Title

Asymptotic Learning Control For A Class Of Cascaded Nonlinear Uncertain Systems

Keywords

Learning control; Lyapunov design; Periodic function; Stability; Uncertain system

Abstract

In this note, the problem of learning unknown functions in a class of cascaded nonlinear systems will be studied. The functions to be learned are those functions that are imbedded in the system dynamics and are of known period of time. In addition to the unknown periodic time functions, nonlinear uncertainties bounded by known functions of the state are also admissible. The objective of the note is to find an iterative learning control under which the class of nonlinear systems are globally stabilized (in the sense of being uniform bounded), their outputs are asymptotically convergent, and a combination of the time functions contained in system dynamics are asymptotically learned. To this end, a new type of differential-difference learning law is utilized to generate the proposed learning control that yields both asymptotic stability of the system output and asymptotic convergence of the learning error. The design is carried out by applying the Lyapunov direct method and the backward recursive design method.

Publication Date

8-1-2002

Publication Title

IEEE Transactions on Automatic Control

Volume

47

Issue

8

Number of Pages

1369-1376

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TAC.2002.801194

Socpus ID

0036686453 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0036686453

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