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Repoint is a robust and reliable points concept which breaks with 200 years of tradition to offer a change in design that will increase reliability, reduce maintenance costs and boost capacity on the railways. It is the result of research carried out with industry experts into improved switches which eradicate known issues with existing designs. The two-and-a-half-year project, funded by the UK rail safety body RSSB (formerly known as the Rail Safety and Standards Board), will see a full scale, prototype track switch developed and deployed. Using safety concepts derived from aerospace and the nuclear industries, Repoint allows redundant, fail-safe actuation and locking of track switches for the first time. This means that a failure of a single actuator element will not cause the failure of the entire switch – allowing trains to continue until such a time as maintenance becomes feasible. With increasing traffic density throughout the network, windows for maintenance work are becoming tighter, which is why the Repoint technology - which will be developed alongside RSSB, London Underground, and its supply base - has so much potential. Repoint has been led by Professor Roger Dixon, Senior Project Engineer Sam Bemment, Professor Roger Goodall, and Dr Chris Ward. The team are part of the Control Systems Research Group in the University's Wolfson School of Mechanical, Electrical and Manufacturing Engineering. Professor Dixon, Head of the Control Systems Research Group, said: "Bringing Repoint a step closer to operation is a fantastic achievement with the potential to fix a 200-year-old problem on rail networks around the world. "Great Britain's rail network, in particular, is under pressure to provide increased capacity and reliability at a reduced cost. With the support of RSSB, we can make track switch failures a thing of the past by introducing a cost-effective alternative which has not been seen before." Neil Webster, RSSB Future Railway Programme Director, commented: "Our continued support for the Repoint project reinforces our belief in the technology and the potential it has to deliver real, tangible benefits to the future of the rail industry. We look forward to seeing the design once implemented, improving reliability and increasing capacity on our ever expanding rail network." With RSSB and Loughborough University at the forefront of implementing the technology in the UK, opportunities are now being pursued with development partners to roll out the patented technology across international rail networks, with discussions so far held with companies in South Africa, Australia and China. Explore further: Rail researchers work on UK's first Tram-Train scheme

Hamad A.,Liverpool John Moores University | Jones K.,LJMU | Yu D.,Control Systems Research Group | Gomm J.B.,LJMU | Sangha M.S.,Cummins
ACM International Conference Proceeding Series | Year: 2011

Fault detection (FD) scheme is developed for automotive engines in this paper. The method uses an independent Radial Basis Function (RBF) Neural Network model to model engine dynamics, and the modelling errors are used to form the basis for residual generation. The method is developed and the performance assessed using the engine benchmark, the Mean Value Engine Model (MVEM) with Matlab/Simulink. Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The simulation results showed that all the simulated faults can be clearly detected in the dynamic condition throughout the operating range. © 2011 ACM.

Ragb O.,Liverpool John Moores University | Jones K.,LJMU | Yu D.,Control Systems Research Group | Gomm J.B.,LJMU
ACM International Conference Proceeding Series | Year: 2011

Automatic control of fuel cell stacks (FCS) using non-adaptive and adaptive radial basis function (RBF) neural network methods are investigated in this paper. The neural network inverse model is used to estimate the compressor voltage for fuel cell stack control at different current demands and 30% reduction in the compressor gain in order to prevent the oxygen starvation. A PID controller is used in the feedback to adjust the difference between the requested and the actual oxygen ratio by compensating the neural network inverse model output. Furthermore, the RBF inverse model is made adaptive to cope with the significant parameter uncertainty, disturbances and environment changes. Simulation results show the effectiveness of the adaptive control strategy. © 2011 ACM.

Alwi H.,University of Leicester | Alwi H.,Control Systems Research Group | Edwards C.,University of Leicester | Edwards C.,Control Systems Research Group
AIAA Guidance, Navigation, and Control Conference 2012 | Year: 2012

This paper presents the results of a novel sensor fault tolerant control scheme based on a robust sliding mode observer. In order to ensure wide coverage of the ight envelope and robustness against uncertainty, the observer is designed using a robust LPV model based method. Fault tolerance is achieved by exploiting the capability of the robust LPV sliding mode observer to provide f ault reconstruction. This allows the faulty measurement to be corrected before it is used in the controller computations, and therefore enables the controller to maintain the required performance. The correction of the faulty measurement means that there is no requirement for the development of a new controller in order to achieve tolerance to faults. Two sets of results are presented to highlight the potential of the proposed scheme - one using a single run at three chosen ight conditions (to provide details of performance of the scheme across the ight envelope) and the second is based on parametric runs developed for an industrial evaluation process. All the results are obtained using a nonlinear benchmark model and demonstrate the potential of the proposed scheme. © 2012 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

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