Model reference controller approach for robot arm tracking using neural networks

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