Neural identification and control of a linear induction motor using an α - β model

Benítez, Victor H.; Loukianov, Alexander G.; Sánchez, Edgar N.

doi:10.1109/acc.2003.1240468

Cited by 16 publications

(13 citation statements)

References 9 publications

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“…Then the learning law is determined as ijk ij ijk ijk e w (6) where k {1,2}, each ijk is positive real constant. For each weight, we obtain:…”

Section: Neural Network Learning Lawmentioning

confidence: 99%

“…In this approach, the whole system is seen as the interconnection of multiple local sub-systems; the control synthesis is done for each local subsystem, considering only the local variables [4], [5]. This approach was created in order to solve the control of largescale systems; for these systems, a centralized controller exchanges a large quantity of information with the local subsystems, easily overloading the computation capabilities existing today [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

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Decentralized neural identification and control for robotics manipulators

Sánchez

Gaytan

Saad

2006

2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006

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This paper presents a decentralized control scheme, based on a recurrent neural identifier with a block control structure, and its application to robotics manipulators. A local joint controller is proposed for each joint, using only local angular position and velocity measurements. These very simple local joint controllers allow trajectory tracking, with reduced computations. The applicability of the proposed scheme is illustrated, via simulations, first by the applications to a two degree of freedom robotic manipulator and then to a seven degree of freedom one.

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“…Then the learning law is determined as ijk ij ijk ijk e w (6) where k {1,2}, each ijk is positive real constant. For each weight, we obtain:…”

Section: Neural Network Learning Lawmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decentralized neural identification and control for robotics manipulators

Sánchez

Gaytan

Saad

2006

2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006

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“…An interesting review for the development of decentralized nonlinear adaptive control theory for large-scale dynamical systems is given in Jiang (1999). The present paper introduces a new approach for decentralized control theory, introducing decentralized recurrent highorder neural networks (RHONN) structures based on Kosmatopoulus et al (1997a), which are able to identify the dynamical behavior of subsystems with only local information and can deal with uncertainties in the absence of matching conditions, as have been discussed in Benitez et al (2003). The variable structure control theory (VSC) is used to obtain a robust control law which guarantees tracking and rejects disturbances.…”

Section: Introductionmentioning

confidence: 98%

Decentralized adaptive recurrent neural control structure

Benítez

Sánchez

Loukianov

2007

Engineering Applications of Artificial Intelligence

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“…In [24], V. H. Benítez et al present a method to control an LIM using dynamic neural networks. They propose a neural identifier of triangle form and design a reduced-order observer in order to estimate the secondary fluxes.…”

Section: Introductionmentioning

confidence: 99%

Discrete and Continuous Model of Three-Phase Linear Induction Motors “LIMs” Considering Attraction Force

2019

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A fifth-order dynamic continuous model of a linear induction motor (LIM), without considering “end effects” and considering attraction force, was developed. The attraction force is necessary in considering the dynamic analysis of the mechanically loaded linear induction motor. To obtain the circuit parameters of the LIM, a physical system was implemented in the laboratory with a Rapid Prototype System. The model was created by modifying the traditional three-phase model of a Y-connected rotary induction motor in a d–q stationary reference frame. The discrete-time LIM model was obtained through the continuous time model solution for its application in simulations or computational solutions in order to analyze nonlinear behaviors and for use in discrete time control systems. To obtain the solution, the continuous time model was divided into a current-fed linear induction motor third-order model, where the current inputs were considered as pseudo-inputs, and a second-order subsystem that only models the currents of the primary with voltages as inputs. For the discrete time model, the current-fed model is discretized by solving a set of differential equations, and the subsystem is discretized by a first-order Taylor series. Finally, a comparison of the continuous and discrete time model behaviors was shown graphically in order to validate the discrete time model.

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Neural identification and control of a linear induction motor using an α - β model

Cited by 16 publications

References 9 publications

Decentralized neural identification and control for robotics manipulators

Decentralized neural identification and control for robotics manipulators

Decentralized adaptive recurrent neural control structure

Discrete and Continuous Model of Three-Phase Linear Induction Motors “LIMs” Considering Attraction Force

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