A Probabilistic Particle-Control Approximation of Chance-Constrained Stochastic Predictive Control

—Robotic systems need to be able to plan control actions that are robust to the inherent uncertainty in the real world. This uncertainty arises due to uncertain state estimation, disturbances and modeling errors, as well as stochastic mode transitions such as component failures. Chance constrained control takes into account uncertainty to ensure that the probability of failure, due to collision with obstacles, for example, is below a given threshold. In this paper we present a novel method for chance constrained predictive stochastic control of dynamic systems. The method approximates the distribution of the system state using a finite number of particles. By expressing these particles in terms of the control variables, we are able to approximate the original stochastic control problem as a deterministic one; furthermore the approximation becomes exact as the number of particles tends to infinity. This method applies to arbitrary noise distributions, and for systems with linear or ...

• Type: Journal
• Year: 2010
• Where: TROB
• Authors: Lars Blackmore, Masahiro Ono, Askar Bektassov, Brian C. Williams
• Keywords: Mixed Integer Linear Programming, Mode Transitions, Stochastic Control, TROB 2010