Process Systems Enaineerina Model Predictive Control with Linear Models Kenneth R. Muske and James B. Rawlings Dept. of Chemical Engineering, University of Texas at Austin, Austin, TX 78712 This article discusse
ProcessSystemsEnaineerinaModelPredictiveControlLinearModelsKennethRawlingsDept.ChemicalEngineering,UniversityAustin,Austin,TX78712articlediscussesexistinglinearmodelpredictivecontrolconceptsunifiedtheoreticalframeworkbasedstabilizing,infinitehorizon,linearquad-raticregulator.representunstablestablemultivariablesystems,standardstate-spac...
Model Predictive Control (MPC) predicts and optimizes time-varying processes over a future time horizon. This control package accepts linear or nonlinear models. Using large-scale nonlinear programming solvers such as APOPT and IPOPT, it solves data reconciliation, moving horizon estimation, real-time...
Chen WH,Hu XB.Model predictive control of linear systems with nonlinearterminal control. International Journal of Robust and Nonlinear Control . 2004Chen W H,Hu X B.Model predictive control of linear systems with nonlinear terminal control.International Journal of Robust and Nonlinear Control. 2004...
The purpose of this section is to provide a tutorial overview of potential strategies for control of nonlinear systems with linear models. A following section relates methods to implement dynamic control with nonlinear models. ExerciseObjective: Design a model predictive controller for an overhead ...
The proposed controller is tested in a co-simulation environment using FEM element model for the strip integrated in Hotint together with a highly nonlinear roll gap model. The simulation results show the excellent accordance of the controlled infeed process for both models (ODE and FEM), as ...
Linear model predictive control Nowadays in the research literature MPC is formulated almost always in the state space. The system to be controlled is described by a linear discrete time model.xk+1=Axk+Buk,x0=x0,where x(k)∈Rn and u(k)∈Rm denote the state and control input, respectivel...
A model predictive controller uses linear plant, disturbance, and noise models to estimate the controller state and predict future plant outputs. Using the predicted plant outputs, the controller solves a quadratic programming optimization problem to determine control moves. ...
Model Predictive Control Toolbox™ provides functions, an app, Simulink® blocks, and reference examples for developing model predictive control (MPC). For linear problems, the toolbox supports the design of implicit, explicit, adaptive, and gain-scheduled MPC. For nonlinear problems, you can ...
Model predictive control with linear models. AIChE J., 39(2):262–287, 1993. [21] G. Pannocchia and J. B. Rawlings. Disturbance models for offset-free model-predictive control. AIChE J., 49(2):426–437, 2003. [22] S. J. Qin and T. A. Badgwell. A survey of industrial model...