Abstract:
An augmented state formulation for multiple model predictive control (MMPC) is developed to improve the regulation of nonlinear and uncertain process systems. By augmenti...Show MoreMetadata
Abstract:
An augmented state formulation for multiple model predictive control (MMPC) is developed to improve the regulation of nonlinear and uncertain process systems. By augmenting disturbances as states that are estimated using a Kalman filter, improved disturbance rejection is achieved compared to an additive output disturbance assumption. The approach is applied to a quadratic tank example, which has challenging dynamic behavior, switching from minimum phase to nonminimum phase behavior as the operating conditions are changed.
Published in: 2007 American Control Conference
Date of Conference: 09-13 July 2007
Date Added to IEEE Xplore: 30 July 2007
ISBN Information: