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Robustness and convergence rate of a discrete-time learning control algorithm for a class of nonlinear systems

✍ Scribed by Samer S. Saab

Publisher
John Wiley and Sons
Year
1999
Tongue
English
Weight
127 KB
Volume
9
Category
Article
ISSN
1049-8923

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✦ Synopsis

In this paper, we apply a discrete-time learning algorithm to a class of discrete-time varying nonlinear systems with a$ne input action and linear output having relative degree one. We investigate the robustness of the algorithm to state disturbance, measurement noise and reinitialization errors. We show that the input and the state variables are always bounded if certain conditions are met. Moreover, we shown that the input error and state error converge uniformly to zero in absence of all disturbances. In addition, we show that, after a "nite number of iterations, the convergence rate is exponential in l. A numerical example is added to illustrate the results.

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