Embedded Robust Model Predictive Path Integral Control Using Sensitivity Tubes and GPU Acceleration

Abstract

This paper proposes a method to robustify model predictive path integral (MPPI) control by directly taking into account the effects of parameter uncertainty into the controller formulation. Leveraging the recent notion of closed-loop state sensitivity, the proposed MPPI can consider the state sensitivity against parameter mismatch as a part of the system state, and consequently exploit this additional information to address the challenge of model mismatch in sampling-based model predictive control. Using an obstacle avoidance scenario, we demonstrate the use of our approach to control an aerial robot. We present an embedded implementation of our method, utilizing parallelization of computations on a GPU. Finally, we show the increased robustness of our approach over a standard MPPI controller through hardware-in-the-loop simulations and validate its embedded real-time properties.