Robust distributed model predictive control for uncertain networked control systems

Langwen Zhang; Jingcheng Wang; Yang Ge; Bohui Wang

Robust distributed model predictive control for uncertain networked control systems

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Author(s): Langwen Zhang ¹ ; Jingcheng Wang ¹ ; Yang Ge ¹ ; Bohui Wang ¹
- Affiliations: 1: Department of Automation, Shanghai JiaoTong University and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, People's Republic of China
Source: Volume 8, Issue 17, 20 November 2014, p. 1843 – 1851
DOI: 10.1049/iet-cta.2014.0311 , Print ISSN 1751-8644, Online ISSN 1751-8652

Received 27/03/2014, Accepted 14/07/2014, Revised 28/06/2014, Published 14/08/2014

In this study, an approach to design robust distributed model predictive control (MPC) is proposed for polytopic uncertain networked control systems with time delays. To reduce the computational complexity and improve the flexibility, the entire system is decomposed into multiple smaller dimensional subsystems. For each subsystem, the proposed robust distributed MPC algorithm requires solving multiple linear matrix inequality optimisation problems to minimise an upper bound on a robust performance objective. An augmented polytopic uncertainty description is invoked to handle the input delays. The conservativeness of distributed MPC algorithm is reduced by utilising a sequence of feedback control laws. An iterative on-line algorithm for robust distributed MPC is developed to coordinate the distributed MPC controllers. Convergence and robust stability of the proposed distributed MPC are investigated. A numerical example is carried out to demonstrate the effectiveness of the proposed algorithm.

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