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Adaptive Control: Algorithms, Analysis and Applications by Ioan Doré Landau, Rogelio Lozano, Mohammed M'Saad, Visit

By Ioan Doré Landau, Rogelio Lozano, Mohammed M'Saad, Visit Amazon's Alireza Karimi Page, search results, Learn about Author Central, Alireza Karimi,

Adaptive Control (second variation) indicates how a wanted point of procedure functionality could be maintained immediately and in actual time, even if strategy or disturbance parameters are unknown and variable. it's a coherent exposition of the numerous points of this box, commencing the issues to be addressed and relocating directly to strategies, their sensible importance and their software. Discrete-time facets of adaptive keep watch over are emphasised to mirror the significance of electronic pcs within the software of the tips presented.

The moment version is punctiliously revised to throw mild on contemporary advancements in concept and purposes with new chapters on:

· multimodel adaptive keep watch over with switching;

· direct and oblique adaptive legislation; and

· adaptive feedforward disturbance compensation.

Many algorithms are newly awarded in MATLAB® m-file layout to facilitate their employment in actual structures. Classroom-tested slides for teachers to take advantage of in instructing this fabric also are now supplied. All of this supplementary digital fabric will be downloaded from www.springer.com/978-0-85729-663-4.

The middle fabric can also be up-dated and re-edited to maintain its standpoint based on glossy principles and extra heavily to affiliate algorithms with their purposes giving the reader an effective grounding in:

· synthesis and research of parameter version algorithms;

· recursive plant version identity in open and closed loop;

· strong electronic keep watch over for adaptive control;

· strong parameter edition algorithms;

· useful concerns and functions, together with versatile transmission platforms, lively vibration keep watch over and broadband disturbance rejection and a supplementary creation on scorching dip galvanizing and a phosphate drying furnace.

Control researchers and utilized mathematicians will locate Adaptive Control of important and enduring curiosity and its use of instance and alertness will attract practitioners operating with unknown- and variable-parameter plant.

Praise for the 1st edition:

…well written, attention-grabbing and straightforward to keep on with, in order that it constitutes a useful addition to the monographs in adaptive keep an eye on for discrete-time linear platforms… appropriate (at least partially) to be used in graduate classes in adaptive control.

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Extra info for Adaptive Control: Algorithms, Analysis and Applications

Sample text

In these schemes, the output of the adjustable predictor (whose parameters are known at each sampling) will behave as the output of a reference model. For this reason, such schemes are also called “implicit model reference adaptive control” (Landau 1981; Landau and Lozano 1981; Egardt 1979). This is illustrated in Fig. 14. To illustrate the idea of “reparameterization” of the plant model, consider the following example. 1) where y is the plant output, u is the plant input and a is an unknown parameter.

The basic scheme for the on-line estimation of plant model parameters is shown in Fig. 12. The basic idea is to build an adjustable predictor for the plant output which may or may not use previous plant output measurements and to compare the predicted output with the measured output. The error between the plant output and the predicted output (subsequently called prediction error or plant-model error) is used by a parameter adaptation algorithm which at each sampling instant will adjust the parameters of the adjustable predictor in order to minimize the prediction error in the sense of a certain criterion.

Gain-scheduling schemes are also used in connection with adaptive control schemes where the gain-scheduling takes care of rough changes of parameters when the conditions of operation change and the adaptive control takes care of the fine tuning of the controller. Note however that in certain cases, the use of this simple principle can be very costly because: 1. It may require additional expensive transducers. 2. It may take a long time and numerous experiments in order to establish the desired relationship between environment measurements and controller parameters.

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