution and the location of input vector in terms of the considered features. The experimental results demonstrate the effectiveness of the proposed algorithm.