Research on Real-Time Monitoring and Performance Optimization of Suspension System in Maglev Train

With the success of the commercial operation of the maglev train, the demand for real-time monitoring and high-performance control of the maglev train suspension system is also increasing. Therefore, a framework for performance monitoring and performance optimization of the maglev train suspension system is proposed in this article. This framework consists of four parts: plant, feedback controller, residual generator, and dynamic compensator. Firstly, after the system model is established, the nominal controller is designed to ensure the stability of the system. Secondly, the observer-based residual generator is identified offline based on the input and output data without knowing the accurate model of the system, which avoids the interference of the unmodeled part. Thirdly, the control performance is monitored and evaluated in real time by analyzing the residual and executing the judgment logic. Fourthly, when the control performance of the system is degraded or not satisfactory, the dynamic compensator based on the residual is updated online iteratively to optimize the control performance. Finally, the proposed framework and theory are verified on the single suspension experimental platform and the results show the effectiveness.

A Data-Driven Scheme for Fault Detection of Discrete-Time Switched Systems

This paper is concerned with the fault detection issue for a class of discrete-time switched systems via the data-driven approach. For the fault detection of switched systems, it is inevitable to consider the mode matching problem between the activated subsystem and the executed residual generator since the mode mismatching may cause a false fault alarm in all probability. Frequently, studies assume that the switching laws are available to the residual generator, by which the residual generator keeps the same mode as the system plant and then the mode mismatching is excluded. However, this assumption is conservative and impractical because many switching laws are hard to acquire in practical applications. This work focuses on the case of switched systems with unavailable switching laws. In view of the unavailability of switching information, the mode recognition is considered for the fault detection process and meanwhile, sufficient conditions are presented for the mode distinguishability. Moreover, a novel decision logic for the fault detection is proposed, based on which new algorithms are established for the data-driven realization. Finally, a benchmark case on a three-tank system is used to illustrate the feasibility and usefulness of the obtained results.

Dynamic compensation control strategy of DC bus voltage based on residual generator

Aiming at the problem of bus voltage control in DC microgrid, a dynamic compensation control strategy based on a residual generator is designed to complete the voltage compensation of DC-DC converter. Firstly, based on the DC microgrid system architecture, the bus voltage fluctuations are analyzed theoretically, and then the DC-DC converter state-space mathematical models of the DC microgrid system are established to obtain the input-output relationship of the control system. Based on the theory of double coprime decomposition and Youla parameterization stable controller, the proposed control architecture based on the residual generator is obtained, and the output value generated by the current disturbance is compensated in reverse by applying model matching theory. The voltage loop compensation controller Q( s) is obtained by the linear matrix inequality method (LMI), and the current loop compensation controller H( s) is designed according to the dynamic structure diagram of the DC-DC converter. Hardware-in-the-loop simulation (HILS) results show that the architecture can improve the dynamic performance of the DC-DC converter without changing the original system structure parameters, and suppress the DC bus voltage fluctuations caused by load switching, power fluctuations, and AC-side load imbalances, and enhance the robustness of the system.

A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles

The implementation of each function of a battery management system (BMS) depends on sensor data. Efficient sensor fault diagnosis is essential to the durability and safety of battery systems. In this paper, a model-based sensor fault diagnosis scheme and fault-tolerant control strategy for a voltage sensor and a current sensor are proposed with recursive least-square (RLS) and unscented Kalman filter (UKF) algorithms. The fault diagnosis scheme uses an open-circuit voltage residual generator and a capacity residual generator to generate multiple residuals. In view of the different applicable state of charge (SOC) intervals of each residual, different residuals need to be selected according to the different SOC intervals to evaluate whether a sensor fault occurs during residual evaluation. The fault values of the voltage and current sensors are derived in detail based on the open-circuit voltage residual and the capacity residual, respectively, and applied to the fault-tolerant control of battery parameters and state estimations. The performance of the proposed approaches is demonstrated and evaluated by simulations with MATLAB and experimental studies with a commercial lithium-ion battery cell.