Download e-book for iPad: Adaptive approximation based control: unifying neural, fuzzy by Jay A. Farrell, Marios M. Polycarpou

By Jay A. Farrell, Marios M. Polycarpou

ISBN-10: 0471727881

ISBN-13: 9780471727880

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

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

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N -m denote a basis for L&. , 0 = @A) such that @'B = Y . Every other solution v tov'@ = Y can be expressed as czl w =8+ N-m a,d, 2=1 for some atE R1. FUNCTION APPROXIMATION 33 Since 6' is orthogonal to C,"=;'" aidi by construction, llwll = ll6'll+ I/ C,";" aidill which is always greater than Il6'il. 7 of [29]. 3 Summary This section has discussed the offline problem of fitting a function to a fixed batch of data. In the process, we have introduced the topic of weighted least squares parameter estimation which is applicable when the number of data points exceeds the number of free parameters defined for the approximator.

28) Finally, to show that the continuous-time least squares estimate of 0 satisfies eqn. 25) we differentiate both sides of eqn. At)) = d(t) = W d t ) ( Y ( 4 - B ( t ) ) . 29). Typically, the initial value of the matrix P is selected to be large. The initial matrix must be nonsingular. Often, it is initialized as P(0) = y I where y n is a large positive number. The implementation does not invert any matrix. m- ~ ( z ) ~(M e ) ~ - q 5 ~ ~d0z . ) APPROXIMATOR PROPERTIES 39 Again, we find the gradient of J with respect to 8, set it to zero, and find the resulting parameter estimate.

2: Top - Data for approximating f using nearest neighbor matching. Bottom Approximated f resulting from nearest neighbor matching. 1. 1. The input-output training data (f(y(i)), y(i)) cannot be expected to be distributed according to an analytic distribution. Instead, the training data will be defined by the control task that the system is performing. The distribution of training data over a fixed-duration window will typically be time varying. If control is operating well, then the training samples will cluster in the vicinity of a state trajectory (several may be possible) defined by the reference input.

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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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