Journal
COMPUTERS & CHEMICAL ENGINEERING
Volume 178, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2023.108381
Keywords
Distributed parameter systems; Hyperbolic partial differential equations; Model predictive control; Moving horizon estimation; Pipelines
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This manuscript proposes moving horizon control and state/parameter estimation designs for pipeline networks modeled by partial differential equations (PDEs) with boundary actuation. The effectiveness of the proposed controller and estimator designs is demonstrated via numerical examples.
This manuscript proposes moving horizon control and state/parameter estimation designs for pipeline networks modelled by partial differential equations (PDEs) with boundary actuation. The spatial-temporal pressure and velocity dynamics within the pipelines are described by a system of six coupled one-dimensional first-order nonlinear hyperbolic PDEs. To address the discrete-time modelling challenge and preserve the infinite-dimensional nature of the pipeline system, the Cayley-Tustin transformation is deployed for model time discretization without any spatial discretization or model reduction. Considering the lack of full state information across the entire pipeline manifold, unknown states and uncertain parameters are estimated using moving horizon estimation (MHE). Based on the estimated states and parameters, a tracking model predictive control (MPC) strategy for the discrete-time infinite-dimensional pipeline system is proposed, which enables specific operation while ensuring physical constraint satisfaction. The effectiveness of the proposed controller and estimator designs is demonstrated via numerical examples.
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