MEMS gyroscopes offer a simple way to measure angular rate of rotation, in packages that easily attach to printed circuit boards, so they are a popular choice to serve as the feedback sensing element in many different types of motion control systems. In this type of function, noise in the angular rate signals (MEMS gyroscope output) can have a direct influence over critical system behaviors, such as platform stability and is often the defining factor in the level of precision that a MEMS gyroscope can support.
Therefore, “low-noise” is a natural, guiding value for system architects and developers as they define and develop new motion control systems. Taking that value (low-noise) a step further, translating critical system-level criteria, such as pointing accuracy, into noise metrics that are commonly available in MEMS gyroscope datasheets, is a very important part of early conceptual and architectural work. Understanding the system’s dependence on gyroscope noise behaviors has a number of rewards, such as being able to establish relevant requirements for the feedback sensing element or, conversely, analyzing the system-level response to noise in a particular gyroscope.
Once system designers have a good understanding of this relationship, they can focus on mastering the two key areas of influence that they have over the noise behaviors in their angular rate feedback loops: (1) developing the most appropriate criteria for MEMS gyroscope selection and (2) preserving the available noise performance throughout the sensor’s integration process.
Motion control basics
Developing a useful relationship between the noise behaviors in a MEMS gyroscope and how it impacts key system behaviors often starts with a basic understanding of how the system works. Figure 1 offers an example architecture for a motion control system, which breaks the key system elements down into functional blocks. The functional objective for this type of system is to create a stable platform for personnel or equipment that can be sensitive to inertial motion. One example application is for a microwave antenna on an autonomous vehicle platform, which is maneuvering through rough conditions at a speed that causes abrupt changes in vehicle orientation. Without some real-time control of the pointing angle, these highly-directional antennas may not be able to support continuous communication, while experiencing this type of inertial motion.

Figure 1: Example Motion Control System Architecture
The system in Figure 1 uses a servo motor, which will rotate in a manner that is equal and opposite of the rotation that the rest of the system will experience. The feedback loop starts with a MEMS gyroscope, which observes the rate of rotation (ωG) on the “stabilized platform.” The gyroscope’s angular rate signals then feed into application-specific digital signal processing that includes filtering, calibration, alignment and integration to produce real-time, orientation feedback, (φE). The servo motor’s control signal (φCOR) comes from a comparison of this feedback signal, with the “commanded” orientation (φCMD), which may come from a central mission control system or simply represent the orientation that supports ideal operation of the equipment on the platform.
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