학술논문

Robust model predictive control through adjustable variables: an application to path planning
Document Type
Conference
Source
2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601) Decision and control Decision and Control, 2004. CDC. 43rd IEEE Conference on. 3:2485-2490 Vol.3 2004
Subject
Robotics and Control Systems
Computing and Processing
Robust control
Predictive models
Predictive control
Path planning
Robustness
Uncertainty
Optimization methods
Control system synthesis
Control systems
Constraint optimization
Language
ISSN
0191-2216
Abstract
Robustness in model predictive control (MPC) is the main focus of this work. After a definition of the conceptual framework and of the problem's setting, we analyze how a technique developed for studying robustness in convex optimization can be applied to address the problem of robustness in the MPC case. Therefore, exploiting this relationship between control and optimization, we tackle robustness issues for the first setting through methods developed in the second framework. Proofs for our results are included. As an application of this robust MPC result, we consider a path planning problem and discuss some simulations thereabout.