Table of Contents
Journal of Nonlinear Dynamics
Volume 2017, Article ID 6594861, 20 pages
Research Article

System Performance of an Inertially Stabilized Gimbal Platform with Friction, Resonance, and Vibration Effects

1Department of Electrical and Computer Engineering, College of Engineering and Computer Science, California State University Northridge, Los Angeles, CA 91330, USA
2Mechanical Engineering Department, College of Engineering and Computer Science, California State University Northridge, Los Angeles, CA 91330, USA
3Northrop Grumman Corporation, Woodland Hills, CA 91367, USA

Correspondence should be addressed to Ruting Jia; ude.nusc@aij.gnitur

Received 3 January 2017; Accepted 16 February 2017; Published 28 March 2017

Academic Editor: Huai-Ning Wu

Copyright © 2017 Ruting Jia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The research work evaluates the quality of the sensor to perform measurements and documents its effects on the performance of the system. It also evaluates if this performance changes due to the environments and other system parameters. These environments and parameters include vibration, system friction, structural resonance, and dynamic system input. The analysis is done by modeling a gimbal camera system that requires angular measurements from inertial sensors and gyros for stabilization. Overall, modeling includes models for four different types of gyros, the gimbal camera system, the drive motor, the motor rate control system, and the angle position control system. Models for friction, structural resonance, and vibration are analyzed, respectively. The system is simulated, for an ideal system, and then includes the more realistic environmental and system parameters. These simulations are run with each of the four types of gyros. The performance analysis depicts that for the ideal system; increasing gyro quality provides better system performance. However, when environmental and system parameters are introduced, this is no longer the case. There are even cases when lower quality sensors provide better performance than higher quality sensors.