Experience-driven Predictive Control with Robust Constraint Satisfaction under Time-Varying State Uncertainty

Experience-driven Predictive Control with Robust Constraint Satisfaction under Time-Varying State Uncertainty

We present an extension to Experience-driven Predictive Control (EPC) that leverages a Gaussian belief propagation strategy to compute an uncertainty set bounding the evolution of the system state in the presence of time-varying state uncertainty. This uncertainty set is used to tighten the constraints in the predictive control formulation via a chance constrained approach, thereby providing a probabilistic guarantee of constraint satisfaction. The parameterized form of the controllers produced by EPC coupled with online uncertainty estimates ensures this robust constraint satisfaction property persists even as the system switches controllers and experiences variations in the uncertainty model. We validate the online performance and robust constraint satisfaction of the proposed Robust EPC algorithm through a series of experimental trials with a small quadrotor platform subjected to changes in state estimate quality.

People

Vishnu Desaraju
PhD, 2017. Now at Toyota Research Institute.
Alex Spitzer
PhD, 2021. Now a Research Scientist at the University of Washington
Nathan Michael
Director, 2014-2023. Now at Shield AI.

Robots

Danaus
Danaus