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Adaptive Control Tutorial (Advances in Design and Control) by Petros Ioannou, Barýp Fidan

By Petros Ioannou, Barýp Fidan

Designed to fulfill the wishes of a large viewers with no sacrificing mathematical intensity and rigor, Adaptive regulate instructional provides the layout, research, and alertness of a wide selection of algorithms that may be used to regulate dynamical platforms with unknown parameters. Its tutorial-style presentation of the elemental recommendations and algorithms in adaptive keep watch over make it compatible as a textbook.

Adaptive keep an eye on educational is designed to serve the desires of 3 unique teams of readers: engineers and scholars drawn to studying how one can layout, simulate, and enforce parameter estimators and adaptive keep watch over schemes with no need to completely comprehend the analytical and technical proofs; graduate scholars who, as well as reaching the aforementioned ambitions, additionally are looking to comprehend the research of easy schemes and get an idea of the stairs concerned with extra complicated proofs; and complex scholars and researchers who are looking to learn and comprehend the main points of lengthy and technical proofs with a watch towards pursuing study in adaptive regulate or comparable issues.

The authors in attaining those a number of ambitions via enriching the publication with examples demonstrating the layout systems and simple research steps and through detailing their proofs in either an appendix and electronically on hand supplementary fabric; on-line examples also are to be had. an answer handbook for teachers will be got by way of contacting SIAM or the authors.

This booklet should be beneficial to masters- and Ph.D.-level scholars in addition to electric, mechanical, and aerospace engineers and utilized mathematicians.

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

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Adaptive Control Tutorial (Advances in Design and Control)

Designed to satisfy the wishes of a large viewers with no sacrificing mathematical intensity and rigor, Adaptive keep an eye on instructional provides the layout, research, and alertness of a wide selection of algorithms that may be used to control dynamical structures with unknown parameters. Its tutorial-style presentation of the basic suggestions and algorithms in adaptive keep watch over make it appropriate as a textbook.

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Parameter Identification: Continuous Time Recursive LS Algorithm with Forgetting Factor Consider the function where 2o = <2o > ^, )S > 0 are design constants and 9o = #(0) is the initial parameter estimate. 40) to include possible discounting of past data and a penalty on the initial error between the estimate OQ and 0*. Since ^-, — € £00, J(0) is a convex function of 0 over 7Z" at each time t. 38) is therefore obtained by solving for 9(t), which yields the nonrecursive LS algorithm where is the so-called covariance matrix.

1, where k is the spring constant, / is the viscous-friction or damping coefficient, M is the mass of the system, u is the forcing input, and x is the displacement of the mass M. , the force acting on the spring is proportional to the displacement, and the friction force is proportional to the velocity x, using Newton's law we obtain the differential equation that describes the dynamics of the system as 16 Chapter 2. 1. Mass-spring-dashpot system. Let us assume that M, /, k are the constant unknown parameters that we want to estimate online.

0(0 at any given time t. The cost J(9) penalizes all the past errors from T = 0 to t that are due to 0(0 / 9*. Since J(9) is a convex function over K at each time t, its minimum satisfies which gives the LS estimate provided of course that the inverse exists. The LS method considers all past data in an effort to provide a good estimate for 9* in the presence of noise dn. , 0(0 converges to the exact parameter value despite the presence of the noise disturbance dn. 38). 6, the estimate z of z and the normalized estimation are generated as where 0(0 is the estimate of 0* at time t, and m] = 1 + n2s is designed to guarantee ^- € £00- Below we present different versions of the LS algorithm, which correspond to different choices of the LS cost J (0).

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