Nonlinear system control based on Neural Networks with Adaptive Predictive strategy

Larrea, Mikel; Irigoyen, Eloy; Gómez, Vicente Simón; Artaza, Fernando

doi:10.1109/etfa.2010.5641348

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…This is suitable when problems are studied and solved in a local domain. However, there is a trend to approach bigger problems with a more abstracted and global perspective, leading to the use of nonlinear methods [9][10][11][12]. At some point of complexity, and certainly if the system involves unknown parts and other uncertainties, an entirely formal analysis of the system becomes unsustainable [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

About Extracting Dynamic Information of Unknown Complex Systems by Neural Networks

et al. 2018

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This work presents a straightforward methodology based on neural networks (NN) which allows to obtain relevant dynamic information of unknown nonlinear systems. It provides an approach for cases in which the complex task of analyzing the dynamic behaviour of nonlinear systems makes it excessively challenging to obtain an accurate mathematical model. After reviewing the suitability of multilayer perceptrons (MLPs) as universal approximators to replace a mathematical model, the first part of this work presents a system representation using a model formulated with state variables which can be exported to a NN structure. Considering the linearization of the NN model in a mesh of operating points, the second part of this work presents the study of equilibrium states in such points by calculating the Jacobian matrix of the system through the NN model. The results analyzed in three case studies provide representative examples of the strengths of the proposed method. Conclusively, it is feasible to study the system behaviour based on MLPs, which enables the analysis of the local stability of the equilibrium points, as well as the system dynamics in its environment, therefore obtaining valuable information of the system dynamic behaviour.

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