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

Ulises Davalos-Guzman,Carlos E Castañeda,Lina Maria Aguilar-Lobo,Gilberto Ochoa-Ruiz

doi:10.3390/app11031154

Abstract

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.

Highlights

In many current medical applications, the utilization of manipulator robots plays a very important role in dedicated procedures that require high accuracy and high performance in real time
We propose a control scheme based on a decentralized recurrent highorder neural network (RHONN) in order to identify the dynamics of a 2DOF system in discrete time, coupled with the sliding-mode control (SMC) technique
The closed-loop simulation results using the field programmable gate array (FPGA) hardware-in-loop architecture are presented in FigTuhreec6lo. sed-loop simulation results using the FPGA hardware-in-loop architecture are presented in FigTuhree 6re. sults for this real-time implementation are limited to the robot’s physical parameters The aTphpelireedsutoltrsqufoerinthFiisgruerael-6tihmaes tihmepmleamxiemnutamtiovnalaureeclaimlcuitleadtedtofothrethriosbcootn’stroplhleyrsiacnadl pitairsaampeptleierds

Summary

Introduction

In many current medical applications, the utilization of manipulator robots plays a very important role in dedicated procedures that require high accuracy and high performance in real time. Medical robotic systems are typically mechanical manipulators with rigid links connected by joints that allow relative motion from one link to another [1]. The systems typically used for these procedures are robots with two degrees of freedom (2DOF). Robot manipulators are examples of nonlinear complex dynamics with strong interconnections, parameters and dynamics that are difficult to measure and to model [4]. One of the non-linearities can be present when the motor rotor is positioned anywhere in the dead zone for the equilibrium position. Other non-linearity can be an asynchronous generator introduced in the system due to actuator saturation [5]

Results

Discussion

Conclusion