Predictive control of uncertain nonlinear parabolic PDE systems using a Galerkin/neural-network-based model

DOI10.1016/j.cnsns.2011.05.019zbMath1239.93039OpenAlexW2026330468MaRDI QIDQ430282

Mohammad Javad Yazdanpanah, Ghazal Montaseri

Publication date: 21 June 2012

Published in: Communications in Nonlinear Science and Numerical Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cnsns.2011.05.019

zbMATH Keywords

Galerkin methodneural networksmodel predictive controltransport-reaction processes

Mathematics Subject Classification ID

Related Items (3)

Robust adaptive PID controller for a class of uncertain nonlinear systems: an application for speed tracking control of an SI engine ⋮ Nonparametric identification based on Gaussian process regression for distributed parameter systems ⋮ Optimal combination of spatial basis functions for the model reduction of nonlinear distributed parameter systems

Uses Software

• Matlab
• Neural Network Toolbox

Cites Work

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• A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability
• Finite-dimensional control of parabolic PDE systems using approximate inertial manifolds
• Approximation by neural networks with a bounded number of nodes at each level
• Analysis and control of parabolic PDE systems with input constraints.
• Receding horizon control of nonlinear systems
• Predictive control of parabolic PDEs with state and control constraints
• Feedback control of linear diffusion processes
• Nonlinear Model Predictive Control: An Introductory Review

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