Stochastic Model Predictive Control for Constrained Linear Systems Using Optimal Covariance Steering

• DOI: 10.48550/arxiv.1905.13296
• Publication Date: 2019-01-01
• Authors: Kazuhide Okamoto; Panagiotis Tsiotras
• Keywords: Model Predictive Control; Stochastic control

This work develops a stochastic model predictive controller~(SMPC) for uncertain linear systems with additive Gaussian noise subject to state and control constraints. The proposed approach is based on the recently developed finite-horizon optimal covariance steering theory, which steers mean of system prescribed target values at given terminal time. We call our steering-based SMPC, or CS-SMPC. show that has several advantages over traditional SMPC approaches in literature. Specifically, it shown newly algorithm can deal unbounded while ensuring stability recursive feasibility, incurs lower computational cost than previous similar approaches. effectiveness CS-SMPC confirmed using numerical simulations.