Assured Autonomous Spacecraft GN&C via Hybrid Control


TECHNOLOGY AREA(S): Space Platforms 

OBJECTIVE: Develop innovative, novel approaches for hybrid mode-logic/discrete-time control/continuous-time physics spacecraft systems that provide mathematically rigorous guarantees of system behavior and performance. 

DESCRIPTION: GN&C systems that involve mode-switching logic or gain-scheduling are both examples of a hybrid system, where the dynamics of a finite-state machine (e.g., the mode-logic or the gain-schedule) interact with the dynamics of a continuous-valued system (e.g., the attitude control system & attitude dynamics). State-of-practice for such systems involves rigorous analysis of both the continuous-valued system and the finite-state machine individually via standard techniques. But analysis of the coupled system comprised of the connected mode-logic and control systems is beyond current analysis approaches - regression testing via Monte-Carlo (MC) analysis is the only option, and does not provide rigorous guarantees. Recently, techniques have been published in the literature that allow rigorous analysis of these coupled system behaviors, and in some cases can design coupled mode-logic/control systems with rigorous guarantees for stability and robustness. This SBIR will apply these techniques to missions of AF interest and demonstrate the utility of the resulting set calculations. Techniques of interest include but are not limited to analysis approaches (e.g., linear-temporal logic specification verification via system approximation, bisimulation, model-checking) as well as synthesis approaches (e.g., guaranteed performance via composition or other proofs). 

PHASE I: Perform literature survey of methods. Working with sponsor, identify appropriate application example(s) & metrics that methods will be evaluated against. Perform trade study to down-select to one or two algorithms that will form the basis of a Phase-II effort. Document results in a Final Report, and deliver any simulation software to the sponsor. 

PHASE II: Develop selected algorithms into suitable software capable of producing results in practical time periods. Develop high-fidelity simulation of application of AF interest with sponsor. Integrate algorithms with simulation to demonstrate utility against metrics. Working with sponsor, examine opportunities for transition of technology to customers of interest. 

PHASE III: Develop and apply algorithms to specific system of interest to sponsor. Work with sponsor, customer, and associated contractors to integrate algorithms and software into application of interest. 


1. S. Karaman, R. Sanfelice, and E. Frazzoli, Optimal control of mixed logical dynamical systems with linear temporal logic specifications, in Decision and Control, 2008. CDC 2008. 47th IEEE Conference on, Dec 2008, pp. 2117“2122.

2. S. Di Cairano, W. Heemels, M. Lazar, and A. Bemporad, Stabilizing dynamic controllers for hybrid systems: A hybrid control lyapunov function approach, Automatic Control, IEEE Transactions on, vol. 59, no. 10, pp. 2629“2643, Oct 2014.

3. Tabuada, Paulo. Verification and control of hybrid systems: a symbolic approach. Springer Science & Business Media, 2009.

4. Duggirala, Parasara Sridhar, Mitra, Sayan, and Viswanathan, Mahesh, "C2E2: A Verification Tool For Stateflow Models," In Proceedings of 21st International Conference Tools and Algorithms for the Construction and Analysis of Systems (TACAS) 2015, London

5. Liu, Jun, et al. "Synthesis of reactive switching protocols from temporal logic specifications." Automatic Control, IEEE Transactions on 58.7 (2013): 1771-1785.


KEYWORDS: Hybrid, Cyber-physical, Verification, Validation, Control, Guidance, Attitude, Orbital 

US Flag An Official Website of the United States Government