Design and validation of an MPC controller for CMG-based testbed

In the last years, Control Moment Gyros (CMGs) are widely used for high-speed attitude control, since they are able to generate larger torque compared to “classical” actuation systems, such as Reaction Wheels . This paper describes the attitude control problem of a spacecraft, using a Model Predictive Control method. The features of the considered linear MPC are: (i) a virtual reference, to guarantee input constraints satisfaction, and (ii) an integrator state as a servo compensator, to reduce the steady-state error. Moreover, the real-time implementability is investigated using an experimental testbed with four CMGs in pyramidal configuration, where the capability of attitude control and the optimization solver for embedded systems are focused on. The effectiveness and the performance of the control system are shown in both simulations and experiments.

A Fast Algorithm for Generalized Arc Consistency of the Alldifferent Constraint

The alldifferent constraint is an essential ingredient of most Constraints Satisfaction Problems (CSPs). It has been known that the generalized arc consistency (GAC) of alldifferent constraints can be reduced to the maximum matching problem in a value graph. The redundant edges, which do not appear in any maximum matching of the value graph, can and should be removed from the graph. The existing methods attempt to identify these redundant edges by computing the strongly connected components after finding a maximum matching for the graph. Here, we present a novel theorem for identification of the redundant edges. We show that some of the redundant edges can be immediately detected after finding a maximum matching. Based on this theoretical result, we present an efficient algorithm for processing alldifferent constraints. Experimental results on real problems show that our new algorithm significantly outperforms the-state-of-art approaches.