Functional Adaptive Control

1. Front Matter

   Pages i-xxi

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2. Introduction

   1. Front Matter

      Pages 1-1

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   2. Introduction

      • Simon G. Fabri, Visakan Kadirkamanathan

      Pages 3-19

3. Deterministic Systems

   1. Front Matter

      Pages 21-21

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   2. Adaptive Control of Nonlinear Systems

      • Simon G. Fabri, Visakan Kadirkamanathan

      Pages 23-46

   3. Dynamic Structure Networks for Stable Adaptive Control

      • Simon G. Fabri, Visakan Kadirkamanathan

      Pages 47-78

   4. Composite Adaptive Control of Continuous-Time Systems

      • Simon G. Fabri, Visakan Kadirkamanathan

      Pages 79-100

   5. Functional Adaptive Control of Discrete-Time Systems

      • Simon G. Fabri, Visakan Kadirkamanathan

      Pages 101-127

4. Stochastic Systems

   1. Front Matter

      Pages 129-129

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   2. Stochastic Control

      • Simon G. Fabri, Visakan Kadirkamanathan

      Pages 131-145

   3. Dual Adaptive Control of Nonlinear Systems

      • Simon G. Fabri, Visakan Kadirkamanathan

      Pages 147-164

   4. Multiple Model Approaches

      • Simon G. Fabri, Visakan Kadirkamanathan

      Pages 165-185

   5. Multiple Model Dual Adaptive Control of Jump Nonlinear Systems

      • Simon G. Fabri, Visakan Kadirkamanathan

      Pages 187-212

   6. Multiple Model Dual Adaptive Control of Spatial Multimodal Systems

      • Simon G. Fabri, Visakan Kadirkamanathan

      Pages 213-241

5. Conclusions

   1. Front Matter

      Pages 243-243

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   2. Conclusions

      • Simon G. Fabri, Visakan Kadirkamanathan

      Pages 245-249

6. Back Matter

   Pages 251-266

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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.

• Adaptive Control
• Intelligent Control
• Neural Networks
• Nonlinear Control
• Simulation
• Stochastic Control
• complex systems
• complexity
• intelligence
• intelligent systems
• model
• uncertainty

• Department of Electrical Power and Control Engineering, University of Malta, Msida, Malta

  Simon G. Fabri

• Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, England

  Visakan Kadirkamanathan

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