Title

New Framework Of Learning Control For A Class Of Nonlinear Systems

Abstract

This paper illustrates a new nonlinear learning control design based on the Lyapunov's direct method. The design is applicable to the class of nonlinear systems consisting of finite cascaded subsystems in performing repeated tasks. A class of difference or difference-differential learning laws is proposed. It will be shown that, under a difference learning control, the class of nonlinear systems is guaranteed to be asymptotically stable with respect to the number of trials. For better rejection of measurement noise, difference-differential learning law can be applied to yield arbitrarily good accuracy. The proposed approach provides closed-form expressions of learning controls, and it gives the designer much flexibility in choosing various combinations of feedforward and learning control parts.

Publication Date

1-1-1995

Publication Title

Proceedings of the American Control Conference

Volume

5

Number of Pages

3024-3028

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

0029190754 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0029190754

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