This title is available electronically through the IET Digital Library
Book title: Robust and Adaptive Model Predictive Control of Nonlinear Systems
Author: Martin Guay, Veronica Adetola and Darryl DeHaan
Product Code: PBCE0830
Stock Status: In stock
Most physical systems have uncertain or unmeasurable parameters and, since parametric uncertainty may degrade the performance of model predictive control (MPC), it is desirable to use mechanisms to update the unknown or uncertain parameters. One method is to apply adaptive extensions of MPC where parameter estimation and control are performed online.
Robust and Adaptive Model Predictive Control of Nonlinear Systems proposes such an approach, with a design methodology for adaptive robust nonlinear MPC (NMPC) systems in the presence of disturbances and parametric uncertainties.
One of the key concepts pursued is set-based adaptive parameter estimation, which provides a mechanism to estimate the parameter uncertainty set as well as the unknown parameters. The knowledge of non-conservative uncertain set estimates are used to design robust adaptive NMPC algorithms that guarantee robustness of the NMPC system in the face of parameter uncertainty.
About the Lead Editor
Martin Guay is a Professor at the Faculty of Engineering and Applied Science at Queens University, Canada, where his research interests include process control, statistical modeling of dynamical systems, extremum seeking control, observation and adaptation in nonlinear systems, and supervisory control design for flexible manufacturing systems.
Robust and Adaptive Model Predictive Control of Nonlinear Systems is suitable for researchers and advanced students in control engineering and modeling, industrial process control engineers and control system platform developers