By Howard Kaufman

ISBN-10: 146120657X

ISBN-13: 9781461206576

ISBN-10: 1461268591

ISBN-13: 9781461268598

Suitable both as a reference for working towards engineers or as a textual content for a graduate path in adaptive keep watch over structures, this ebook is a self -contained compendium of comfortably implementable adaptive keep an eye on algorithms which were constructed and utilized via the authors for over fifteen years. those algorithms, which don't require the person to spot the method parameters explicitly, were effectively utilized to a large choice of engineering difficulties together with versatile constitution keep an eye on, blood strain keep an eye on, and robotics; they're appropriate for a wide selection of a number of input-output keep an eye on platforms with uncertainity and exterior disturbances. The textual content is meant to permit a person with wisdom of uncomplicated linear multivariable platforms to evolve the algorithms to difficulties in a wide selection of disciplines. hence, as well as constructing the theoretical info of the algorithms awarded, the textual content offers significant emphasis to layout of algorithms and to consultant functions in flight keep an eye on, versatile constitution regulate, robotics, and drug-infusion keep watch over. Engineers can therefore use and attempt those algorithms in sensible difficulties. This moment variation has been corrected and up to date all through. It uses MATLAB courses for a few of the illustrative examples; those courses are defined within the textual content and will be got from the MathWorks dossier server.

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**Extra info for Direct Adaptive Control Algorithms: Theory and Applications**

**Example text**

For the sake of generality, any particular assumptions about the input signal um(t), except for its boundedness, will now be removed. 111) because in this manner x;(t) remains differentiable. 112) has a solution for the matrix X. 113) rank [Cp Cm] = rank rep] , which is satisfied, in general, because the system has more variables than equations, especially if the number of plant states greatly exceeds the number of model states. 115) x;(t) = Apx;(t) + Bpu;(t). 115) gives x;(t) = ApXxm(t) + BpKxxm(t) + BpKuum(t).

Unfortunately, the control law requires knowledge of the unknown ideal plant input. However, boundedness of the output error was ensured when an. approximation to the ideal plant input was utilized in the adaptive control law. Bar-Kana and Kaufman [49] removed the restriction on the algorithm [19], [65], [31] that the model input be a step command. They also incorporated [48], [68], [20] Ioannou's fixed sigma-algorithm [43] into the basic algorithm. This results in a robust MRAC law for ASPR multi-inputmulti-output plants.

16), Cpx;(t) = Cmxm(t), and therefore, Cpxp = Cmx m , which is the desired result. timately bounded. 5), are together a special case of the command generator tracker when the state vector is available, and it is assumed that x;(t) = xm(t). Since x;(t) = 8uxm(t) + 8 12 U m , the PMF conditions imply that 8 11 = I and 8 12 = o. 37) These equations have a solution when the column vectors of the difference matrix Am - Ap and of the matrix Brn are linearly dependent on the column vectors of the matrix Bp.

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