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

By Jay A. Farrell, Marios M. Polycarpou

A hugely obtainable and unified method of the layout and research of clever regulate structures Adaptive Approximation dependent keep an eye on is a device each keep watch over dressmaker must have in his or her keep watch over toolbox. blending approximation concept, parameter estimation, and suggestions keep watch over, this e-book offers a unified technique designed to allow readers to use adaptive approximation dependent keep watch over to current platforms, and, extra importantly, to realize sufficient instinct and realizing to control and mix it with different keep watch over instruments for purposes that experience now not been encountered ahead of. The authors offer readers with a thought-provoking framework for carefully contemplating such questions as: * What houses may still the functionality approximator have? * Are convinced households of approximators stronger to others? * Can the soundness and the convergence of the approximator parameters be assured? * Can regulate platforms be designed to be strong 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 sorts of nonlinear dynamic platforms are amenable to this process? * What are the constraints of adaptive approximation established keep an eye on? Combining theoretical formula and layout concepts with vast use of simulation examples, this publication is a stimulating textual content for researchers and graduate scholars and a helpful source for working towards engineers.

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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. We have also discussed the under-constrained case when there is not sufficient data available to completely specify the parameters of the approximator. Normally in online control applications, the number of data samples rn will eventually be much larger that the number of parameters N .

Even if the theoretical conditions required for CP to be invertible are satisfied, if zi is near z j for i # j, then Q, may be nearly singular. In this case, any measurement error in Y may be magnified in the determination of 8. In addition, the solution via eqn. 9) may 30 APPROXIMATIONTHEORY be numerically unstable. Preferred methods of solution are by QR, UD, or singular value decompositions [991. For a unique solution to exist, the number of free parameters (Lee,the dimension of 0) must be exactly equal to the number m of sample points 2 % Therefore, .

Plot the approximation error versus v at t = 100. Discuss why it is small near v = 20 and v = 100, but not small elsewhere. 3. Using the Lyapunov function show that the time derivative of V evaluated along the error dynamics ofthe approximationbased control system is negative semidefinite. Why can we only say that thi; derivative is semidefinite? What does this fact imply about each component of (a, B J , #,)? This Page Intentionally Left Blank CHAPTER 2 APPROXIMATION THEORY This chapter formulates the numeric data processing issues of interpolation and function approximation, and then discusses function approximator properties that are relevant to the use of adaptive approximation for estimation and feedback control.

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