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.
|Title of host publication||Proceedings of the 2nd IFAC Workshop on Estimation and Control of Networked Systems|
|Place of Publication||France, Annecy|
|Publication status||Published - 2010|
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.