Model-based predictive control (MPC) has proved to be a fertile area of research. It has gained enormous success within industry, especially in the context of process control. Nonlinear model-based predictive control (NMPC) is of particular interest as this best represents the dynamics of most real plant. This book collects together the important results which have emerged in this field, illustrating examples by means of simulations on industrial models. In particular there are contributions on feedback linearisation, differential flatness, control Lyapunov functions, output feedback, and neural networks. The international contributors to the book are all respected leaders within the field, which makes for essential reading for advanced students, researchers and industrialists in the field of control of complex systems.

About the Authors

Basil Kouvaritakis is Professor of Engineering Science at Oxford University and has been researching MPC and computationally efficient NMPC for the last 12 years, publishing over 50 papers on the subject.

Mark Cannon is departmental lecturer at the Engineering Department at Oxford University and has been working on MPC for the past 5 years, including the development of computationally efficient NMPC.

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