Detalhes bibliográficos
| Resumo: | This study investigates the problem of system identification and control for non-affine nonlinear deterministic systems using a generalised state-space neuro-fuzzy model. The proposed new model consists of a seven-layer neural network, where the consequent part comprises a finite set of discrete-time invariant linear state-space models. For tracking control design, quadratic stabilisers are integrated within a Parallel Distributed Compensation framework. Instead of merely ensuring the closed-loop stability of the state-augmented system, the feedback matrices are computed by solving a region-constrained Linear Matrix Inequality problem, which guarantees that the closed-loop eigenvalues remain within a D-stable region. The proposed generalised neuro-fuzzy model is proven to be a universal approximator on compact sets, with sufficient conditions for closed-loop stability established under the Neuro-Fuzzy-based Parallel Distributed Compensation framework. Experimental results on a nonlinear benchmark system validate the effectiveness and practical feasibility of the proposed neuro-fuzzy tracking control strategy. |
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| Autores principais: | Gil, Paulo |
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| Outros Autores: | João, Miguel; Carvalho, Carolina; Palma, Luís Brito; Henriques, Jorge |
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| Assunto: | Linear Matrix Inequality-region Neuro-fuzzy control Nonlinear systems Takagi–Sugeno inference D-stability Control and Systems Engineering Artificial Intelligence Electrical and Electronic Engineering |
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| Ano: | 2025 |
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| País: | Portugal |
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| Tipo de documento: | artigo |
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| Tipo de acesso: | acesso aberto |
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| Instituição associada: | Universidade Nova de Lisboa |
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| Idioma: | inglês |
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| Origem: | Repositório Institucional da UNL |
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