Current Issue : July - September Volume : 2016 Issue Number : 3 Articles : 4 Articles
Analytical redundancy technique is of great importance to guarantee the reliability and safety of aircraft engine system. In this\npaper, a machine learning based aeroengine sensor analytical redundancy technique is developed and verified through hardwarein-\nthe-loop (HIL) simulation. The modified online sequential extreme learning machine, selective updating regularized online\nsequential extreme learning machine (SROS-ELM), is employed to train the model online and estimate sensor measurements. It\nselectively updates the output weights of neural networks according to the prediction accuracy and the norm of output weight\nvector, tackles the problems of singularity and ill-posedness by regularization, and adopts a dual activation function in the hidden\nnodes combing neural and wavelet theory to enhance prediction capability.The experimental results verify the good generalization\nperformance of SROS-ELM and show that the developed analytical redundancy technique for aeroengine sensor fault diagnosis\nbased on SROS-ELM is effective and feasible....
The axisymmetric flow field around a ducted rotor is thoroughly analysed by means of a nonlinear and semi-analytical model which\nis able to deal with some crucial aspects of shrouded systems like the interaction between the rotor and the duct, and the slipstream\ncontraction and rotation. Not disregarding the more advanced CFD based methods, the proposed procedure is characterised by\na very low computational cost that makes it very appealing as analysis tool in the preliminary steps of a design procedure of\nhierarchical type. The work focuses on the analysis of the effects of the camber and thickness of the duct cross section onto the\nperformance of the device. It has been found that an augmentation of both camber and thickness of the duct leads to an increase\nof the propulsive ideal efficiency...
Unmanned Aerial Vehicle (UAV) is a nonlinear dynamic system with uncertainties and noises. Therefore, an appropriate control\nsystem has an obligation to ensure the stabilization and navigation of UAV. This paper mainly discusses the control problem of\nquad-rotor UAV system, which is influenced by unknown parameters and noises. Besides, a sliding mode control based on online\nadaptive error compensation support vector machine (SVM) is proposed for stabilizing quad-rotor UAV system. Sliding mode\ncontroller is established through analyzing quad-rotor dynamics model in which the unknown parameters are computed by offline\nSVM. During this process, the online adaptive error compensation SVM method is applied in this paper. As modeling errors and\nnoises both exist in the process of flight, the offline SVM one-time mode cannot predict the uncertainties and noises accurately.\nThe control law is adjusted in real-time by introducing new training sample data to online adaptive SVM in the control process,\nso that the stability and robustness of flight are ensured. It can be demonstrated through the simulation experiments that the UAV\nthat joined online adaptive SVM can track the changing path faster according to its dynamic model. Consequently, the proposed\nmethod that is proved has the better control effect in the UAV system....
Aiming at reducing joint velocity jumps caused by an unexpected joint-locked failure during space manipulator on-orbit operations\nwithout shutting downmanipulator, trajectory optimization strategy considering the unexpectedness characteristics of joint-locked\nfailure is proposed in the paper, which can achieve velocity jumps reduction in both operation space and joint space simultaneously.\nIn the strategy, velocity in operation space concerning task completion directly is treated as equality constraints, and velocity\nin joint space concerning motion performance is treated as objective function. Global compensation vector which consists of\ncoefficient, gradient of manipulability, and orthogonal matrix of null space is constructed to minimize the objective function. For\neach particular failure time, unique optimal coefficient can be obtainedwhen the objective function is minimal.As a basis, amethod\nfor optimal coefficient function fitting is proposed based on a priori failure information (possible failure time and the corresponding\noptimal coefficient) to guarantee the unexpectedness characteristics of joint-locked failure. Simulations are implemented to validate\nthe efficiency of trajectory optimization strategy in reducing velocity jumps in both joint space and operation space. And the\nfeasibility of coefficient function is also verified in reducing velocity jump no matter when joint-locked failure occurs...
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