Fault Detection Based on Tracking Differentiator Applied on the Suspension System of Maglev Train

A fault detection method based on the optimized tracking differentiator is introduced. It is applied on the acceleration sensor of the suspension system of maglev train. It detects the fault of the acceleration sensor by comparing the acceleration integral signal with the speed signal obtained by the optimized tracking differentiator. This paper optimizes the control variable when the states locate within or beyond the two-step reachable region to improve the performance of the approximate linear discrete tracking differentiator. Fault-tolerant control has been conducted by feedback based on the speed signal acquired from the optimized tracking differentiator when the acceleration sensor fails. The simulation and experiment results show the practical usefulness of the presented method

Research on the Filtering Algorithm in Speed and Position Detection of Maglev Trains

This paper introduces in brief the traction system of a permanent magnet electrodynamic suspension (EDS) train. The synchronous traction mode based on long stators and track cable is described. A speed and position detection system is recommended. It is installed on board and is used as the feedback end. Restricted by the maglev train’s structure, the permanent magnet electrodynamic suspension (EDS) train uses the non-contact method to detect its position. Because of the shake and the track joints, the position signal sent by the position sensor is always aberrant and noisy. To solve this problem, a linear discrete track-differentiator filtering algorithm is proposed. The filtering characters of the track-differentiator (TD) and track-differentiator group are analyzed. The four series of TD are used in the signal processing unit. The result shows that the track-differentiator could have a good effect and make the traction system run normally

Closed-form solution of discrete-time optimal control and its convergence

The convergence of a new closed-form solution for the discrete-time optimal control is presented. First, a new time optimal control law with simple structure is constructed in the form of the state feedback for a discrete-time double-integral system by using the state backstepping approach. The control signal sequence in this approach is determined by the linearised criterion according to the position of the initial state point on the phase plane. This closed-form non-linear state feedback control law clearly shows that time optimal control in discrete time is not necessarily the bang-bang control. Second, the convergence of the time optimal control law is proved by demonstrating the convergence path of the state point sequence driven by the corresponding control signal sequence. Finally, numerical simulation results demonstrate the effectiveness of this new discrete-time optimal control law.NRF (Natl Research Foundation, S’pore)MOE (Min. of Education, S’pore)Published versio

Maglev Train Signal Processing Architecture Based on Nonlinear Discrete Tracking Differentiator

In a maglev train levitation system, signal processing plays an important role for the reason that some sensor signals are prone to be corrupted by noise due to the harsh installation and operation environment of sensors and some signals cannot be acquired directly via sensors. Based on these concerns, an architecture based on a new type of nonlinear second-order discrete tracking differentiator is proposed. The function of this signal processing architecture includes filtering signal noise and acquiring needed signals for levitation purposes. The proposed tracking differentiator possesses the advantages of quick convergence, no fluttering, and simple calculation. Tracking differentiator’s frequency characteristics at different parameter values are studied in this paper. The performance of this new type of tracking differentiator is tested in a MATLAB simulation and this tracking-differentiator is implemented in Very-High-Speed Integrated Circuit Hardware Description Language (VHDL). In the end, experiments are conducted separately on a test board and a maglev train model. Simulation and experiment results show that the performance of this novel signal processing architecture can fulfill the real system requirement

A simple discrete-time tracking differentiator and its application to speed and position detection system for a maglev train

In this brief, a novel tracking differentiator (TD) based on discrete time optimal control (DTOC) is presented. In particular, using the state back-stepping method, a DTOC law for a discrete-time, double-integral system is determined by linearized criterion, which equips the TD with a simple structure. The analysis of the proposed TD reveals its filtering mechanism. Simulation results show that it performs well in signal tracking and differentiation acquisition, and reduces the computational resources needed. Experiments conducted on the speed and position detection system for a maglev train demonstrate that the proposed TD group, with moving average algorithm, can filter noises, amend distortion signals effectively, and compensate for phase delays when the train is passing over track joints.NRF (Natl Research Foundation, S’pore)MOE (Min. of Education, S’pore)Accepted versio

1 Tracking Pedestrian with Incrementally Learned

Most of the existing tracking algorithms are challenged for the deficiency of han-dling non-stationary target appearance such as the drastic scale and perspective change of a moving pedestrian in the PTZ surveillance record. We propose a novel pedestrian tracking algorithm to cope with this problem by integrating incrementally learned repre-sentation and classification model. In the representation model, besides the widely used intensity template, the contour template with several sets of profiles from different per-spectives is also employed to cope with the change of pedestrian contour. Both templates are updated incrementally during the tracking process to deal with the non-stationary appearance of the pedestrian. In the classification model, a multiple instance classier based on an incremental support vector machine is trained on-line as new observation becomes available. The learned classifier keeps the evolving representation model from drifting and enables reinitialization of the tracker once a failure occurs in the tracking process. The effectiveness of our algorithm is tested over several surveillance records captured from PTZ. The experiment results show that our algorithm can track the pedes-trian more robustly than the other two compared cutting edge tracking algorithms

High-precision tracking differentiator via generalized discrete-time optimal control

An enhanced discrete-time tracking differentiator (TD) with high precision based on discrete-time optimal control (DTOC) law is proposed. This law takes the form of state feedback for a double-integral system that adopts the Isochronic Region approach. There, the control signal sequence is determined by a linearized criterion based on the position of the initial state point on the phase plane. The proposed control law can be easily extended to the TD design problem by combining the first-state variable of the double-integral system with the desired trajectory. To improve the precision of the discretization model, we introduced a zero-order hold on the control signal. We also discuss the general form of DTOC law by analysing the relationship between boundary transformations and boundary characteristic points. After comparing the simulation results from three different TDs, we determined that this new TD achieves better performance and higher precision in signal-tracking filtering and differentiation acquisition than do existing TDs. Also the comparisons of the computational complexities between the proposed DTOC law and normal one are demonstrated. For confirmation of its utility, we processed raw phasor measurement units data via the proposed TD. In the absence of complex power system modelling and historical data, it was verified that the proposed TD is suitable for applications of real-time synchrophasor estimations, especially when the states are corrupted by noise.Ministry of Education (MOE)National Research Foundation (NRF)Accepted versionThis study is an outcome of the Future Resilient System (FRS) project at the Singapore-ETH Centre (SEC), which is funded by the National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) program. Part of this work is also supported by the Ministry of Education (MOE), Singapore, under Contract MOE 2016-T2-1-119

An amiRNA screen uncovers redundant CBF and ERF34/35 transcription factors that differentially regulate arsenite and cadmium responses

Arsenic stress causes rapid transcriptional responses in plants. However, transcriptional regulators of arsenic-induced gene expression in plants remain less well known. To date, forward genetic screens have proven limited for dissecting arsenic response mechanisms. We hypothesized that this may be due to the extensive genetic redundancy present in plant genomes. To overcome this limitation, we pursued a forward genetic screen for arsenite tolerance using a randomized library of plants expressing >2,000 artificial microRNAs (amiRNAs). This library was designed to knock-down diverse combinations of homologous gene family members within sub-clades of transcription factor and transporter gene families. We identified six transformant lines showing an altered response to arsenite in root growth assays. Further characterization of an amiRNA line targeting closely homologous CBF and ERF transcription factors show that the CBF1,2 and 3 transcription factors negatively regulate arsenite sensitivity. Furthermore, the ERF34 and ERF35 transcription factors are required for cadmium resistance. Generation of CRISPR lines, higher-order T-DNA mutants and gene expression analyses, further support our findings. These ERF transcription factors differentially regulate arsenite sensitivity and cadmium tolerance