Most physical systems possess parametric uncertainties or unmeasurable parameters and, since parametric uncertainty may degrade the performance of model predictive control (MPC), mechanisms to update the unknown or uncertain parameters are desirable in application. One possibility is to apply adaptive extensions of MPC in which parameter estimation and control are performed online. This book 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 the concept of set-based adaptive parameter estimation, which provides a mechanism to estimate the unknown parameters as well as an estimate of the parameter uncertainty set. The knowledge of non-conservative uncertain set estimates is exploited in the design of robust adaptive NMPC algorithms that guarantee robustness of the NMPC system to parameter uncertainty.

Topics covered include: a review of nonlinear MPC; extensions for performance improvement; introduction to adaptive robust MPC; computational aspects of robust adaptive MPC; finite-time parameter estimation in adaptive control; performance improvement in adaptive control; adaptive MPC for constrained nonlinear systems; adaptive MPC with disturbance attenuation; robust adaptive economic MPC; setbased estimation in discrete-time systems; and robust adaptive MPC for discrete-time systems.

About the Authors

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. He is Deputy Editor-in-Chief of the Journal of Process Control, and Associate Editor of Automatica, IEEE Transactions on Control Systems Technology and Canadian Journal of Chemical Engineering.

Veronica Adetola is a Research Engineer at the United Technologies Research Centre, USA. Her research interests include model-based design and control of complex dynamical systems, model predictive control of constrained uncertain systems, real-time optimization, adaptive control, parameter estimation and system identification.

Darryl DeHaan is currently a Senior Process Control Engineer with LyondellBasell and has been engaged in both industrial controller implementation and research since 2006. He has a Ph.D. in Chemical Engineering from Queens University, Canada, where his research efforts focused on model predictive control techniques for nonlinear uncertain systems.

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