Abstract
In this paper we present a stochastic model predictive control (SMPC) approach for networked control systems (NCSs) that are subject to time-varying sampling intervals and timevarying transmission delays. These network-induced uncertain parameters are assumed to be described by random processes, having a bounded support and an arbitrary continuous probability density function. Assuming that the controlled plant can be modeled as a linear system, we present a SMPC formulation based on scenario enumeration and quadratic programming that optimizes a stochastic performance index and provides closed-loop stability in the mean-square sense. Simulation results are shown to demonstrate the performance of the proposed approach.
| Original language | English |
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| Title of host publication | Proceedings of the 2nd IFAC Workshop on Estimation and Control of Networked Systems |
| Place of Publication | France, Annecy |
| Pages | 7-12 |
| Publication status | Published - 2010 |
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Cite this
Bernardini, D., Donkers, M. C. F., Bemporad, A., & Heemels, W. P. M. H. (2010). A model predictive control approach for stochastic networked control systems. In Proceedings of the 2nd IFAC Workshop on Estimation and Control of Networked Systems (pp. 7-12). France, Annecy.