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Design for low-noise feedback control with MEMS gyroscopes

17 May 2016  | Mark Looney

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MEMS gyroscopes provide 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 behaviours, 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 behaviours has a number of rewards, such as being able to establish relevant requirements for the feedback sensing element or, conversely, analysing 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 behaviours 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 behaviours in a MEMS gyroscope and how it impacts key system behaviours 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 manoeuvring 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 "stabilised 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.


Example application
Moving from the architectural view of a motion control system in figure 1, valuable definitions and insights also come from analysing application-specific, physical attributes. Consider the system in figure 2, which offers a conceptual view of an automated inspection system for a production line. This camera system inspects items that move in and out of its field of view on a conveyor belt. In this arrangement, the camera attaches to the ceiling through a long bracket, which establishes its height (See "D" in figure 2), in order to optimise its field of view for the size of the objects it will inspect. Since factories are full of machinery and other activity, the camera can experience swinging motion (see "ωSW(t)" in figure 2) at times, which can cause distortion in the inspection images.

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