Adaptive Control, Filtering, and Signal Processing by Karl J. ÅStröM (auth.), K. J. Åström, G. C. Goodwin, P. R. PDF

By Karl J. ÅStröM (auth.), K. J. Åström, G. C. Goodwin, P. R. Kumar (eds.)

ISBN-10: 1441985689

ISBN-13: 9781441985682

ISBN-10: 1461264391

ISBN-13: 9781461264392

The region of adaptive platforms, which encompasses recursive identity, adaptive keep an eye on, filtering, and sign processing, has been essentially the most lively components of the previous decade. because adaptive controllers are essentially nonlinear controllers that are utilized to nominally linear, probably stochastic and time-varying platforms, their theoretical research is mostly very tough. however, over the last decade a lot primary growth has been made on a few key questions pertaining to their balance, convergence, functionality, and robustness. furthermore, adaptive controllers were effectively hired in several sensible functions, and feature even entered the marketplace.

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Additional resources for Adaptive Control, Filtering, and Signal Processing

Example text

Let v be the velocity of the state at time 0-. 2) it is useful to have analytic expressions for the derivative of the function f . 2. 2). Assume that T < h, then the function and its derivative are given by f(h) = df(h) dh C(I + iI>(h))-l(ro(h) - r1(h)) C(I + iI>(h))-1iI>(h)(2e- AT • - I)B C(I + iI>(h))-2iI>A(ro(h) - r1(h)) Proof. The proof is obtained by a straightforward calculation. 1. Stability analysis. The analysis of the stability of the limit cycles is complicated for systems with time delays, because the state of the system is infinite dimensioned .

A . N. R. PARTINGTON , Worst case system identification in 1'1 : op tim al algorithms and error bounds , Syst , and Contr. Letters, vol. 19, (1992), pp . 419 -424 . [4] G. P. KHARGONEKAR, Linear and nonlinear algorithms for identification in 1i oo with error bounds, IEEE Trans , Automat. Control, vo!. AC-37, (1992), pp . 953-963. M . P. KHARGONEKAR, A comparison of classical stochastic and deterministic robust identification, IEEE Trans . Automat . Control, vol . AC37, (1992) , pp. 994-1000. [6] L.

15b) are well defined . For the verification of the strong consist ency of a family of least squares estimates of th e unknown parameter vector, the following assumption is used. s . 1) . 2 . Assume that (A 2) is satisfied. ) : H -+ D~-:( is continuous, (Ax , Vx(t, x)) for x E V(A) can be extended to a continuous function h : [0,T] x H -+ IW. and Ih(t, x)1+ lV(t , x)1+ IVx(t, X)ID~:' + IVxx(t, X)IC(H) + IVi(t, x)1::::k (l + Ixn for (t ,x) E [O ,T) x Hand p > O,k > O. Assume that one of the following three conditions is satisfied.

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