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Real-time torque control using discrete-time recurrent high-order neural networks

Castañeda, C. ; Loukianov, A. ; Sanchez, E. ; Castillo-Toledo, B.

Neural computing & applications, 2013-05, Vol.22 (6), p.1223-1232 [Periódico revisado por pares]

London: Springer-Verlag

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Título:
Real-time torque control using discrete-time recurrent high-order neural networks
Autor: Castañeda, C. ; Loukianov, A. ; Sanchez, E. ; Castillo-Toledo, B.
Assuntos: Applied sciences ; Artificial Intelligence ; Combinatorics ; Combinatorics. Ordered structures ; Computational Biology/Bioinformatics ; Computational Science and Engineering ; Computer Science ; Computer science; control theory; systems ; Data Mining and Knowledge Discovery ; Designs and configurations ; Distribution theory ; Exact sciences and technology ; Global analysis, analysis on manifolds ; Image Processing and Computer Vision ; Learning and adaptive systems ; Mathematics ; Original Article ; Probability and statistics ; Probability and Statistics in Computer Science ; Sciences and techniques of general use ; Statistics ; Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds
É parte de: Neural computing & applications, 2013-05, Vol.22 (6), p.1223-1232
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
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Descrição: This paper presents a discrete-time direct current (DC) motor torque tracking controller, based on a recurrent high-order neural network to identify the plant model. In order to train the neural identifier, the extended Kalman filter (EKF) based training algorithm is used. The neural identifier is in series-parallel configuration that constitutes a well approximation method of the real plant by the neural identifier. Using the neural identifier structure that is in the nonlinear controllable form, the block control (BC) combined with sliding modes (SM) control techniques in discrete-time are applied. The BC technique is used to design a nonlinear sliding manifold such that the resulting sliding mode dynamics are described by a desired linear system. For the SM control technique, the equivalent control law is used in order to the plant output tracks a reference signal. For reducing the effect of unknown terms, it is proposed a specific desired dynamics for the sliding variables. The control problem is solved by the indirect approach, where an appropriate neural network (NN) identification model is selected; the NN parameters (synaptic weights) are adjusted according to a specific adaptive law (EKF), such that the response of the NN identifier approximates the response of the real plant for the same input. Then, based on the designed NN identifier a stabilizing or reference tracking controller is proposed (BC combined with SM). The proposed neural identifier and control applicability are illustrated by torque trajectory tracking for a DC motor with separate winding excitation via real-time implementation.
Editor: London: Springer-Verlag
Idioma: Inglês

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Real-time torque control using discrete-time recurrent high-order neural networks

Castañeda, C. ; Loukianov, A. ; Sanchez, E. ; Castillo-Toledo, B.

London: Springer-Verlag

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