Precise train stopping is a key technology of automatic train operation. On the basis of analyzing the train stopping phase, the delay characteristics of brake model and constraint conditions of train characteristics were considered, using generalized predictive control theory, a multi-objective predictive controller with constraints was designed taking account of train speed and distance as control targets and combining the control constraint conditions. The simulation results show that the proposed controller can accurately track the train stopping curve to achieve high precision stopping requirements and higher comfort.

Electronic Paper Display (EPD) can exhibit good comfortability in reading, but it has a critical drawback - slow refresh, which will be overcome by optimizing the design of the display's driver software. A tri-buffer-based architecture as well as its design methodology for the EPD driver software was proposed in this paper. Also an e-reader integrating the EPD driver in it was implemented to verify the design. Compared with the traditional dual-buffer architecture, the proposed tri-buffer scheme set an additional memory area to keep the EPD data frame. Test results show that the driver software works well in a real device without screen flicker and can help the display to achieve excellent performance.

In order to improve the speed of global optimization algorithm, a global optimization algorithm of intelligence control system was presented. The feedback idea of closed loop control system was applied in this algorithm that made the value of the object function gradually close to the input in the iterative process until reaching the global optimization. The key of the algorithm lies in the design of control strategy and the initial setting of parameters. In order to reduce the difficulty of initial setting of parameters and ensure the precision of the algorithm, the filled function was used to improve the global optimization algorithm of intelligence control system. Verified by twelve standard test functions, the improved algorithm is faster than filled function method, and is more accurate than the global optimization algorithm of intelligence control system.