Wind Turbine Condition Monitoring : Sensorless Kalman Filter Example for Bearing Fault Detection (I) Two modelling techniques were applied: the velocity-based method and a modified version of the Jacobian linearisation method. The difficulties of this task reside in a lack of available trimming procedure, treated in the literature as a usual prerequisite for the LPV modelling, and a high dimension of the scheduling parameter vector. This paper presents the construction of a linear parameter varying (LPV) model describing the behaviour of a re-entry space probe in flight.
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The model describing the behaviour of a vehicle in free flight is nonlinear, characterised by the off-equilibrium dynamics and absence of the input signal. Keywords: Control education using laboratory equipment, University-industry co-operation in control engineering education, Complexity modellingĪbstract: Ballistic testing of flying vehicles, such as space probes or projectiles, is an important part of their design. LPV Modelling of the Atmospheric Re-Entry Vehicle in Free Flight (I) Keywords : Simulators, Nonlinear control system
We introduce a way to emulate the principle of loss of resistance felt by the anaesthetist when the needle reaches the appropriate depth. This works aims at simulating this second part as it has already been done on the first part in previous studies. This medical operation can be divided into two different gestures: first the insertion of a needle between two vertebrae and second the application of pressure on the plunger all along the insertion of the needle.
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The goal of this work is to offer a novel robotic solution to teach students how to perform an epidural anaesthesia. In France, the Haute Autorité de la Santé (H.A.S.) has stated to "never the first time with a patient" as a requirement for the training of new doctors.
GDR MACS - Application in Robotics and AutomationĪbstract: Medicine requires advanced teaching methods in order to reach an efficient student training without having to train them directly on patients. And from the customer perspective, predictive maintenance, fault detection, diagnosis and prognostics are key to improving operational reliability and the overall performance of Airbus products. Digitalisation is also transforming the way our industry operates, creating numerous opportunities to decrease development cycles whilst increasing maturity and level of confidence. Distributed architectures are promising, not only at aircraft or satellite level, but as part of a “system of systems”, connecting all aerospace operations’ stakeholders. Efficient systems’ architectures must be designed with this in mind. In this context, some future aerospace challenges will include the reduction of the system complexity while maintaining the same high-level safety standards. In addition, the design of space systems faces increasingly aggressive worldwide competition, with continuously changing and more demanding markets, calling for reliable, efficient, and new control design tools and technologies, compatible with reduced development cycles. Flying more safely, more simply, but also fully respecting environmental targets are our growing challenges. Passengers have also become increasingly mindful of the ecological impact of their travel choices and are asking for more comfort and connectivity. By 2050, the air transport industry could be handling up to 16 billion passengers and 400 million tons of cargo annually. The aviation sector is expanding passenger demand doubles every 15 years. Keywords: Avionics and on-board equipmentsĪbstract: Starting in 1974, with airplanes ranging from the A300, to the A380, the A350 XWB, and the A320/A330neos, Airbus delivered its 10,000th aircraft in October 2016.
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