Research Area

Uncertainty Synthesis for Trusted Autonomy

Control algorithms with provable guarantees for systems that learn - from Mars powered descent to autonomous vehicles

5 Selected Projects

14 Selected Publications

The Uncertainty Synthesis for Trusted Autonomy research area focuses on developing formal methods, theoretical frameworks, and algorithms that enable autonomous systems with learning-enabled components to operate reliably and safely under uncertainty. Our work bridges control theory, machine learning, and formal verification to create trustworthy autonomous systems.

Our core research addresses safety assurances for learning-enabled components, optimal control under parameter uncertainty, stochastic optimal control for uncertain dynamics, and formal verification of autonomous systems. We develop statistical and probabilistic methods for learning algorithmic controllers, sensitivity analysis for robust control, and recovery methods for systems that encounter unexpected conditions.

Applications include aviation autonomy, powered descent guidance for spacecraft landing, autonomous vehicles with AI components, and robust recovery mechanisms. Our work emphasizes both theoretical guarantees and practical validation, with close collaboration with aerospace and automotive partners to transition technologies to real-world systems.

Sponsors

AFOSR
NASA
NSF

Selected Projects

Optimal Covariance Steering on Hypercomplex Number Lie Groups for Astrodynamics Applications

This project extends optimal covariance steering from Euclidean spaces to the hypercomplex number Lie groups on which rigid-body motion naturally evolves, with application to spacecraft attitude and relative motion problems

Control-Theoretic Framework for Reliable and Robust Deep Neural Networks with Application to Space CPS

This project formulates deep neural networks as dynamical systems that shape distributions of data, so that learning-enabled components can be certified and flown on board a spacecraft

Safe Aviation Autonomy with Learning-Enabled Components in the Loop: From Formal Assurances to Trusted Recovery Methods

This project aims to develop safe, trustworthy, and robust machine learning methods for future autonomous aviation systems by incorporating formal assurances and trusted recovery methods

Learning Optimal Control using Forward-Backward SDEs

This project establishes a framework for learning optimal control in systems with highly nonlinear, underactuated dynamics by solving a system of decoupled forward and backward stochastic differential equations (FBSDEs)

Stochastic Optimal Control for Powered Descent Guidance

This research applies stochastic optimal control theory to the powered descent guidance problem for spacecraft landing, aiming to guarantee constraint satisfaction and optimize fuel consumption under uncertainty

Selected Publications

Sivaramakrishnan, V., Pilipovsky, J., Oishi, M., and Tsiotras, P., “Distribution Steering for Discrete-Time Linear Systems with General Disturbances using Characteristic Functions,” American Control Conference, Atlanta, GA, June 8-10 , 2022.

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Yin, J., Zhang, Z., Theodorou, E., and Tsiotras, P., “Trajectory Distribution Control for Model Predictive Path Integral Control using Covariance Steering,” International Conference on Robotics and Automation, Philadelphia, PA, May 23-27, 2022.

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Pakniyat, A., and Tsiotras, P., “Partially Observed Steering the State of Linear Stochastic Systems,” 60th IEEE Conference on Decision and Control, Austin, TX, Dec.13-15, 2021, pp. 3780-3785.

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Pakniyat, A., and Tsiotras, P., “Steering the State of Linear Stochastic Systems: A Constrained Minimum Principle Formulation,” American Control Conference, New Orleans, LA, May 26-28, 2021.

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Pilipovsky, J. and Tsiotras, P., “Chance-Constrained Optimal Covariance Steering with Iterative Risk Allocation,” American Control Conference, New Orleans, LA, May 26-28, 2021, pp. 2011-2016.

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Pilipovsky, J., and Tsiotras, P. “Covariance Steering with Optimal Risk Allocation,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 57, No. 6, pp. 3719-3733, 2021.

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Ridderhof, J., Okamoto, K., and Tsiotras, P., “Chance Constrained Covariance Control for Linear Stochastic Systems With Output Feedback,” 59th IEEE Conference on Decision and Control, Jeju Island, South Korea, Dec. 8-11, 2020.

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Ridderhof, J., and Tsiotras, P., “Stochastic Atmosphere Modeling for Risk-Adverse Aerocapture Guidance,” IEEE Aerospace Conference, Big Sky, Montana, March 7-14, 2020.

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Makkapati, V. R., Sarabu, H., Comandur, V., Tsiotras, P., and Hutchinson, S., “Safe Optimal Control under Parametric Uncertainties,” IEEE Robotics and Automation Letters, Vol. 5, No. 4, pp. 5725-5731, 2019.

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10.

Okamoto, K. and Tsiotras, P., “Input Hard Constrained Optimal Covariance Steering,” 58th IEEE Conference on Decision and Control, Nice, France, Dec. 11-13, 2019.

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11.

Okamoto, K. and Tsiotras, P., “Optimal Stochastic Vehicle Path Planning Using Covariance Steering,” IEEE Robotics and Automation Letters, Vol. 4, No. 3, pp. 2276-2281, 2019.

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12.

Ridderhof, J., Okamoto, K. and Tsiotras, P., “Nonlinear Uncertainty Control with Iterative Covariance Steering,” 58th IEEE Conference on Decision and Control, Nice, France, Dec. 11-13, 2019.

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13.

Ridderhof, J., and Tsiotras, P., “Minimum-fuel Powered Descent in the Presence of Random Disturbances,” AIAA Guidance, Navigation, and Control Conference, San Diego, CA, Jan. 7-11, 2019 (Best Student Paper Award).

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14.

Ridderhof, J., and Tsiotras, P., “Uncertainty Quantification and Control During Mars Powered Descent and Landing using Covariance Steering,” AIAA Guidance, Navigation, and Control Conference, Kissimmee, FL, Jan. 8-12, 2018.

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