Stochastic tube MPC with state estimation

详细信息	查看全文 | 推荐本文 |

• 作者：Mark Cannon^a ; ¹ ; ^{mark.cannon@eng.ox.ac.uk}  ; [Author Vitae] ; Qifeng Cheng^a ; ^{qifeng.cheng@st-hughs.ox.ac.uk}  ; [Author Vitae] ; Basil Kouvaritakis^a ; ^{basil.kouvaritakis@eng.ox.ac.uk}  ; [Author Vitae] ; Sa&scaron ; a V. Rakovi膰^b ; ^{svr@sasavrakovic.com}  ; [Author Vitae]
• 关键词：Model predictive control ; Output feedback ; State estimation ; Constrained control ; Stochastic systems
• 刊名：Automatica
• 出版年：2012
• 期刊代码：13_00051098
• 类别：cp
• 出版时间：March, 2012
• 卷：48
• 期：3
• 页码：536-541
• 文件大小：476 K

摘要

An output feedback Model Predictive Control (MPC) strategy for linear systems with additive stochastic disturbances and probabilistic constraints is proposed. Given the probability distributions of the disturbance input, the measurement noise and the initial state estimation error, the distributions of future realizations of the constrained variables are predicted using the dynamics of the plant and a linear state estimator. From these distributions, a set of deterministic constraints is computed for the predictions of a nominal model. The constraints are incorporated in a receding horizon optimization of an expected quadratic cost, which is formulated as a quadratic program. The constraints are constructed so as to provide a guarantee of recursive feasibility, and the closed loop system is stable in a mean-square sense. All uncertainties in this paper are taken to be bounded鈥攊n most control applications this gives a more realistic representation of process and measurement noise than the more traditional Gaussian assumption.