Design and Research of Dynamical Property Testing System for Nano and Micron Inertial Components

Abstract:

The accelerometer is a sensitive inertial component of the inertial navigation system, and its output signal is proportional to the transporters acceleration. In system design and test, the dynamic characteristic of the closed-loop system is an important parameter. At present, the use of wire vibration or angular vibration to provide an input signal cannot meet the amplitude and phase of system testing requirements, and the test cost is high. Therefore, the study of how the dynamic characteristics of electrical simulation test system to give a precise mathematical accelerometer model is an important part of the analysis of the inertial navigation system,which is an effective method to acquire the dynamic characteristics and can be extended to mini inertia instruments. In this paper, we use the system identification method to identify the model of the system. Modeling of the system identification method is to determine the mathematical model of the system by observing the relationship between system inputs and outputs. The content of system identification generally includes four parts which are experimental design, model structure identification, parameter estimation and model test. Circuit simulation test of dynamic character of accelerometer system and model identification method have been applied in practical application. This paper has tested the accuracy of developed system designed by different system identifications.

• Periodical: Key Engineering Materials (Volumes 609-610)
• Pages: 1349-1356
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.609-610.1349
• Online since: April 2014
• Authors: Li Yong Zhang*
• Keywords: Electrical Simulation Test, Hopfield Neural Network Identification, Least Square (LS), Pseudo-Random Signal

References

[1] J. vander Geer, J.A.J. Hanraads, R.A. Lupton, The art of writing a scientific article, J. Sci. Commun. 163 (2000) 51-59.

[2] Culshaw B. Optical Fiber Sensing and Signal Pracessing. 1986. 72.

[3] J. Kennedy,R. Eberhart. Particle swarm optimization. IEEE International Conference on Neural Networks, 1995, 4, 1942-(1948).

[4] W.D. Chang. Parameter identification of Chen and Lu systems: A differential zevolution approach, Chaos, Solitons &Fractals, 2007, 32(4): 1469-1476.

DOI: 10.1016/j.chaos.2005.11.067

[5] J.E. Slotine W.P. Li. On the adaptive control of robot manipulators. The International Journal of Robotics Research, 1987, 6(3): 49-59.

[6] O. Karahan,Z. Bingul. Modelling and identification of STAUBLI RX-60 robot. 2008 IEEE Conference on Robotics, Automation and Mechatronics, 2008，Chengdu: 78-83.

DOI: 10.1109/ramech.2008.4681356