By Petros Ioannou, Barýp Fidan

ISBN-10: 0898716152

ISBN-13: 9780898716153

Designed to satisfy the wishes of a large viewers with out sacrificing mathematical intensity and rigor, Adaptive keep watch over instructional offers the layout, research, and alertness of a large choice of algorithms that may be used to regulate dynamical platforms with unknown parameters. Its tutorial-style presentation of the elemental suggestions and algorithms in adaptive keep an eye on make it appropriate as a textbook.

Adaptive regulate instructional is designed to serve the desires of 3 unique teams of readers: engineers and scholars attracted to studying how one can layout, simulate, and enforce parameter estimators and adaptive keep watch over schemes with no need to totally comprehend the analytical and technical proofs; graduate scholars who, as well as achieving the aforementioned ambitions, additionally are looking to comprehend the research of easy schemes and get an concept of the stairs all in favour of extra complicated proofs; and complicated scholars and researchers who are looking to research and comprehend the main points of lengthy and technical proofs with an eye fixed towards pursuing examine in adaptive regulate or similar themes.

The authors in achieving those a number of ambitions by way of enriching the booklet with examples demonstrating the layout techniques and simple research steps and by means of detailing their proofs in either an appendix and electronically on hand supplementary fabric; on-line examples also are on hand. an answer guide for teachers could be received through contacting SIAM or the authors.

This ebook can be invaluable to masters- and Ph.D.-level scholars in addition to electric, mechanical, and aerospace engineers and utilized mathematicians.

Preface; Acknowledgements; record of Acronyms; bankruptcy 1: advent; bankruptcy 2: Parametric types; bankruptcy three: Parameter id: non-stop Time; bankruptcy four: Parameter identity: Discrete Time; bankruptcy five: Continuous-Time version Reference Adaptive keep an eye on; bankruptcy 6: Continuous-Time Adaptive Pole Placement keep watch over; bankruptcy 7: Adaptive keep watch over for Discrete-Time structures;

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Example text

The first step in the design of online parameter identification (PI) algorithms is to lump the unknown parameters in a vector and separate them from known signals, transfer functions, and other known parameters in an equation that is convenient for parameter estimation. For the above example, one such suitable parametric representation is obtained by expressing the above system as and in the compact algebraic form where In the general case, this class of parameterizations is of the form where 0* e JHW is the vector with all the unknown parameters and z e W, 0 6 i)t" are signals available for measurement.

Parametric Models where Since the parameter b and the input u and output y are known, the signals z, 4> can be generated by filtering. 6 Consider the second-order ARMA model where, at instant A;, >>(&), u(k), and their past values are available for measurement. 8) in the form of SPM, we rewrite it as by shifting each side by four in the time axis. This is equivalent to filtering each side with the fourth-order stable filter p-. 2 are unknown constants. For identification purposes, the system may be expressed as and put in the form of the DPM where If we want W(s) to be a design transfer function with a pole, say at A.

31). 31), imply that £, sms e £00. 36) that from which we establish that ems e £2 and hence s e LI (due to m2s = 1 + n2}. 35) we have which together with j^ e £00 and \sms \ e £2 imply that # € £ 2 0 £00, and the proof of (i) is complete. The proof of parts (ii) and (iii) is longer and is presented in the web resource [94]. 2 The rate of convergence of 0 to 9* can be improved if we choose the design parameters so that 1 — y\ is as small as possible or, alternatively, y\ e (0, 1) is as close to 1 as possible.

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Adaptive control tutorial by Petros Ioannou, Barýp Fidan


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