# Research on Real-Time Monitoring and Performance Optimization of Suspension System in Maglev Train
## 1 Introduction  
As a new type of rail transportation, the maglev train has acquired attention at home and abroad due to its low noise, low energy consumption, and high comfort [1]. In addition, the commercial operation of high-speed maglev trains is also proceeding as scheduled [2], and maglev trains are gradually becoming a promising transportation tool. For actual automation engineering systems, control performance monitoring has always been a part of the operation of the system. By measuring and monitoring the performance of system elements and equipment, the operation of the system can be obtained, and maintenance plans can be made to reduce the life cycle cost of the system [3; 4; 5]. In order to perform performance monitoring, engineers usually pre-design a performance monitoring and evaluation unit, which evaluates the control performance based on the measurement values obtained by the sensors. Model-based control performance monitoring is widely used in industrial automation control systems and has always been favored [6; 7; 8]. However, the model-based control performance monitoring program requires prior knowledge of physics and mathematics, which reduces the robustness to the uncertainty of the model to a certain extent. With the development of data network technology, data acquisition systems are widely used in industrial automation systems, and more and more process data can be collected and stored. Data-driven process monitoring programs have been widely used in many complex industries in recent years, for example [9; 10; 11]. This scheme can directly extract the necessary process information from a large amount of process data, thus saving the complicated modeling process, simple in form and low design workload [12; 13; 14; 15; 16]. However, for large-scale systems, the number and types of sensors are very large, a large amount of process data will be generated during operation, which undoubtedly increasesthe difficulty of performance monitoring and evaluation. Therefore, it is necessary to establish a unified dimension based on measurement data, and the monitoring and evaluation of system performance can be realized based on this dimension. Meanwhile, similarly to other automation systems for Industry 4.0 [17], maglev trains are equipped with highly intelligent components and equipment, so they also face the problem of ensuring high control performance during the entire operation [18]. Taking the suspension system of the maglev train as an example, the nominal controller of suspension system in the maglev train is basically a PID controller. Although the PID controller ensures the stable suspension of the maglev train, and there is a certain stability margin to counter interference and other factors. In actual operation, maglev trains are often subject to various environmental conditions (such as track irregularities, load fluctuations caused by passengers getting on and off the train, excessive temperature of electromagnetics [2; 18; 20; 21; 22], etc. ), the control performance of the PID controller is not ideal. If no active and effective measures are taken, the control performance of the suspension system is very likely to be degraded, causing the train to deviate from the operating point or even fail. Although there are many algorithms for the online adjustment of PID parameters, in actual engineering, online adjustment of PID parameters is likely to cause system instability [23; 24; 25; 26]. In addition, the controllers of most systems are encapsulated inside the system, and it is difficult to change easily once it has been determined. Therefore, it is an urgent problem to be solved in the commercial operation of the maglev train to improve the controller of the suspension system and optimize the control performance of the system in the operation of the maglev train.