Trajectory Tracking of a Data-Based Model of a Two-Link Robotic Manipulator Using Model Predictive Controller

Abstract

To achieve accurate position tracking, there is a need to develop high-fidelity robot arm models that are compliant and affordable. However, physics-based models are constrained by their stiffness and complexity. Therefore, reduced-order modelling developed from data through sub-space system identification is proposed as a solution to this problem. A high-fidelity simulation model of a two-link robot arm developed in MATLAB and Simulink was used to generate synthetic data, and the data acquired was used for the estimation and validation of first- and second-order linear state-space models. Due to its effective tracking characteristics, the model predictive control technique was used for trajectory tracking. The results of the simulations demonstrate that the first-order and second-order models can track the intended set points accurately but at the cost of larger joint torques required to counteract gravity. The results show that low-order and data-compliant models can follow trajectories with high precision. MATLAB 2020a was used for all simulations.