Control Allocation and Parameter Estimation for Space Manipulation Using Multiple Underactuated Spacecraft

Multiple underactuated spacecraft agents can cooperatively manipulate a large target object as a single overactuated system with multiple thrusters and reaction wheels, offering very large workspaces with enhanced robustness and dexterity compared to a single space manipulator. This paper proposes a control allocation method for optimally generating the required net forces and moments, taking into account actuator limits, as well as a distributed parameter estimation method for determining the relative agent positions and angular states using only local noise-corrupted measurements. The control allocation problem is solved via quadratic programming, while each agent performs parameter estimation using a Kalman filter for position and an extended Kalman filter for the angular states. The proposed methods for control allocation and parameter estimation are verified through Monte Carlo simulations.