tailieunhanh - Kiểm soát và ổn định thích ứng dự toán cho các hệ thống phi tuyến P5

Overview The use of function approximation actually has a long history in control systems. For instance, we use function approximation ideas in the development of models for control design and analysis, and conventional adaptive control generally involves the on-line tuning of linear functions (linear approximators) to match unknown linear functions (., tuning a linear model to match a linear plant with constant but unknown parameters) as we discussed in Chapter 1. The adaptive routines we will study in this book may be described as on-line function approximation techniques where we adjust approximators to match unknown nonlinearities (., plant. | Stable Adaptive Control and Estimation for Nonlinear Systems Neural and Fuzzy Approximator Techniques. Jeffrey T. Spooner Manfredi Maggiore Raul Ordonez Kevin M. Passino Copyright 2002 John Wiley Sons Inc. ISBNs 0-471-41546-4 Hardback 0-471-22113-9 Electronic Chapte r Function Approximation Overview The use of function approximation actually has a long history in control systems. For instance we use function approximation ideas in the development of models for control design and analysis and conventional adaptive control generally involves the on-line tuning of linear functions linear approximators to match unknown linear functions . tuning a linear model to match a linear plant with constant but unknown parameters as we discussed in Chapter 1. The adaptive routines we will study in this book may be described as on-line function approximation techniques where we adjust approximators to match unknown nonlinearities . plant nonlinearities . In Chapter 4 we discussed the tuning of several candidate approximator structures and especially focused on neural networks and fuzzy systems. In this chapter we will show that fuzzy systems or neural networks with a given structure possess the ability to approximate large classes of functions simply by changing their parameters hence they can represent for example a large class of plant nonlinearities. This is important since it provides a theoretical foundation on which the later techniques are built. For instance it will guarantee that a certain ideal level of approximation accuracy is possible and whether or not our optimization algorithms succeed in achieving it this is what the stability and performance of our adaptive systems typically depends on. It is for this reason that neural network or fuzzy system approximators are preferred over linear approximators like those studied in adaptive control for linear systems . Linear approximator structures cannot represent as wide of a class of functions and for many .