scholarly journals Particle Swarm Based Approach of a Real-Time Discrete Neural Identifier for Linear Induction Motors

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Alma Y. Alanis ◽  
E. Rangel ◽  
J. Rivera ◽  
N. Arana-Daniel ◽  
C. Lopez-Franco
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Discrete Time ◽  
Induction Motors ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Linear Induction ◽  
Linear Induction Motor ◽  
High Order Neural Network ◽  
Novel Algorithm

This paper focusses on a discrete-time neural identifier applied to a linear induction motor (LIM) model, whose model is assumed to be unknown. This neural identifier is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high-order neural network (RHONN) trained with a novel algorithm based on extended Kalman filter (EKF) and particle swarm optimization (PSO), using an online series-parallel con…figuration. Real-time results are included in order to illustrate the applicability of the proposed scheme.

scholarly journals Design and Implementation of a Real Time Control System for a 2DOF Robot Based on Recurrent High Order Neural Network Using a Hardware in the Loop Architecture

2021 ◽  
Vol 11 (3) ◽  
pp. 1154
Author(s):  
Ulises Davalos-Guzman ◽  
Carlos E. Castañeda ◽  
Lina Maria Aguilar-Lobo ◽  
Gilberto Ochoa-Ruiz
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Discrete Time ◽  
Sliding Mode ◽  
External Perturbation ◽  
High Order ◽  
Real Time Control ◽  
Motion Error ◽  
Hardware In Loop ◽  
High Order Neural Network

In this paper, a real-time implementation of a sliding-mode control (SMC) in a hardware-in-loop architecture is presented for a robot with two degrees of freedom (2DOF). It is based on a discrete-time recurrent neural identification method, as well as the high performance obtained from the advantages of this architecture. The identification process uses a discrete-time recurrent high-order neural network (RHONN) trained with a modified extended Kalman filter (EKF) algorithm. This is a method for calculating the covariance matrices in the EKF algorithm, using a dynamic model with the associated and measurement noises, and it increases the performance of the proposed methodology. On the other hand, the decentralized discrete-time SMC technique is used to minimize the motion error. A Virtex 7 field programmable gate array (FPGA) is configured based on a hardware-in-loop real-time implementation to validate the proposed controller. A series of several experiments demonstrates the robustness of the algorithm, as well as its immunity to noise and the inherent robustness to external perturbation, as are typically found in the input reference signals of a 2DOF manipulator robot.

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