Overview
- Authors:
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Simon G. Fabri
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Department of Electrical Power and Control Engineering, University of Malta, Msida, Malta
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Visakan Kadirkamanathan
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Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, England
- Unique in its systematic attention and approach to stochastic systems
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Table of contents (11 chapters)
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Introduction
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- Simon G. Fabri, Visakan Kadirkamanathan
Pages 3-19
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Deterministic Systems
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- Simon G. Fabri, Visakan Kadirkamanathan
Pages 23-46
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- Simon G. Fabri, Visakan Kadirkamanathan
Pages 47-78
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- Simon G. Fabri, Visakan Kadirkamanathan
Pages 79-100
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- Simon G. Fabri, Visakan Kadirkamanathan
Pages 101-127
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Stochastic Systems
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Front Matter
Pages 129-129
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- Simon G. Fabri, Visakan Kadirkamanathan
Pages 131-145
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- Simon G. Fabri, Visakan Kadirkamanathan
Pages 147-164
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- Simon G. Fabri, Visakan Kadirkamanathan
Pages 165-185
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- Simon G. Fabri, Visakan Kadirkamanathan
Pages 187-212
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- Simon G. Fabri, Visakan Kadirkamanathan
Pages 213-241
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Conclusions
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Front Matter
Pages 243-243
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- Simon G. Fabri, Visakan Kadirkamanathan
Pages 245-249
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Back Matter
Pages 251-266
About this book
The field of intelligent control has recently emerged as a response to the challenge of controlling highly complex and uncertain nonlinear systems. It attempts to endow the controller with the key properties of adaptation, learn ing and autonomy. The field is still immature and there exists a wide scope for the development of new methods that enhance the key properties of in telligent systems and improve the performance in the face of increasingly complex or uncertain conditions. The work reported in this book represents a step in this direction. A num ber of original neural network-based adaptive control designs are introduced for dealing with plants characterized by unknown functions, nonlinearity, multimodal behaviour, randomness and disturbances. The proposed schemes achieve high levels of performance by enhancing the controller's capability for adaptation, stabilization, management of uncertainty, and learning. Both deterministic and stochastic plants are considered. In the deterministic case, implementation, stability and convergence is sues are addressed from the perspective of Lyapunov theory. When compared with other schemes, the methods presented lead to more efficient use of com putational storage and improved adaptation for continuous-time systems, and more global stability results with less prior knowledge in discrete-time sys tems.
Authors and Affiliations
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Department of Electrical Power and Control Engineering, University of Malta, Msida, Malta
Simon G. Fabri
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Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, England
Visakan Kadirkamanathan
