Recently we have been working on a
European Space Agency (ESA) project called "On-line Reconfiguration Control System and Avionics Technologies" or "ORCSAT". Our role, in collaboration with the
control group at the University of Cambridge, has been to design a Model Predictive Control (MPC) system for spacecraft rendezvous. The scenario is the rendezvous in Mars orbit with a sample canister, launched from the Martian surface and to be transported back to Earth for analysis. Loosely speaking, we're trying to catch a basketball from 50km away...
MPC works by using a trajectory optimizer to design the manoeuvres in real time to use minimum propellant. The challenge is to design the optimizer to balance complexity with speed. Too detailed, and the optimization takes too long to solve: too approximate, and the manoeuvre doesn't go where predicted and you burn too much fuel. The figure on the right shows an example of the resulting trajectory. Bristol researcher Paul Trodden developed the far-term MPC that gets us to a similar orbit to the target and close enough to track it. Then Cambridge researcher Ed Hartley's controllers take over to "hop" closer to the target and finally intercept it. Initial results suggest that MPC is very efficient, reducing the fuel needed to accomplish the rendezvous.
Our collaborator
Alberto Bemporad from the University of Trento developed MPC software to enable all these controllers to share the same optimizer. Also in the consortium are
GMV in Spain, who provided a rendezvous simulator,
Reliacon of the Netherlands, who are verifying the controllers, and
Thales Alenia Space Italy (TAS-I), who lead the project. TAS-I and GMV are now implementing and testing the MPCs as the project continues.
Abstract This article presents a new form of robust distributed model predictive control (MPC) for multiple dynamically decoupled subsystems, in which distributed control agents exchange plans to achieve satisfaction of coupling constraints. The new method offers greater flexibility in communications than existing robust methods, and relaxes restrictions on the order in which distributed computations are performed. The local controllers use the concept of tube MPC - in which an optimisation designs a tube for the system to follow rather than a trajectory - to achieve robust feasibility and stability despite the presence of persistent, bounded disturbances. A methodical exploration of the trades between performance and communication is provided by numerical simulations of an example scenario. It is shown that at low levels of inter-agent communication, distributed MPC can obtain a lower closed-loop cost than that obtained by a centralised implementation. A further example shows that the flexibility in communications means the new algorithm has a relatively low susceptibility to the adverse effects of delays in computation and communication.