Nonlinear and Robust Control of PDE Systems: Methods and Applications to Transport-Reaction Processes

By Panagiotis D. Christofides, J. Chow. 2002

724 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/c9b163c07a23addfa04b4070ba439df95f6682b6

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In Control and Systems Engineering

Citations in the corpus

Linear Model Predictive Control for Transport-Reaction Processes (2017, neutral): “0 1 v zðg; 0Þe21v Ð g 0 ðw2sIÞd/dg e 1 v Ð f 0 ðw2sIÞd/ (11)”
Finite-dimensional control of nonlinear parabolic PDE systems with time-dependent spatial domains using empirical eigenfunctions (2001, methodology): “…estimators using stochas - tic methods in ( Ray and Seinfeld , 1975 ) , and the design of nonlinear and robust controllers on the basis of finite - dimensional models obtained using a combination of Galerkin ' s method with approximate inertial manifolds ( Armaou and Christofides , 1999 ; 2001 ) .”
Recent Advances on Fuzzy-Model-Based Nonlinear Networked Control Systems: A Survey (2016, neutral): “partial differential equations [132], [133].”
Integrated optimal actuator/sensor placement and robust control of uncertain transport-reaction processes (2002, background): “Keywords: Robust nonlinear control; Static output feedback control; Optimal actuator/sensor placement; Diffusion–convection-reaction processes”
A spatio-temporal Volterra modeling approach for a class of distributed industrial processes (2009, methodology): “3 is a typical transport-reaction process in the chemical industry [11].”
Fuzzy-Model-Based Reliable Static Output Feedback $\mathscr{H}_{\infty }$ Control of Nonlinear Hyperbolic PDE Systems (2016, methodology): “Thus, a typical method to control of parabolic PDE systems is to use Galerkin’s approach to the PDE system to derive an approximate ODE model as the basis for the controller design [16], [17], [20], [21].”
On Iterative Learning Control of parabolic distributed parameter systems (2009, background): “Many infinite-dimensional dynamical systems can be described by parabolic PDEs, including processes such as heat and mass transfer, convection, diffusion, and transport (see, e.g., [10], [ 11 ] for more examples).”
A tutorial review of economic model predictive control methods (2014, background): “Almost all of the work on control of distributed parameter systems modeled by PDEs has focused on steady-state stabilization and operation [30] especially in the context of predictive control formulated for PDE systems (e.”
A robust multi-model predictive controller for distributed parameter systems (2012, background): “The dissipative nature of the kind of systems considered in this work allows us to separate the system dynamics in two subsets [67, 68, 31]: one stable with fast dynamics and infinite dimensional (zB) and the other (zA) composed by a finite number of elements with slow dynamics which may be unstable”
Predictive control of parabolic PDEs with boundary control actuation (2006, methodology): “, in-domain control actuation which typically results in control formulations where the manipulated inputs enter directly into the PDE)—and other highly dissipative PDE systems that arise in the modeling of spatially distributed processes—on the basis of nonlinear low-order ODE models derived throug”
Control oriented 1D electrochemical model of lithium ion battery (2007, methodology): “Model order reduction techniques, performed in the time [ 16 ,17] or frequency [9] domain, offer potential to reduce further the model order and avoid restrictive assumptions.”