Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a single predefined matrix or a single self-generated matrix. It is difficult to obtain deeper spatial information by only relying on a single adjacency matrix. In this paper, we present a progressive multi-graph convolutional network (PMGCN), which includes spatiotemporal attention, multi-graph convolution, and multi-scale convolution modules. Specifically, we use a new spatiotemporal attention multi-graph convolution that can extract extensive and comprehensive dynamic spatial dependence between nodes, in which multiple graph convolutions adopt progressive connections and spatiotemporal attention dynamically adjusts each item of the Chebyshev polynomial in graph convolutions. In addition, multi-scale time convolution was added to obtain an extensive and comprehensive dynamic time dependence from multiple receptive field features. We used real datasets to predict traffic speed and traffic flow, and the results were compared with a variety of typical prediction models. PMGCN has the smallest Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) results under different horizons (H = 15 min, 30 min, 60 min), which shows the superiority of the proposed model.

A novel variable structure active-disturbance rejection position controller of permanent magnet synchronous motor (PMSM) servo system is presented in this paper. Not only maintaining the original features of controller, but also reducing the adjustable parameters, based on the variable structure designing. Through the analysis of cross-axis output equation, proposing a new position variable structure active-disturbance rejection controller (VS-ADRC) scheme, ensuring the system dynamic performance, while improving the ability of the anti-load disturbance. Simulation results show that the improved system with strong robustness to load and parameter changes within the system has fast response, no overshoot and the high control accuracy characteristics.