Stochastic Model Predictive Control of Autonomous Systems with Non-Gaussian Correlated Uncertainty

TL;DR

This paper introduces a novel stochastic model predictive control framework that effectively manages non-Gaussian correlated uncertainties in autonomous systems, enhancing safety and performance.

Contribution

It develops a new stochastic Galerkin method with specialized basis functions and quadrature for better uncertainty propagation in control problems.

Findings

Successfully handles non-Gaussian correlated uncertainties.

Enables conversion of chance constraints into deterministic problems.

Validated on obstacle avoidance, path following, and quadrotor tracking tasks.

Abstract

Many systems such as autonomous vehicles and quadrotors are subject to parametric uncertainties and external disturbances. These uncertainties can lead to undesired performance degradation and safety issues. Therefore, it is important to design robust control strategies to safely regulate the dynamics of a system. This paper presents a novel framework for chance-constrained stochastic model predictive control of dynamic systems with non-Gaussian correlated probabilistic uncertainties. We develop a new stochastic Galerkin method to propagate the uncertainties using a new type of basis functions and an optimization-based quadrature rule. This formulation can easily handle non-Gaussian correlated uncertainties that are beyond the capability of generalized polynomial chaos expansions. The new stochastic Galerkin formulation enables us to convert a chance-constraint stochastic model predictive control problem into a deterministic one. We verify our approach by several stochastic control tasks, including obstacle avoidance, vehicle path following, and quadrotor reference tracking.