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.



  • Vishnu Desaraju, Research Scientist, Toyota Research Institute

  • Alexander Spitzer, PhD Student, Carnegie Mellon University

  • Nathan Michael, Associate Research Professor, Carnegie Mellon University



Experience-driven predictive control with robust constraint satisfaction under time-varying state uncertainty VR Desaraju, AE Spitzer, N Michael Robotics: Science and Systems Conference (RSS), July 2017