Dynamic Modeling and Robust-adaptive Iterative Learning Control of 3-RRR Spherical Parallel Mechanism

Abstract

A dynamic model based on attitude parameters of moving platform for a 3-RRR spherical parallel robot is developed by using Lagrange method. Further,a dynamic model with parameter uncertainties is developed considering the parameters uncertainties caused by tiny changes such as approximately computing in linear density,thickness,and mechanism abrasion during working process. Aiming at its characteristic of acting repeatedly in vibration measuring and parameter uncertainties,a robust-adaptive iterative learning controller (ILC) is designed for this mechanism,and its stability is proved. The adaptive algorithm,which is powerful in leaning unknown constants,is used to compensate the uncertain parts of dynamics model,and the iterative learning algorithm is used to track the desired path without errors. Because of the utilization of certain information of dynamics model,the Lipschitz condition necessary for most unknown systems during iterative learning control is avoidable. The simulation results indicate that this robot can accurately track the desired path repeatedly.