On-Line System Identification and Control Strategies for Improving the Performance of Inertial MEMS

智能优势：微机电系统（MEMS）的最新发展导致了各种各样的微传感器和执行器，它们在飞机和航天器的制导和控制方面具有巨大的潜力。这种设备可以以低功耗提供高带宽性能，并且它们的小尺寸和重量允许在传统传感器/执行器技术限制测量通道数量和执行器自由度的应用中使用许多传感器和/或执行器。为了使这些新兴技术实现具有竞争力的性能水平，需要创新的设备校准和闭环控制方法。这种方法的必要性源于这样一个事实，即由于关键机电元件的微观规模，制造的不规则性可以在任何一批假定相同的设备中产生广泛的动态响应。传感器和执行器动力学的灵敏度和可变性可以通过基于输入/输出数据的详细建模和参数识别的单个设备的精确校准以及内置在微设备中的控制回路来补偿。更广泛的影响：本研究的重点是开发多变量系统识别和控制策略，以提高MEMS振动角速率传感器的性能。本研究的智力价值源于对以下需求所激发的问题的研究：1)第一个主要研究课题是开发实现最佳器件灵敏度的算法。当传感器的两个动态模式呈现退化时，达到最高的信噪比。鲁棒的测试方法和算法已经开发出来，通过传感器动态的实时调谐，极大地提高了与角速率估计相关的信噪比。此外，在线算法在运行过程中持续探测传感器动态，以保持调谐状态；2)第二个主要研究课题开发了一种通过闭环噪声谱的整形来最小化指定积分时间的速率不确定性的过程。在这种情况下，控制滤波器中的额外自由度被用于以期望的方式塑造速率估计的allan方差。该研究项目是在PI的微传感器实验室进行的，其传感器原型是从美国宇航局喷气推进实验室借给PI的。研究贡献具有显著的广泛的影响，远远超出了研究生教育。在这个项目下设计的算法和方法已经改变了喷气推进实验室和其他地方的pi合作者建模、测试和控制传感器原型的方式。在加州大学洛杉矶分校的本科工程课程中，一系列设计课程让学生接触到这种快速发展的传感器技术，其中包括为加州大学洛杉矶分校DARPA大挑战赛团队设计和建造一个稳定的传感器平台，该平台使用了由BEI Technologies捐赠的商业GyroChip振动陀螺仪。

Intellectual Merit:Recent developments in microelectromechanical systems (MEMS) have lead to a variety of microsensorsand actuators that have great potential for guidance and control of aircraft and spacecraft. Such devicescan deliver high-bandwidth performance with low power consumption, and their small size and weightpermit the use of numerous sensors and/or actuators in applications where conventional sensor/actuatortechnology limits the number of measurement channels and actuator degrees of freedom.For these emerging technologies to realize competitive performance levels, innovative methods are requiredfor device calibration and closed-loop control. The necessity of such methods arise from the factthat fabrication irregularities can produce a wide range of dynamic responses among any batch of supposedlyidentical devices due to the microscopic scale of key electro-mechanical components. The sensitivityand variability of sensor and actuator dynamics can be compensated by precise calibration of individualdevices based on detailed modeling and parameter identification from input/output data, and by controlloops built into the micro device.Broader Impact:This research focuses on the development of multivariable system identification and control strategiesfor improving the performance of MEMS vibratory angular rate sensors. The intellectual merit of thisresearch derives from the study of problems stimulated by the following needs: 1) The first major researchtopic develops algorithms for achieving optimum device sensitivity. The highest signal-to-noise ratiois achieved when two dynamic modes of the sensor are rendered degenerate. Robust test methods andalgorithms have been developed that vastly improve the SNR associated with the angular rate estimate viareal-time tuning of the sensor dynamics. Furthermore, on-line algorithms continuously probe the sensordynamics during operation in order to maintain the tuned condition; 2) The second major research topicdevelops a process for minimizing the rate uncertainty at designated integration times via the shaping ofclosed-loop noise spectra. In this case, additional freedom in the control filters is exploited for shaping theAllan variance of the rate estimate in a desired manner.This research program is conducted in the PIs Microsensor Laboratory on sensor prototypes loanedto the PI from NASAs Jet Propulsion Laboratory. The research contributions have significant broaderimpacts well beyond graduate education. The algorithms and methods devised under this program havechanged the way the PIs collaborators at JPL and elsewhere model, test and control their sensor prototypes.In the undergraduate engineering curriculum at UCLA, a series of design courses expose students to thisrapidly evolving sensor technology with projects that include the design and construction of a stabilizedsensor platform for the UCLA DARPA Grand Challenge Team using commercial GyroChip vibratorygyros donated by BEI Technologies.