Micro-Satellite Systems Design, Integration, and Flight

Abstract

Within the past decade, the aerospace engineering industry has evolved beyond the constraints of using single, large, custom satellites. Due to the increased reliability and robustness of commercial, off-the-shelf printed circuit board components, missions have instead transitioned towards deploying swarms of smaller satellites. Such an approach significantly decreases the mission cost by reducing custom engineering and deployment expenses. Nanosatellites can be quickly developed with a more modular design at lower risk. The Alpha mission at the Cornell University Space Systems Studio is fabricated in this manner. However, for the purpose of development, the initial proof of concept included a two-satellite system. The manuscript will discuss system engineering approaches used to model and mature the design of the pilot satellite. The two systems that will be primarily focused on are the attitude control system of the carrier nanosatellite and the radio frequency communications on the excreted femto-satellites. Milestones achieved include ChipSat to ChipSat communication, ChipSat to ground station communication, packet creation, error correction, appending a preamble, and filtering the signal. Other achievements include controller traceability/verification and validation, software rigidity tests, hardware endurance testing, Kane damper, and inertial measurement unit tuning. These developments matured the technological readiness level (TRL) of systems in preparation for satellite deployment.

Keywords: CDMA; GFSK; IMU tuning; Kane damper; MBSE; PD controller; RF communication; RTL-SDR; TI-RTOS; TinyGS; controller optimization controller modeling; controller verification and validation; forward error correction; matched filtering; systems engineering.

Figure 1

(a) Moment of inertia testing for CubeSat attitude control system development; (b) balloon test to validate ChipSat long-range radio frequency capabilities.

Figure 2

The systems engineering “Vee Diagram”.

Figure 3

Black box diagram (George E. Mobus and Michael C. Kalton, p. 604 [33]).

Figure 4

Subsystem black box diagram (George E. Mobus Michael C. Kalton, p. 607 [33]).

Figure 5

Process diagram (George E. Mobus Michael C. Kalton, p. 609 [33]).

Figure 6

Interface matrix.

Figure 7

Network diagram.

Figure 8

Full network diagram.

Figure 9

(a) Integration testing flight hardware; (b) flight-ready electronics aboard the CubeSat.

Figure 10

Connecting to TI launchpads on SmartRF Studio 7.

Figure 11

(a) Configuring the PacketTX in SmartRF Studio 7. (b) Configuring the PacketRX in SmartRF Studio 7.

Figure 12

TI-RTOS kernel priority levels. Image Credit: TI [34].

Figure 13

Semaphore process flow.

Figure 14

TI-RTOS semaphore APIs.

Figure 15

Binary frequency shift keying (BFSK).

Figure 16

Dual Gaussian frequency shift keying (2DFSK). Note that the y axis in this instance displays frequency. Image Credit: “802.11 Wireless Networks: The Definitive Guide” [36].

Figure 17

4 GFSKs. Four different frequencies corresponding to the four different possible 2-bit sequences. Image Credit: “802.11 Wireless Networks: The Definitive Guide” [36].

Figure 18

Four-channel Gaussian frequency shift keying (GFSK). Image Credit: “802.11 Wireless Networks: The Definitive Guide” [36].

Figure 19

Data packet formulation.

Figure 20

Binary matrix multiplication of the LHS G matrix with the data.

Figure 21

Appended FEC.

Figure 22

Addition of a preamble.

Figure 23

CDMA allows the receiver to recognize which device is transmitting.

Figure 24

AIRSPY dashboard identifies ChipSat transmissions at 915 MHz.

Figure 25

Photo from a balloon launch.

Figure 26

Example TinyGS dashboard.

Figure 27

Satellite height as a function of TinyGS transmission timestamps.

Figure 28

CubeSat detumble after deployer ejection. The blue, red, and green arrows symbolizes the initial spin of the satellite along its three respective axes. Image Credit: Josh Umansky-Castro.

Figure 29

(a) CubeSat and light sail spin stabilization; (b) CubeSat achieves pointing. Image Credit: Josh Umansky-Castro.

Figure 30

Torque coils made in-house.

Figure 31

Passive ACS techniques used in Alpha: (a) lead weights to increase the moment rotational moment of inertia; (b) steel weights to balance the center of mass toward the middle of the CubeSat; (c) the rotational z axis and the geometric z axis within 5 deg for stability. Image Credit: Josh Umansky-Castro.

Figure 32

Kane damper model.

Figure 33

PD feedback block model.

Figure 34

Unrealistic convergence of Kane controller.

Figure 35

Moment of inertia testing setup [40]. (a) Diagram of setup; (b) Live picture of setup as tested.

Figure 36

True convergence of Kane controller.

Figure 37

Circuit setup (a) Wire diagram of the ACS endurance testing; (b) Live picture of setup as tested.

Figure 38

The gyrocompass measuring the magnetic field of the torque coil.

Figure 39

Confining search algorithm. The red dot symbolizes optimal result, within resolution of the search algorithm.

Figure 40

Zoomed-in search region. The red square symbolizes the region in which the optimal solution lies.

Figure 41

Optimal solution found on the border. This is shown by the red dot.

Figure 42

Borderline search region with autonomous search region expansion. The red square symbolizes a growth in the search region in order to search beyond the boarder.

Figure 43

Borderline search region without expansion. The red square symbolizes a decision not to expand the search region, but to confirm a boarder result by searching within the convergence field.

Figure 44

Pseudocode to find optimal Id and c.

Figure 45

Convergence criteria. n number of points within an m% region of convergence, and the endpoint must lie in the region of convergence. (n = 5 and m = 10).

Figure 46

Effects of hard iron and soft iron offsets. (a) Skew matrix, and (b) displacement vector.

Figure 47

X Coil 3-degree polyfit IMU offsets.

Figure 48

(a) Y coil 3-degree polyfit IMU offsets. (b) Z coil 3-degree polyfit IMU offsets.

Figure 49

Motion sensor calibration tool shows hard iron offsets.

Figure 50

Negligible hysteresis error in temperature offset. (a) x axis magnetic field offsets as a function of temperature increasing (blue) and decreasing (red); (b) y axis magnetic field offsets; (c) z axis magnetic field offsets.

Figure 51

Magnetic flux due to changes in temperature with a three-degree polyfit.