Model reference controller approach for robot arm tracking using neural networks

Najva N,Abdul Saleem

doi:10.17485/ijst/2019/v12i39/148004

Abstract

Objectives: This study proposes a neural network (NN)-based model reference controller (MRC) for robot arm trajectory tracking. Methods/statistical analysis: The proposed methodology uses two NNs: a reference model and a controller. The NN-based reference model is initially trained such that it follows any desired reference trajectory. The position of the robot arm is controlled by changing the joint angles, which is achieved by applying the desired torque. The NN controller provides the desired torque, and the controller is trained until the error between the outputs of the actual plant and the reference model is driven to a value which is approximately zero. The trained NN controller is employed for the actual trajectory tracking. Findings: Our NN-based reference model is capable of approximating the nonlinear model of the robot arm motion, and it is expected to minimise the effect of model uncertainties. Simulations are done to validate the proposed method, which found that the NN-based MRC is capable of following the desired trajectories with approximately zero tracking error. Application/improvements: The proposed controller effectively tracks any desired trajectory with least tracking error and minimum control input. Simulation results illustrate that the total control effort and maximum control input required to track the desired trajectory are very less compared to that required for a PID controller.Keywords: Model Reference Controller, Robot Arm, Tracking, Neural Networks, Tracking Error, Control Input

Highlights

Robot manipulators are electromechanical devices which are capable of performing a variety of functions in a more flexible working environment and at a lower production cost
This study proposes a model reference neural network (NN)-based trajectory controller which gives the best performance in terms of tracking error, control input, total control effort and maximum torque compared to conventional PID controllers
To illustrate the effectiveness of the proposed method, the results are compared with that obtained for a PID controller-based robot arm trajectory tracking

Summary

Introduction

Robot manipulators are electromechanical devices which are capable of performing a variety of functions in a more flexible working environment and at a lower production cost. The various applications include industrial, educational and medical fields apart from their applications in farm, home, hospital and military. The robots can work in unpredictable and hazardous situations where a human might find very difficult to reach. One of the objectives of the robot is its arm movement from an initial position to a target position. Planning robot arm trajectory that satisfies obstacle avoidance and developing an appropriate method to control trajectory tracking are important research problems in robotics. An effective trajectory tracking controller should be able to track the desired trajectory with minimum tracking error, lesser control input and minimum total control effort

Objectives

Methods

Results

Conclusion