Identificação de Sistemas Dinâmicos Não Lineares Utilizando Modelos Neuro-Fuzzy Lineares Locais com um Algoritmo LOLIMOT-PSO

Mapping Intimacies ◽

10.48011/asba.v2i1.1356 ◽

2020 ◽

Author(s):

Maria Emília Andrade Borges ◽

Tiago G. de Oliveira ◽

Luiz F. Pugliese ◽

Fadul F. Rodor

Keyword(s):

Monte Carlo ◽

Linear Model ◽

Model Trees ◽

Local Linear ◽

Neuro Fuzzy ◽

Nonlinear Autoregressive

Este trabalho propõe o uso do algoritmo de otimização baseado em enxame de partículas para a determinação dos pontos de divisão do subespaço de uma dada dimensão de entrada utilizando algoritmo de treinamento de modelos Neuro-Fuzzy conhecido como LOLIMOT (Local Linear Model Trees). A proposta foi avaliada em dois sistemas dinâmicos não lineares, sendo um modelo NARX (Nonlinear Autoregressive Exogenous) e um sistema de nível. Simulações de Monte Carlo foram efetuadas para analisar o efeito da inicialização aleatória do algoritmo PSO. Os resultados foram comparados com o algoritmo LOLIMOT convencional e em todos os casos foi possível observar uma melhora com relação a função de custo.

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Automatic Model Selection in Local Linear Model Trees (LOLIMOT) for Nonlinear System Identification of a Transport Delay Process

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)42927-2 ◽

1997 ◽

Vol 30 (11) ◽

pp. 699-704 ◽

Cited By ~ 8

Author(s):

Oliver Nelles ◽

Oliver Hecker ◽

Rolf Isermann

Keyword(s):

System Identification ◽

Nonlinear System ◽

Model Selection ◽

Linear Model ◽

Nonlinear System Identification ◽

Model Trees ◽

Transport Delay ◽

Automatic Model Selection ◽

Local Linear

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Optimized hybrid decoupled visual servoing with supervised learning

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/09596518211028379 ◽

2021 ◽

pp. 095965182110283

Author(s):

Alireza Rastegarpanah ◽

Ali Aflakian ◽

Rustam Stolkin

Keyword(s):

Linear Model ◽

Visual Servoing ◽

Three Dimensional ◽

Interaction Matrix ◽

Two Dimensional ◽

Model Tree ◽

Local Linear ◽

Neuro Fuzzy ◽

Fuzzy Neural ◽

Classical Image

This study proposes an optimized hybrid visual servoing approach to overcome the imperfections of classical two-dimensional, three-dimensional and hybrid visual servoing methods. These imperfections are mostly convergence issues, non-optimized trajectories, expensive calculations and singularities. The proposed method provides more efficient optimized trajectories with shorter camera path for the robot than image-based and classical hybrid visual servoing methods. Moreover, it is less likely to lose the object from the camera field of view, and it is more robust to camera calibration than the classical position-based and hybrid visual servoing methods. The drawbacks in two-dimensional visual servoing are mostly related to the camera retreat and rotational motions. To tackle these drawbacks, rotations and translations in Z-axis have been separately controlled from three-dimensional estimation of the visual features. The pseudo-inverse of the proposed interaction matrix is approximated by a neuro-fuzzy neural network called local linear model tree. Using local linear model tree, the controller avoids the singularities and ill-conditioning of the proposed interaction matrix and makes it robust to image noises and camera parameters. The proposed method has been compared with classical image-based, position-based and hybrid visual servoing methods, both in simulation and in the real world using a 7-degree-of-freedom arm robot.

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