Performance of Nonlinear Approximate Adaptive Controllers

In recent years there has been wide interest in nonlinear adaptive control using function approximator models, either for tracking or regulation. Such techniques are often described as ′neural network based control′. In this book, the authors present a critical evaluation of the approximate model philosophy and its setting, showing situations in which its designs can be expected to surpass rival algorithms.

Describing complex concepts with a variety of theoretical and practical illustrations, this book also:

• Develops a comprehensive set of performance based adaptive control results
• Provides a constructive framework for estimating both upper and lower bounds of performance cost functionals
• Presents theoretical principles and a mathematically well–founded introduction to the area of ′intelligent control′

Featuring a toolkit that allows quantitative comparisons between competing control designs, Performance of Nonlinear Approximate Adaptive Controllers will prove an invaluable reference for control practitioners and theorists, AI researchers, applied mathematicians and graduate students in adaptive control and stability.