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This paper addresses the problem of singularity avoidance in a cluster of four Single-Gimbal Control Moment Gyroscopes (SGCMGs) in a pyramid configuration when used for the attitude control of a satellite by introducing a new gimballed control moment gyroscope (GCMG) cluster scheme. Four SGCMGs were used in a pyramid configuration, along with an additional small and simple stepper motor that was used to gimbal the full cluster around its vertical (z) axis. Contrary to the use of four variable-speed control moment gyroscopes (VSCMGs), where eight degrees of freedom are available for singularity avoidance, the proposed GCMG design uses only five degrees of freedom (DoFs), and a modified steering law was designed for the new setup. The proposed design offers the advantages of SGCMGs, such as a low weight, size, and reduced complexity, with the additional benefit of overcoming the internal elliptic singularities, which create a minor attitude error. A comparison with the four-VSCMG cluster was conducted through numerical simulations, and the results indicated that the GCMG design was considerably more efficient in terms of power while achieving a better gimbal configuration at the end of the simulation, which is essential when it is desired for different manoeuvres to be consecutively executed. Additionally, for a nano-satellite of a few kilograms, the results prove that it is feasible to manufacture the GCMG concept by using affordable and lightweight commercial off-the-shelf (COTS) stepper motors.

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This paper addresses the problem of singularity avoidance for a 4-Control Moment Gyroscope (CMG) pyramid cluster, as used for the attitude control of a satellite using machine learning (ML) techniques. A data-set, generated using a heuristic algorithm, relates the initial gimbal configuration and the desired maneuver—inputs—to a number of null space motions the gimbals have to execute—output. Two ML techniques—Deep Neural Network (DNN) and Random Forest Classifier (RFC)—are utilized to predict the required null motion for trajectories that are not included in the training set. The principal advantage of this approach is the exploitation of global information gathered from the whole maneuver compared to conventional steering laws that consider only some local information, near the current gimbal configuration for optimization and are prone to local extrema. The data-set generation and the predictions of the ML systems can be made offline, so no further calculations are needed on board, providing the possibility to inspect the way the system responds to any commanded maneuver before its execution. The RFC technique demonstrates enhanced accuracy for the test data compared to the DNN, validating that it is possible to correctly predict the null motion even for maneuvers that are not included in the training data.

In this paper, a low-cost, miniature spacecraft attitude control simulator is presented for testing miniature actuators such as Nano Control Moment Gyroscopes (CMGs) for simple maneuvers. The experimental setup is composed by an attitude control system (ACS) that mainly consists of a four-CMG cluster in a pyramid configuration and a custom-made air bearing. The one-degree-of-freedom (DoF) air bearing is fabricated to reproduce the frictionless conditions of a nano-satellite in orbit. The ACS is made exclusively using low-cost commercial-off-the-shelf (COTS) components, whilst the air bearing is made using 3D-printed parts. Both hardware and software implementations are described in detail and the performance of the developed simulator is evaluated by two maneuver experiments. Despite the manufacturing imperfections, the ACS is capable of providing higher angular velocities than previously presented in the literature while following the theoretical or simulation data. The results indicate that it is possible to manufacture a low-cost, miniature actuator such as a CMG, using COTS components to demonstrate the operation of an agile nano-satellite. Any deviations from the theoretical values are addressed and several improvements are discussed to further enhance the performance of the air bearing testing platform.