This article has two purposes： the first is to give some structure results for the class of m-isometries, and the second purpose is to extend the notions of left and right inverses to m-left and m-right inverses resp...This article has two purposes： the first is to give some structure results for the class of m-isometries, and the second purpose is to extend the notions of left and right inverses to m-left and m-right inverses respectively.展开更多
The real dynamic thrust measurement system usually tends to be nonlinear due to the complex characteristics of the rig, pipes connection, etc. For a real dynamic measuring system, the nonlinearity must be eliminated b...The real dynamic thrust measurement system usually tends to be nonlinear due to the complex characteristics of the rig, pipes connection, etc. For a real dynamic measuring system, the nonlinearity must be eliminated by some adequate methods. In this paper, a nonlinear model of dynamic thrust measurement system is established by using radial basis function neural network （RBF-NN）, where a novel multi-step force generator is designed to stimulate the nonlinearity of the system, and a practical compensation method for the measurement system using left inverse model is proposed. Left inverse model can be considered as a perfect dynamic compensation of the dynamic thrust measurement system, and in practice, it can be approximated by RBF-NN based on least mean square （LMS） algorithms. Different weights are set for producing the multi-step force, which is the ideal input signal of the nonlinear dynamic thrust measurement system. The validity of the compensation method depends on the engine＇s performance and the tolerance error 0.5%, which is commonly demanded in engineering. Results from simulations and experiments show that the practical compensation using left inverse model based on RBF-NN in dynamic thrust measuring system can yield high tracking accuracy than the conventional methods.展开更多
文摘This article has two purposes： the first is to give some structure results for the class of m-isometries, and the second purpose is to extend the notions of left and right inverses to m-left and m-right inverses respectively.
文摘The real dynamic thrust measurement system usually tends to be nonlinear due to the complex characteristics of the rig, pipes connection, etc. For a real dynamic measuring system, the nonlinearity must be eliminated by some adequate methods. In this paper, a nonlinear model of dynamic thrust measurement system is established by using radial basis function neural network （RBF-NN）, where a novel multi-step force generator is designed to stimulate the nonlinearity of the system, and a practical compensation method for the measurement system using left inverse model is proposed. Left inverse model can be considered as a perfect dynamic compensation of the dynamic thrust measurement system, and in practice, it can be approximated by RBF-NN based on least mean square （LMS） algorithms. Different weights are set for producing the multi-step force, which is the ideal input signal of the nonlinear dynamic thrust measurement system. The validity of the compensation method depends on the engine＇s performance and the tolerance error 0.5%, which is commonly demanded in engineering. Results from simulations and experiments show that the practical compensation using left inverse model based on RBF-NN in dynamic thrust measuring system can yield high tracking accuracy than the conventional methods.
基金supported by NSFC(No.10871161,No.10971160,No.11101336,No.11226044)Scientific Research Program funded by Shaanxi Provincial Education Department(No.12JK0876)the Fundof Xi'an University of Architecture and Technology(No.RC1110,No.JC1219,No.QN1316)
文摘张力检测是实现两电机变频调速系统高性能控制及无传感器运行的关键。为实现两电机系统的张力辨识,在证明系统数学模型左可逆的基础上,针对其左逆辨识数学表达式较为复杂、存在参数时变和负载扰动的特点,提出最小二乘支持向量机(least squares support vector machines,LSSVM)左逆张力辨识策略。该策略采用LSSVM构造张力左逆辨识模型,实现简单,逼近精确。对其进行仿真及实验研究,结果表明,该策略辨识出的张力能快速、准确跟踪实际值,具有良好的鲁棒性,适用于两电机调速系统的张力辨识。
