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Tracking control for network-based T-S fuzzy systems with asynchronous constraints

conference contribution
posted on 2017-12-06, 00:00 authored by Dawei Zhang, Qing-Long Han, Xin-Chun Jia
This paper considers H∞ tracking control for a class of network-based T-S fuzzy systems with available asynchronous constraints on membership functions. Using a fuzzy controller associated with available sampled-data of both premise variables and feedback states, the network-based control system is equivalent to an asynchronous T-S fuzzy system with an interval time-varying sawtooth delay. Notice that the routine relaxation methods in traditional T-S fuzzy systems can not be used for stability analysis and controller design of the asynchronous fuzzy system since common product terms of membership functions can not be grouped. Instead, a new relaxation method is proposed by using asynchronous constraints on fuzzy membership functions to introduce some free weighting matrices. By using the proposed relaxation method and a new discontinuous Lyapunov-Krasovskii functional, some criteria on H∞ tracking performance analysis and controller design are derived. Compared with the existing results, the derived criteria are of less conservatism and allow the existence of a network-based fuzzy controller with different gains, which can ensure a better H∞ tracking performance. The effectiveness of the proposed method is illustrated by an example.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Start Page

1

End Page

8

Number of Pages

8

Start Date

2012-01-01

Finish Date

2012-01-01

ISSN

1098-7584

ISBN-13

9781467315074

Location

Brisbane, Qld., Australia

Publisher

Institute of Electrical and Electronics Engineers Inc.

Place of Publication

Piscataway, NJ

Peer Reviewed

  • Yes

Open Access

  • No

External Author Affiliations

Centre for Intelligent and Networked Systems (CINS); Shanxi da xue; TBA Research Institute;

Era Eligible

  • Yes

Name of Conference

IEEE International Conference on Fuzzy Systems;IEEE World Congress on Computational Intelligence