In recent years there has been a wide interest in non-linearadaptive control using approximate models, either for tracking orregulation, and usually under the banner of neural network basedcontrol. The authors present a unique critical evaluation of theapproximate model philosophy and its setting, rigorously comparingthe performance of such controls against competing designs.Analysing a very topical aspect of contemporary research andcontrol practice this book highlights the situations in whichapproximate model based designs are most appropriate and indicatesscenarios in which other designs could be used more productively.Throughout the text concepts are illustrated using a variety ofexamples, both academic problems and those based on physicalexamples. The work is designed to open the door to realisticapplications. * Unified coverage of the theory and application of a wide range ofcontrol systems areas including neural network based control andcontrol using the approximate model * Presents a mathematically well founded introduction to the areaof intelligent control * A varied selecion of practical examples drawn from a variety offields, including robotics and aerospace, illustrate theoreticalprinciples * Clear compaisons of a variety of control designs * Cross disciplinary approach to this leading edge topic A valuable reference for control practitioners and theorists,artificial intelligence researchers and applied mathematicians, aswell as graduate students and researchers with an interest inadaptive control and stability.weiterlesen