Automatic train operation systems of high-speed trains are critical to guarantee operational safety, comfort, and parking accuracy. However, implementing optimal automatic operation control is challenging due to the train's uncertain dynamics and actuator saturation. To address this issue, this paper develops a data-driven Koopman model based predictive control method for automatic train operation systems. The proposed control scheme is designed within a data-driven framework. First, using operational data of trains and the Koopman operator, an explicit linear Koopman model is built to characterize the train dynamics. Then, a model predictive controller is designed based on the Koopman model under comfort and actuator constraints. Furthermore, an online update mechanism for the Koopman model is developed to cope with the changing dynamic characteristics of trains, which reduces the accumulation errors and improves control performance. Stability analysis of the closed-loop control system is provided. Comparative simulation results validate the effectiveness of the proposed control approach.

Remaining useful life (RUL) estimation is expected to provide appropriate maintenance for components or systems in industry to improve the reliability of the systems. Most data-based methods are limited to a single model, which is susceptible to various factors like environmental variability and diversity of operating conditions. In this paper, we propose an optimal stacking ensemble method combining different learning algorithms as meta-learners to mitigate the impact of multi-operating conditions. The selection of meta-learners follows a multi-objective evolutionary algorithm named non-dominated sorting genetic algorithms-II to balance the two conflicting objectives in terms of accuracy and diversity. Then the eventually evolved meta-learners are integrated by the meta-classifier for RUL estimation. In addition, a long-short-term feature extraction strategy is proposed to capture more degradation information from lifecycle data dynamically. Extensive experiments are performed on aero-engine dataset and battery dataset provided by NASA, which achieves the higher prognostic accuracy compared with the single models and existing methods.

Aircraft ground deicing plays an important role of guaranteeing the aircraft safety. In practice, most airports generally use as many deicing fluids as possible to remove the ice, which causes the waste of the deicing fluids and the pollution of the environment. Therefore, the model of aircraft ground deicing should be built to establish the foundation for the subsequent research, such as the optimization of the deicing fluid consumption. In this article, the heat balance of the deicing process is depicted, and the dynamic model of the deicing process is provided based on the analysis of the deicing mechanism. In the dynamic model, the surface temperature of the deicing fluids and the ice thickness are regarded as the state parameters, while the fluid flow rate, the initial temperature, and the injection time of the deicing fluids are treated as control parameters. Ignoring the heat exchange between the deicing fluids and the environment, the simplified model is obtained. The rationality of the simplified model is verified by the numerical simulation and the impacts of the flow rate, the initial temperature and the injection time on the deicing process are investigated. To verify the model, the semi-physical experiment system is established, consisting of the low-constant temperature test chamber, the ice simulation system, the deicing fluid heating and spraying system, the simulated wing, the test sensors, and the computer measure and control system. The actual test data verify the validity of the dynamic model and the accuracy of the simulation analysis.

This paper studied the problem of ice and snow pollutants identification and freezing temperature detection under winter runway operation, and designed an active detection system for runway ice and snow pollutants and freezing temperature. The temperature change trend during the liquid freezing process was researched, and the freezing temperature detection model based on sequence segmented linear fitting and inflection point identification was proposed in combination with active cooling technology. The differences in dielectric properties of runway snow and ice cover are studied, and a multi-frequency detection-based forked-finger planar capacitive sensor was identified for snow and ice pollutants detection considering practical application scenarios. A finite element simulation model of the forked-finger planar capacitive sensor was established, to optimize the structural parameters of the sensor and verify the feasibility of the capacitor sensor. Finally, the detection device and control system were designed and fabricated, and built an experimental platform for system test. The field experimental results showed that the system has good reliability and stability, and the error of freezing temperature detection model is less than 0.3 °C. At the same time, it can identify three types of pollutants, including water, ice and ice–water mixture, with an accuracy of 89%.

An urban railway train is a braking power-distributed system consisting of multiple carriages, which is becoming a powerful transportation tool to alleviate traffic congestion within cities as well as across cities. It is critical to control an urban railway train synchronously for improving braking performances, but challenging to be achieved due to strong coupling, unknown dynamics, and disturbances. This paper proposes an observer-driven distributed consensus braking control method for an urban railway train. Specifically, according to the data intersection among carriages, a distributed consensus braking controller is designed to make the velocity of each carriage converge to the desired braking curve. A sliding mode disturbance observer is then developed to estimate the non-linear coupling force and disturbances. The estimation value is utilized to compensate for the distributed consensus braking control law. Moreover, the potential fields are introduced to guarantee that the distances between any two neighbouring carriages are stabilized in a safe range. The effectiveness of the developed control strategy is firstly authenticated via the Lyapunov stability theory and then validated via numerical comparative simulations.