Neural network-based adaptive robust control for a class of uncertain systems with measurement noise

Jinyong Yang; Yingmin Jia

Neural network-based adaptive robust control for a class of uncertain systems with measurement noise

Jinyong Yang^*
, Yingmin Jia

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

Beihang University

Research output: Contribution to conference › Paper › peer-review

2 Scopus citations

Abstract

In this paper, neural networks (NNs) and adaptive robust control (ARC) design philosophy are integrated to design performance-oriented control laws for a class of uncertain systems whose output is corrupted by external disturbances. The derived adaptive-robust control schemes not only guarantee all the signals are bounded in the closed loop but also make the system preserve certain prescribed properties. The cone-bounded assumption on the uncertain dynamics is removed via neural networks. The feedback information is the state with measurement noise.

Original language	English
Pages	1475-1478
Number of pages	4
State	Published - 2002
Event	2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering - Beijing, China Duration: 28 Oct 2002 → 31 Oct 2002

Conference

Conference	2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering
Country/Territory	China
City	Beijing
Period	28/10/02 → 31/10/02

Keywords

Measurment noise
Neural networks
Robust adaptive control
Uncertain systems

Cite this

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title = "Neural network-based adaptive robust control for a class of uncertain systems with measurement noise",

abstract = "In this paper, neural networks (NNs) and adaptive robust control (ARC) design philosophy are integrated to design performance-oriented control laws for a class of uncertain systems whose output is corrupted by external disturbances. The derived adaptive-robust control schemes not only guarantee all the signals are bounded in the closed loop but also make the system preserve certain prescribed properties. The cone-bounded assumption on the uncertain dynamics is removed via neural networks. The feedback information is the state with measurement noise.",

keywords = "Measurment noise, Neural networks, Robust adaptive control, Uncertain systems",

author = "Jinyong Yang and Yingmin Jia",

year = "2002",

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note = "2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering ; Conference date: 28-10-2002 Through 31-10-2002",

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AU - Yang, Jinyong

AU - Jia, Yingmin

PY - 2002

Y1 - 2002

N2 - In this paper, neural networks (NNs) and adaptive robust control (ARC) design philosophy are integrated to design performance-oriented control laws for a class of uncertain systems whose output is corrupted by external disturbances. The derived adaptive-robust control schemes not only guarantee all the signals are bounded in the closed loop but also make the system preserve certain prescribed properties. The cone-bounded assumption on the uncertain dynamics is removed via neural networks. The feedback information is the state with measurement noise.

AB - In this paper, neural networks (NNs) and adaptive robust control (ARC) design philosophy are integrated to design performance-oriented control laws for a class of uncertain systems whose output is corrupted by external disturbances. The derived adaptive-robust control schemes not only guarantee all the signals are bounded in the closed loop but also make the system preserve certain prescribed properties. The cone-bounded assumption on the uncertain dynamics is removed via neural networks. The feedback information is the state with measurement noise.

KW - Measurment noise

KW - Neural networks

KW - Robust adaptive control

KW - Uncertain systems

UR - https://www.scopus.com/pages/publications/0037661137

M3 - 论文

AN - SCOPUS:0037661137

SP - 1475

EP - 1478

T2 - 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering

Y2 - 28 October 2002 through 31 October 2002

ER -

Neural network-based adaptive robust control for a class of uncertain systems with measurement noise

Abstract

Conference

Keywords

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