Adaptive approximation based control: unifying neural, fuzzy by Jay A. Farrell, Marios M. Polycarpou

By Jay A. Farrell, Marios M. Polycarpou

A hugely available and unified method of the layout and research of clever regulate platforms Adaptive Approximation dependent regulate is a device each keep an eye on fashion designer must have in his or her regulate toolbox. blending approximation thought, parameter estimation, and suggestions keep an eye on, this booklet provides a unified process designed to allow readers to use adaptive approximation established keep watch over to present platforms, and, extra importantly, to realize adequate instinct and figuring out to govern and mix it with different regulate instruments for functions that experience no longer been encountered ahead of. The authors offer readers with a thought-provoking framework for conscientiously contemplating such questions as: * What houses should still the functionality approximator have? * Are sure households of approximators greater to others? * Can the soundness and the convergence of the approximator parameters be assured? * Can keep watch over structures be designed to be powerful within the face of noise, disturbances, and unmodeled results? * Can this technique deal with major adjustments within the dynamics as a result of such disruptions as method failure? * What kinds of nonlinear dynamic platforms are amenable to this strategy? * What are the restrictions of adaptive approximation dependent keep an eye on? Combining theoretical formula and layout innovations with wide use of simulation examples, this e-book is a stimulating textual content for researchers and graduate scholars and a precious source for practising engineers.

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Extra resources for Adaptive approximation based control: unifying neural, fuzzy and traditional adaptive approximation approaches

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ON-']. 2 for amoredetailed discussionofpolynomialapproximators. R1, has the form of eqn. 4 for amore detailed discussion of radial basis function approximators.

Y k I T , and w k is an appropriately dimensioned positive definite matrix. Solution of this equation requires inversion of an N x N matrix. When the ( k 1)st sample becomes available, this expression requires the availability of all previous training samples and again requires inversion of a new N x N matrix. For a diagonal weighting matrix w k , direct implementation of the WLS algorithm has storage and computational requirements that increase with k . This is not satisfactory, since k is increasing without bound.

Polycarpou Copyright @ 2006 John Wiley & Sons, Inc. 23 24 APPROXIMATIONTHEORY be discussed through the remainder of this chapter. 2 discusses the problem of function interpolation. 3 discusses the problem of function approximation. 4 discusses function approximator properties in the context of online function approximation. 1 MOTIVATING EXAMPLE Consider the following simple example that illustrates some of the issues that arise in approximation based control applications. 1) u ( k )= Yd(k + 1) - P [ Y d P ) - Y (k)l - f*(Y(k)).

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