Del i serien Control, Robotics and Sensors

Non-linear Predictive Control

Theory and practice

Inbunden, Engelska, 2001

AvBasil, Kouvaritakis,Basil Kouvaritakis,Mark Cannon

2 109 kr

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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.

Produktinformation

Utgivningsdatum2001-10-26
Mått156 x 234 x 18 mm
Vikt499 g
FormatInbunden
SpråkEngelska
SerieControl, Robotics and Sensors
Antal sidor276
FörlagInstitution of Engineering and Technology
ISBN9780852969847

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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.

Part IChapter 1: Review of nonlinear model predictive control applicationsChapter 2: Nonlinear model predictive control: issues and applicationsPart IIChapter 3: Model predictive control: output feedback and tracking of nonlinear systemsChapter 4: Model predictive control of nonlinear parameter varying systems via receding horizon control Lyapunov functionsChapter 5: Nonlinear model-algorithmic control for multivariable nonminimum-phase processesPart IIIChapter 6: Open-loop and closed-loop optimality in interpolation MPCChapter 7: Closed-loop predictions in model based predictive control linear and nonlinear systemsChapter 8: Computationally efficient non linear predictive control algorithm for control of constrained nonlinear systemsPart IVChapter 9: Long-prediction-horizon nonlinear model predictive controlChapter 10: Nonlinear control of industrial processesChapter 11: Nonlinear model based predictive control using multiple local modelsChapter 12: Neural network control of a gasoline engine with rapid sampling