Predictive Control of Nonlinear System Based on Neural Networks

Jiamei Deng

ISBN 10: 3844300090 / ISBN 13: 9783844300093

Verlag: LAP LAMBERT Academic Publishing Feb 2011, 2011

Beschreibung

This item is printed on demand - it takes 3-4 days longer - Neuware -Model predictive control (MPC) is an important industrial control technique. Most conventional MPC schemes use linear models. However, the use of linear models can result in a serious deterioration of control performance with many types of nonlinear plants. Feedback linearisation is an important nonlinear control technique which can transform a nonlinear system into a linear system. Dynamic neural networks have the ability to approximate multi-input multi-output general nonlinear systems and have the differential equation structure. This book presents a hybrid control strategy integrating dynamic neural networks and feedback linearisation into a predictive control scheme. This book can be used as a course textbook, a source for practising control engineers with an interest in nonlinear control techniques and also a reference material for academic researchers in nonlinear control theory. 200 pp. Englisch.

Bestandsnummer des Verkäufers 9783844300093

Bibliografische Details

• Titel: Predictive Control of Nonlinear System Based on Neural Networks
• Verlag: LAP LAMBERT Academic Publishing Feb 2011
• Veröffentlichungsdatum: 2011
• Sprache: Englisch
• ISBN-10: 3844300090
• ISBN-13: 9783844300093
• Bindung: Taschenbuch
• Zustand: Neu

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Inhaltsangabe

Model predictive control (MPC) is an important industrial control technique. Most conventional MPC schemes use linear models. However, the use of linear models can result in a serious deterioration of control performance with many types of nonlinear plants. Feedback linearisation is an important nonlinear control technique which can transform a nonlinear system into a linear system. Dynamic neural networks have the ability to approximate multi-input multi-output general nonlinear systems and have the differential equation structure. This book presents a hybrid control strategy integrating dynamic neural networks and feedback linearisation into a predictive control scheme. This book can be used as a course textbook, a source for practising control engineers with an interest in nonlinear control techniques and also a reference material for academic researchers in nonlinear control theory.