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Publication details

State Estimation and Model Predictive Control for the Systems with Uniform Noise

Conference Paper (international conference)

Pavelková Lenka, Belda Květoslav


serial: IFAC-PapersOnLine. Volume 49, Issue 7 - 11th IFAC Symposium on Dynamics and Control of Process SystemsIncluding Biosystems DYCOPS-CAB 2016, p. 967-972

action: IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS-CAB) /11./, (Trondheim, NO, 20160606)

project(s): GA16-09848S, GA AV ČR

keywords: model predictive control, bounded noise, probabilistic models, linear state-space models

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abstract (eng):

This paper concerns the model predictive control applied to the systems with bounded uncertainties. These systems are described by a state-space model with uniformly distributed states and outputs with unknown bounds of respective distributions. The model matrices are assumed to be known. The approximate estimation of states and noise bounds is based on the Bayesian approach. A state-space generalised predictive control is selected as a suitable target model predictive control strategy. The proposed concept of the above mentioned estimation within generalised predictive control is illustrated by representative comparative simulation examples.

RIV: BC

2012-12-21 16:10