TPF-I SWG Report - Exoplanet Exploration Program - NASA

F ORMATION F LYING A L G O R I T H M D E V E L O P M E N T
standalone operational mode to allow for safe operations. Similarly, attitude knowledge loss of partner’s
attitude, while still having range/bearing knowledge, will result in precautionary safing of self while still
maintaining the formation operational mode.
At any time, while in either cluster or separated configuration, loss of spacecraft attitude knowledge will
result in an attempt to regain inertial knowledge by nulling any residual attitude rates and acquisition of
celestial and inertial attitude sensors.
Control Mapper
The control allocator takes the requested force and torque from the controller and generates low-level
commands to the thrusters and/or reaction wheels. Forces can only be implemented by the thrusters.
Torques can be implemented by thrusters, reaction wheels, or a combination of both. The current FACS
control allocator allows torques to be implemented by only thrusters or only reaction wheels. This
limitation is more operational, and if blended thruster/reaction wheel torques are desired (e.g., in the event
of multiple reaction wheel or thruster failures), the FACS control allocator could be extended. If torques are
generated by reaction wheels, the commanded torques are simply passed to the reaction wheels. As such,
the principal FACS control allocator algorithm takes forces and torques to thruster on-times.
The thruster allocation problem is formulated as a convex optimization problem by minimizing a cost
function consisting of force and torque errors and a weighted sum of thruster on-times. The latter creates
the convexity of the cost function and is a measure of the fuel consumed. The constraints are that the ontime
for each thruster must be between a high and low value. This constraint is also convex as desired.
Using the problem structure, a gradient descent algorithm is used to solve the constrained optimization
problem. Hence, if the thrust allocation algorithm is interrupted in real-time while it is optimizing, the
current value of thruster on-times in the optimization will be better than the previous value. The gradient
descent starts at the unconstrained solution, which can be determined analytically.
FAST – Distributed Real-Time Simulation
The Formation and Algorithms and Simulation Testbed (FAST) is a hard real-time, distributed simulation
environment for precision formation algorithm design and validation. FAST is built upon several PowerPC
750 flight-like processors running a flight-qualified, real-time OS. (The Mars Reconnaissance Orbiter is
currently flying the radiation-hardened version of the PPC 750.) A ground console is used for commanding
a formation and for processing the telemetry. The dynamics of spacecraft, sensors, actuators, up-links,
down-links, and inter-spacecraft communication are also simulated on distributed processors using the
Hierarchical Distributed Re-configurable Architecture (HYDRA) simulation environment (Martin et al.
2003). In particular, the dynamics of each spacecraft are integrated on separate processors, thereby enabling
a fully scalable, distributed simulation architecture. With the open-architecture HYDRA, FAST can be used
to simulate a five-spacecraft formation in low-Earth orbit or, with the addition of processors, a thirtyspacecraft
formation in deep space. Furthermore, the distributed architecture enforces truly distributed
algorithms and prevents inadvertent data sharing.
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