Information Theoretic Model Predictive Control on Jump Diffusion Processes

TL;DR

This paper introduces an information theoretic model predictive control method for systems affected by jump diffusion processes with compound Poisson noise, enabling real-time control through GPU acceleration.

Contribution

It extends previous path integral control methods to handle discontinuous jump processes and develops a parallelizable MPC algorithm using importance sampling.

Findings

Improved control performance demonstrated in simulations.

Real-time implementation on GPU is feasible.

Highlights the importance of modeling stochastic disturbance characteristics.

Abstract

In this paper we present an information theoretic approach to stochastic optimal control problems for systems with compound Poisson noise. We generalize previous work on information theoretic path integral control to discontinuous dynamics with compound Poisson noise and develop an iterative model predictive control (MPC) algorithm using importance sampling. The proposed algorithm is parallelizable and when implemented on a Graphical Processing Unit (GPU) can run in real time. We test the performance of the proposed algorithm in simulation for two control tasks using a cartpole system and a quadrotor. Our simulations demonstrate improved performance of the new scheme and indicate the importance of incorporating the statistical characteristics of stochastic disturbances in the computation of the stochastic optimal control policies.