A Convex Allocation Framework for Singularity Avoidance in Control Moment Gyro Clusters

11/25/2022

In robotics, the occurrence of singularities typically results in a loss of a degree of freedom. For spacecrafts employing control moment gyro clusters, singularities may occur due to the alignment of the gimbals, which inhibits the creation of torque in at least one direction. This translates into a loss of control authority that has a direct impact on the spacecraft’s attitude control system. In this work, an optimal allocation framework for singularity avoidance is proposed. The presented solution aims to provide a singularity robust allocation scheme that can be used as an add-on to a conventional attitude controller. This algorithm resorts to the model predictive control framework to predict the future states of the gimbals and, subsequently, take control actions that lead to singularity-free configurations while minimizing the control energy spent. The use of a redundant actuator makes it possible to avoid singularities while the system meets the torque references defined by the controller. Moreover, a novel, computationally efficient and numerically robust, singularity metric is derived to assess the proximity of a singularity. This function overcomes the complexity of the standard literature solutions, such as the condition number, and can be integrated as a linear constraint in a convex optimization problem. Finally, the proposed approach is applied to a two-dimensional control moment gyro cluster in a simulation environment. It is verified that the system is capable of avoiding all of the internal singularities of the cluster at a relatively low computational expense.

Authors in the community:

• 

  H

  Hugo André Chamusca Pereira

  ist190268

Supervisors of this institution:

• 

  P

  Pedro Tiago Martins Batista

  ist14388

mechanical-engineering - Mechanical engineering

eng - English

Instituto Superior Técnico