Dynamic Modeling, Parameter Identification and Nonlinear Model Predictive Control of a Cable-Suspended Parallel Robot with Four Cables

In this research, a nonlinear model predictive controller is designed for the trajectory tracking of spatial cable-suspended parallel robots with four cables. The dynamic model of this robot is derived firstly using the Newton-Euler approach and is subsequently verified using the Simscape environment. Moreover, an identification method based on the Genetic Algorithm is presented to identify the unknown inertial and frictional parameters in the model. It is observed that the Interior-Point optimization method is able to solve the optimization problem resulting from the proposed model predictive controller in real-time. Also, the effectiveness of the designed controller, combined with the presented identification approach, is investigated by assessing the controller performance when the estimated values are used in the model, compared to the cases where the precise values are utilized. Three groups of various inertial and frictional parameter values are considered in these assessments, and it is observed that when the estimated values are utilized in the model, the control of the robot is performed with an average RMSE of 1.1073mm, which is only 0.75716mm more than the average RMSE when the precise values are utilized. The results obtained from the simulations reveal the promising performance of the proposed model predictive controller when combined with the suggested identification method.