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

• Published in: Applied sciences Vol. 11; no. 3; p. 1154
• Main Authors: Davalos-Guzman, Ulises, Castañeda, Carlos E., Aguilar-Lobo, Lina Maria, Ochoa-Ruiz, Gilberto
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2021
• Subjects: 2DOF robot manipulator; Accuracy; Approximation; Control algorithms; Controllers; covariance matrices; Design; Field programmable gate arrays; Identification; Kalman filter; Neural networks; Real time; real-time implementation; recurrent high order neural network; Robotics; Robots; Simulation; sliding mode control; Systems design
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11031154

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.