Introduction

The S.I.R.I.U.S. flight computer is the next generation of the N.E.B.U.L.A. flight computer. Designed for the 2020 IREC competition in New Mexico, S.I.R.I.U.S. has many similarities to N.E.B.U.L.A. from a functional standpoint but learning from last year’s challenges, the board has been physically redesigned to fit a smaller form-factor for easier testing. The board also has many features to make it far more user-friendly and more effective as a whole. Read on for those details.

Electrical

S.I.R.I.U.S. is an all-encompassing flight computer measuring altitude, acceleration, pitch, roll, yaw, magnetic fields, and GPS location with the ability to send this data over radio as well as save to an onboard MicroSD card. This flight computer features redundant altimeters (MS5607 and MPL3115A2) with a max measurable altitude of 120,000 feet, one high-g accelerometer (ADXL377), and a low-g accelerometer in a 9 Degree of Freedom IMU (LSM9DS1). For GPS tracking, the flight computer communicates over UART with the Adafruit Ultimate GPS Module with the ability to add an external antenna. Also using UART is the XBee Pro SX 900MHz transceiver, with a maximum LOS range of 60 miles (according to the datasheet). S.I.R.I.U.S. boasts four MOSFET deployment channels capable of handling 18V as well as two PWM outputs for servo operation (with the ability to use the regulated 5V output included in the board). Coinciding the four deployment channels are four LEDs that, by default, indicate continuity of charges using a voltage divider network connected to the analog inputs of the Teensy. These four LEDs are programmable to fit the user’s needs. The flight computer also has five buttons connected to the Teensy for user input. Buttons 1-4 default to a custom sequence for testing deployment channels 1-4, respectively, while the fifth button defaults to sending a test message over the XBee UART. The LSM9DS1 Inertial Measurement Unit (IMU) communicates over I2C to deliver acceleration readings up to 16g, angular rate readings up to 2000dps, and magnetic readings up to 16 gauss. Pitch, roll, and yaw will be determined using the readings from the IMU.

• (1) – Teensy 3.6 Microcontroller
• (1) – MS5607 Altimeter
• (1) – MPL3115A2 Altimeter
• (1) – ADXL377 200g Accelerometer
• (1) – LSM9DS1 16g Accelerometer, Gyroscope, Magnetometer
• (4) – A04320 Deployment MOSFETs
• (5) – Indicator LEDs
• (1) – TPS54527 Switching Voltage Regulator (5V)
• (1) – LT3080 LDO (3.3V)
• (1) – Adafruit Ultimate GPS
• (1) – XBee Pro SX or XBee SX 900MHz Transceiver

Goals for S.I.R.I.U.S.

One specific goal I have for S.I.R.I.U.S. is the ability to determine apogee. So far I have looked at altitude, acceleration, and a combination of both for apogee detection. Altimeters are challenging to use for apogee detection specifically because the rocket likely hits mach. In practice, one would constantly read the altimeter going up, and once it is going down for enough samples, you have reached apogee. I know little to nothing about fluid mechanics, but I know pressure is dramatically influenced near mach so the barometric altimeters used would be unreliable if not ineffective. Another approach for apogee detection is the integration of acceleration to find zero velocity. I am doubtful of this method because the two accelerometers on the board measure 16g and 200g, respectively. I am fairly certain the rocket experiences over 16g of acceleration, meaning the low-g accelerometer readings would “clip.” On the other end of the spectrum, I do not think the high-g accelerometer has a granular enough resolution for measuring lower g-forces.

The most simple approach I have looked at is detection of nose-over (when the rocket starts turning upside down). To detect nose-over it should be as simple as seeing when the vertical component of acceleration hits zero and another dimension increases. My hesitation from this method comes from the intense vibrations the rocket experiences. I do not want some vibrational “noise” to trick the accelerometer.

The method for apogee detection I am most interested in is the Kalman Filter. From Wikipedia, a Kalman Filter is “an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone.” In my head, the Kalman Filter takes a series of variables and makes predictions based on previous data. Then, the filter compares the next set of acquired data with the expected and carries on. The Kalman Filter is frequently used in vehicle guidance, specifically with aircraft and spacecraft (hence why I am trying to work with it).

Structures