Though Global Navigation Satellite System (GNSS) has been pretty mature and widely used in our daily life, the positioning of the indoor environment cannot be realized due to the shielding of concretes or glasses by GNSS. Many positioning methods have been proposed to solve that problem, and Bluetooth Low Energy (BLE) is one of the most widely used wireless technologies. This paper takes BLE Angle of Arrival (AOA) as the research object and proposes a method based on signal fitting and Propagator Direct Data Acquisition (PDDA) angle estimation algorithm. This method can be applied to both linear antenna array and rectangular antenna array and has good positioning performance. The method proposed in this paper does not need to calculate the phase differences between antennas, making it unnecessary to calculate the intersection of cones during spatial positioning. The angle estimation is divided into two steps. Firstly, the frequency deviation of Constant Tone Extension (CTE) is calculated based on the IQ samples captured during the reference period, and the received signal matrix of all antennas is fitted based on frequency deviation. Secondly, the antenna received signal model is established based on hardware antenna arrangement, and the arrival angle is estimated by the PDDA algorithm. In order to verify the effectiveness of the proposed method, the plane and spatial positioning experiments are carried out using the BLE devices of Nordic Company, which shows a good positioning accuracy. In spatial positioning, the average positioning accuracy of BLE in channel 37, channel 38, and channel 39 is 0.59m, 0.74m, and 0.77m, respectively, which can meet the basic indoor positioning requirements and cover a large area by one receiver. Compared with the classical MUSIC algorithm, the PDDA algorithm has the same positioning accuracy but lower computational complexity, so it has certain advantages.

In this paper, a permanent magnet (PM) electro-dynamic suspension and electro-magnetic suspension (EDS & EMS) hybrid suspension system is proposed to overcome the shortcomings of the under-damped dynamics in a PM EDS system. The linearized model is established and its stability is also analyzed using the Routh criterion. According to the rational distribution between the repulsive force and the electromagnetic force, a controller is designed to meet the dynamic performance required in practical applications. A novel maglev system using a hybrid suspension scheme is also presented in this paper. Furthermore, a single-degree-of-freedom (SDOF) hybrid suspension experimental mechanism is established, and some experiments have also been undertaken to validate its effectiveness on suppressing the oscillation of the PM EDS system caused by external disturbances.

Cyber-physical systems (CPSs) are increasingly threatened by stealthy false data injection (SFDI) attacks, which compromise system integrity by manipulating control signals and introducing false sensor data. These attacks are particularly challenging due to their diversity and often indistinguishable nature. In response to this issue, our work uncovers the fundamental causes behind SFDI attacks in linear time-invariant (LTI) systems and elucidates the principles enabling their stealth. We present a novel virtual extended system framework designed to eliminate strictly stealthy attacks within the entire CPS. Utilizing deep reinforcement learning (DRL) methodologies, we pioneer the use of detection results for real-time SFDI attack classification. Through numerical simulations, we validate our proposed method’s effectiveness, demonstrating a classification accuracy of no less than 95%. Notably, even in scenarios where attackers manage to breach the framework partially, our method continues to provide a reliable success rate in SFDI attack detection and classification, showcasing its robustness and efficacy. Note to Practitioners —Cyber-physical systems (CPSs), a critical component of modern industries, are becoming increasingly susceptible to stealthy false data injection (SFDI) attacks. These attacks compromise system integrity by subtly manipulating control signals and feeding false sensor data, making them challenging to detect. Our research presents an innovative framework that uses deep reinforcement learning techniques to detect and classify these elusive attacks, achieving a classification accuracy of over 95%. The information on SFDI attack categories, ascertained by this method, lays the groundwork for the development of subsequent defence strategies. For professionals working in sectors reliant on CPSs, such as manufacturing, healthcare, and transportation, this framework offers a promising tool to enhance system security. Even in scenarios where the system has been partially compromised, our method continues to provide reliable detection and classification, underscoring its robustness and practical utility. The system remains effective despite full breach attempts on specific attack types, ensuring resilience against a broad range of SFDI attacks. In conclusion, our research offers a substantial advancement in protecting CPSs against cyber threats.

The commercial operation of the maglev train puts critical demands on reliability and operation performance. Since maglev trains' levitation system is open-loop unstable, its closed-loop stability under various operation conditions is a primary consideration. This paper thus addresses real-time stability performance monitoring and evaluation of maglev trains' levitation system. First, a novel real-time performance indicator for stability performance monitoring is proposed. It utilizes only the online data of the levitation system, without knowing its accurate model. Then, performance evaluation is carried out by grading the monitored stability performance into different levels for further handling. Moreover, the proposed methods offer a way of controller evaluation for the levitation system before the train is put into commercial operation. It is noteworthy that, the proposed methods do not require the model of the levitation system to be known and run in real-time. The effectiveness and efficiency of the proposed methods are validated on a levitation system.

The dynamic interaction of vehicles/bridges is an important part of the research field of the Electromagnet Suspension (EMS) maglev train. However, multiple factors affect the dynamics of the coupling system, which increases the complexity of the vehicle-bridge coupling system, and makes it difficult to analyze. Herein, combining the advantages of theoretical derivation and numerical calculation, a fast and accurate analysis method of maglev vehicle and bridge system is proposed to analyze the influence of bridge and controller on the dynamics of the maglev system. This work is directed to the Changsha maglev express line, in which the mathematical equations for the vehicle- bridge system is presented. Then the influence of the parameter on the system is analyzed. Finally, the vibration experimental test system is set up, and the vibration test of the maglev vehicle bridge is tested by the test scheme, and the experimental results data are given. The test results of vibration acceleration are in good agreement with the calculation results of the proposed method, which verifies the accuracy of the model and the analysis method. Therefore, the research in this paper can provide theoretical guidance for Maglev engineering, and the analysis method in this paper can also be used in the study of other vehicles, such as the high-speed maglev train.

Track irregularities, caused by reasons such as track deformation and installation error, are common in real maglev line. These disturbances brought by tracks exert an adverse influence on the performance of a maglev train levitation system. It takes a lot of maintenance costs and time to keep the tracks in a good condition. In another way, a disturbance rejection through a controller optimization method is proposed in this paper. First, the influences of the track irregularity disturbances on levitation performance are illustrated in detail. Then, an online optimization of the PnP control architecture method is adopted to reject the disturbances caused by track irregularities. The effectiveness of this method is verified through the MATLAB simulation. This method makes the levitation system adaptive to known and unknown time-varying track irregularities, saving time, and human resources.