Adaptive approximation based control: unifying neural, fuzzy - download pdf or read online

By Jay A. Farrell, Marios M. Polycarpou

ISBN-10: 0471727881

ISBN-13: 9780471727880

A hugely available and unified method of the layout and research of clever keep watch over platforms Adaptive Approximation established keep an eye on is a device each keep watch over fashion designer must have in his or her keep watch over toolbox. blending approximation concept, parameter estimation, and suggestions keep watch over, this e-book provides a unified technique designed to let readers to use adaptive approximation dependent keep watch over to current structures, and, extra importantly, to realize adequate instinct and realizing to govern and mix it with different keep an eye on instruments for purposes that experience now not been encountered ahead of. The authors supply readers with a thought-provoking framework for conscientiously contemplating such questions as: * What houses should still the functionality approximator have? * Are definite households of approximators more suitable to others? * Can the steadiness 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 strategy deal with major alterations within the dynamics as a result of such disruptions as procedure failure? * What varieties of nonlinear dynamic structures are amenable to this strategy? * What are the constraints of adaptive approximation established regulate? Combining theoretical formula and layout innovations with large use of simulation examples, this ebook is a stimulating textual content for researchers and graduate scholars and a priceless source for working towards engineers.

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Additional info for Adaptive approximation based control: unifying neural, fuzzy and traditional adaptive approximation approaches

Example text

Hint: add -tu EIJ to eqn. 5) and substitute eqn. ) + Show that the correct values for the model of eqn. 5) to match eqn. 2. First, duplicate the results of the example. Do the estimated parameters converge to the same values each time TI is commanded to the same operating point? Using the Lyapunov function 71 Yz 73 show that the time derivative of V evaluated along the error dynamics of the adaptive control system is negative semidefinite. Why can we only say that this derivative is semidefinite?

Since with the specified approximation structure the data cannot be fit perfectly, the designer may instead select the approximator parameters to minimize some measure of the function approximation error. 10) which corresponds to the norm \lY\lb = i Y T W Y where W is symmetric and positive definite. 13) where it has been assumed that rank(@)= N (Lee, that @ has N linearly independent rows and columns) so that @W@' is nonsingular. , the N rows of @ are not linearly independent), then either additional data are required or the under-constrained approach defined below must be used.

Therefore, the specification of the adaptive approximator f(z;8, a)has a critical affect on the tracking performance that the approximationbased control system will be capable of achieving. The approximator structure defined in eqn. 27) is sufficient to describe the various approximators used in the neural and fuzzy control literature, as well as many other approximators. Issues related to the adaptive approximation problem and approximator selection will be discussed in Chapter 2. Specific approximators will be discussed in Chapter 3.

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


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