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By Professor I. D. Landau, Professor R. Lozano, Professor M. M’Saad (auth.)

Adaptive Control presents innovations for computerized, real-time alterations in controller parameters on the way to attaining and/or retaining a fascinating point of procedure functionality within the presence of unknown or variable procedure parameters.

Many elements of the sector are handled in coherent and orderly type, beginning with the issues posed by means of process uncertainties and relocating directly to the presentation of recommendations and their functional value. in the normal context of contemporary advancements, the ebook seems to be at:

• synthesis and research of parameter model algorithms;

• recursive plant-model identity in open and closed loop;

• strong electronic keep watch over for adaptive control;

• direct and oblique adaptive regulate; and

• useful facets and applications.

To mirror the significance of electronic desktops for the appliance of adaptive keep an eye on strategies, discrete-time features are emphasised. to steer the reader, the e-book comprises quite a few functions of adaptive keep watch over techniques.

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Extra resources for Adaptive Control

Example text

In such sit uat ions , a n adapt ive cont ro l sche me ca n be chea p er to implement since it will not use addit ional me asurem ents a nd requires only addit ional comput er pow er . 1 and whi ch was illustrated in Fig. 1. On e of the key points is the spec ifica t ion of the desired con trol loop p erform ance. In m any ca ses , the desired performance of the feedback control syst em ca n b e specified in t erms of the cha ract erist ics of a dynamic sys te m whi ch is a realization of the desir ed b eh aviour of the closed loop sys te m .

Ther efor e, the sampling period T s is ti ed to the st rip spee d using the formula: £+0 Ts - -v d - '. r»: (d = integer) wh ere 0 is an addit ional small time-d elay corresponding to the equivalent t imedelay of the industrial network and of the progr ammabl e cont roller used for pressure regul ation and d is the discr et e-time delay (inte ger) . A linearized di scr et e-time model can be identified . However , t he param et ers of t he model will dep end on t he dis tance between the air knives and the steel strip and on the spe ed V .

However , nothing forbids us to update the est imates of the pl ant model p arameters at each sampling in st ant , a nd to update the controller pa ramet er s onl y eve ry N sam pling inst ants. Ar gumen t s for choosing t his procedure a re re la t ed t o: • the possibility of get t ing better pa ramet er estimates for cont rol design , • the event ual re-initia lization of the plant paramet er s est imat ion algorithm aft er eac h con troller updating, • the possibilit y of usin g a mo re sophist icated contro l design procedure (in particular robust control design) requiring a large amount of computation .

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