Distributed Stochastic MPC of Linear Systems With Additive Uncertainty and Coupled Probabilistic Constraints
- 22 September 2016
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Automatic Control
- Vol. 62 (7), 3474-3481
- https://doi.org/10.1109/tac.2016.2612822
Abstract
This technical note develops a new form of distributed stochastic model predictive control (DSMPC) algorithm for a group of linear stochastic subsystems subject to additive uncertainty and coupled probabilistic constraints. We provide an appropriate way to design the DSMPC algorithm by extending a centralized SMPC (CSMPC) scheme. To achieve the satisfaction of coupled probabilistic constraints in a distributed manner, only one subsystem is permitted to optimize at each time step. In addition, by making explicit use of the probabilistic distribution of the uncertainties, probabilistic constraints are converted into a set of deterministic constraints for the predictions of nominal models. The distributed controller can achieve recursive feasibility and ensure closed-loop stability for any choice of update sequence. Numerical examples illustrate the efficacy of the algorithm.Keywords
Funding Information
- National Basic Research Program of China (973 Program) (2012CB720000)
- National Natural Science Foundation of China (61603041, 61225015, 61105092, 61422102)
- Beijing Natural Science Foundation (4161001)
- National Natural Science Foundation of China (61321002)
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