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Sparse and Decoupling Control Strategies based on Takagi-Sugeno Fuzzy Models
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-0334-2554
2019 (English)In: IEEE transactions on systems, man and cybernetics. Part B. Cybernetics, ISSN 1083-4419, E-ISSN 1941-0492Article in journal (Refereed) Epub ahead of print
Abstract [en]

In order to better handle the coupling effects when controlling multiple-input multiple-output (MIMO) systems, taking the decentralized control structure as the basis, this paper proposes a sparse control strategy and a decoupling control strategy. Type-1 and type-2 Takagi-Sugeno (T-S) fuzzy models are used to describe the MIMO system, and the relative normalized gain array (RNGA) based criterion is employed to measure the coupling effects. The main contributions include: i). compared to the previous studies, a manner with less computational cost to build fuzzy models for the MIMO systems is provided, and a more accurate method to construct the so-called effective T-S fuzzy model (ETSM) to express the coupling effects is developed; ii). for the sparse control strategy, four indexes are defined in order to extend a decentralized control structure to a sparse one. Afterwards, an ETSM-based method is presented that a sparse control system can be realized by designing multiple independent single-input single-output (SISO) control-loops; iii). for the decoupling control strategy, a novel and simple ETSM-based decoupling compensator is developed that can effectively compensate for both steady and dynamic coupling effects. As a result, the MIMO controller design can be transformed to multiple non-interacting SISO controller designs. Both of the sparse and decoupling strategies allow to use linear SISO control algorithms to regulate a closely coupled nonlinear MIMO system without knowing its exact mathematical functions. Two examples are used to show the effectiveness of the proposed strategies

Place, publisher, year, edition, pages
IEEE, 2019.
Keywords [en]
Effective fuzzy model, T-S fuzzy model, sparse control, decoupling control, type-2 fuzzy logic
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:oru:diva-71904DOI: 10.1109/TCYB.2019.2896530OAI: oai:DiVA.org:oru-71904DiVA, id: diva2:1283352
Available from: 2019-01-29 Created: 2019-01-29 Last updated: 2020-03-10Bibliographically approved

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Liao, QianfangSun, Da

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