STATE STEERING BY LEARNING FOR A CLASS OF NONLINEAR CONTROL-SYSTEMS

被引：10

作者：

LUCIBELLO, P

机构：

[1] Dipartimento di Informatica e Sistemistica, Università di Roma La Sapienza, 00186 Roma

来源：

AUTOMATICA | 1994年 / 30卷 / 09期

关键词：

ITERATIVE METHODS; LEARNING SYSTEMS; NONLINEAR CONTROL SYSTEMS; NONLINEAR SYSTEMS;

D O I：

10.1016/0005-1098(94)90012-4

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The problem of steering the state of nonlinearly perturbed linear systems by learning is investigated. A family of algorithms which compute the steering control by means of successive trials on the real plant is presented. Convergence in the face of a class of nonlinear plant perturbations is proven. State feedback linearizable systems are shown to be addressable by the presented algorithms. Two examples illustrate the applicability of algorithms.

引用

页码：1463 / 1468

页数：6

共 17 条

[1] BETTERING OPERATION OF ROBOTS BY LEARNING [J].

ARIMOTO, S ;

KAWAMURA, S ;

MIYAZAKI, F .

JOURNAL OF ROBOTIC SYSTEMS, 1984, 1 (02) :123-140

[2]

ARIMOTO S, 1985, 24TH P IEEE C DEC CO, P1375

[3]

Bryson A.E, 1975, APPL OPTIMAL CONTROL

[4]

CASALINO G, 1984, P IASTED S ROBOTICS

[5]

CRAIG JJ, 1984, 1984 P AM CONTR C, P1566

[6]

Hahn W., 1967, STABILITY MOTION

[7]

HAUSER J, 1987, 26TH IEEE C DEC CONT, P859

[8]

Isidori A., 1989, NONLINEAR CONTROL SY, Vsecond

[9]

Lucibello P., 1992, Proceedings of the 1992 American Control Conference (IEEE Cat. No.92CH3072-6), P2011

[10]

LUCIBELLO P, 1992, 31ST P IEEE C DECISI, P2531

← 1 2 →