Research Projects

In this project, the ControlX team with ERAU partnership will develop agile, reconfigurable, and resilient dynamics and G&C algorithms for on-orbit servicing to capture a broad set of OSAM applications such as remediation of resident space object (RSO) (e.g., via de-orbiting, recycle, end-of-life servicing, satellite refueling, etc.) using effective tools and methods involving geometric mechanics, constrained G&C synthesis, and reconfigurable robotic manipulators (RRMs). 

Dynamic response to emergent situations is a necessity in the on-orbit servicing, assembly and manufacturing (OSAM) field because traditional on-orbit guidance and control (G&C) cannot respond efficiently and effectively to such dynamic situations (i.e., they are based on either constant mass or diagonal matrix of inertia). In these circumstances, the current challenge is to develop modeling strategies and control systems that exhibit intelligence, robustness and adaptation to the environment changes and disturbances (e.g., uncertainties, constraints and flexible dynamics). Note that the current state-of-the-art methods do not offer a reliable, accurate framework for real-time, optimal accommodation of constraints in the system dynamics that account for orbital-attitude coupling in the motion of the bodies without encountering singularity or non-uniqueness issues. In this project, the ControlX team with ERAU partnership will develop agile, reconfigurable, and resilient dynamics and G&C algorithms for on-orbit servicing to capture a broad set of OSAM applications such as remediation of resident space object (RSO) (e.g., via de-orbiting, recycle, end-of-life servicing, satellite refueling, etc.) using effective tools and methods involving geometric mechanics, constrained G&C synthesis, and reconfigurable robotic manipulators (RRMs). 

The proposed work in this Phase I includes reconfigurable systems for on-orbit servicing, assembly and manufacturing with learning control methods that minimize tracking error of the end-effector of the RRM in the presence of uncertainties, optimize configuration and accommodate constraint-changing scenarios. Our developments will avoid singularity; not rely on the concept of costates or Lagrange multipliers that are restrictive; handle system uncertainties while enforcing the constraints; use RRMs in different tasks (recycle, debris removing, maintenance, etc.); not need in sensors or exact model knowledge for robotic arms. Specifically, constrained space vehicle control (predicated on Udwadia and Kalaba (UK) formalism) will offer not only accurate and resilient design but also reconfigurability in that G&C algorithms can easily be modified to suit a wide spectrum of OSAM applications. ControlX team will also consider the feasibility of hardware implementation. The selection of sensors, actuators and onboard computers will be an important trade-off between among size, weight, and power (SWaP) constraints, reliability, and computing performance. A real-time operating system (RTOS) is planned to meet timing and memory management constraints, partitioning hardware resources to control software application interactions. An analysis suite for the autonomous system implementation, based on system modeling and learning techniques, will provide onboard analysis, enabling decision-making as to whether the system can continue to meet mission requirements.