### Abstract

Original language | English |
---|---|

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 |

### Fingerprint

### Cite this

*Proceedings of the 2nd IFAC Workshop on Estimation and Control of Networked Systems*(pp. 7-12). France, Annecy.

}

*Proceedings of the 2nd IFAC Workshop on Estimation and Control of Networked Systems.*France, Annecy, pp. 7-12.

**A model predictive control approach for stochastic networked control systems.** / Bernardini, D.; Donkers, M.C.F.; Bemporad, A.; Heemels, W.P.M.H.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - A model predictive control approach for stochastic networked control systems

AU - Bernardini, D.

AU - Donkers, M.C.F.

AU - Bemporad, A.

AU - Heemels, W.P.M.H.

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

M3 - Conference contribution

SP - 7

EP - 12

BT - Proceedings of the 2nd IFAC Workshop on Estimation and Control of Networked Systems

CY - France, Annecy

ER -